sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_202206.xlsx")

1 Introduction

This is mostly just a run of this worksheet to reacquaint myself with it.

This document is intended to provide a general overview of the TMRC2 samples which have thus far been sequenced. In some cases, this includes only those samples starting in 2019; in other instances I am including our previous (2015-2016) samples.

In all cases the processing performed was:

  1. Default trimming was performed.
  2. Hisat2 was used to map the remaining reads against the Leishmania panamensis genome revision 36.
  3. The alignments from hisat2 were used to count reads/gene against the revision 36 annotations with htseq.
  4. These alignments were also passed to the pileup functionality of samtools and the vcf/bcf utilities in order to make a matrix of all observed differences between each sample with respect to the reference.

The analyses in this document use the matrices of counts/gene from #3 and variants/position from #4 in order to provide some images and metrics describing the samples we have sequenced so far.

2 Annotations

Everything which follows depends on the Existing TriTrypDB annotations revision 46, circa 2019. The following block loads a database of these annotations and turns it into a matrix where the rows are genes and columns are all the annotation types provided by TriTrypDB.

The same database was used to create a matrix of orthologous genes between L.panamensis and all of the other species in the TriTrypDB.

tt <- sm(library(EuPathDB))
orgdb <- "org.Lpanamensis.MHOMCOL81L13.v46.eg.db"
tt <- sm(library(orgdb, character.only=TRUE))
pan_db <- org.Lpanamensis.MHOMCOL81L13.v46.eg.db
all_fields <- columns(pan_db)

all_lp_annot <- sm(load_orgdb_annotations(
    pan_db,
    keytype = "gid",
    fields = c("annot_gene_entrez_id", "annot_gene_name",
               "annot_strand", "annot_chromosome", "annot_cds_length",
               "annot_gene_product")))$genes

lp_go <- sm(load_orgdb_go(pan_db))
lp_lengths <- all_lp_annot[, c("gid", "annot_cds_length")]
colnames(lp_lengths)  <- c("ID", "length")
all_lp_annot[["annot_gene_product"]] <- tolower(all_lp_annot[["annot_gene_product"]])
orthos <- sm(EuPathDB::extract_eupath_orthologs(db = pan_db))

3 Load a genome

meta <- sm(EuPathDB::download_eupath_metadata(webservice="tritrypdb"))
lp_entry <- EuPathDB::get_eupath_entry(species="Leishmania panamensis", metadata=meta)
## Found the following hits: Leishmania panamensis MHOM/COL/81/L13, Leishmania panamensis strain MHOM/PA/94/PSC-1, choosing the first.
## Using: Leishmania panamensis MHOM/COL/81/L13.
colnames(lp_entry)
##  [1] "AnnotationVersion"  "AnnotationSource"   "BiocVersion"       
##  [4] "DataProvider"       "Genome"             "GenomeSource"      
##  [7] "GenomeVersion"      "NumArrayGene"       "NumChipChipGene"   
## [10] "NumChromosome"      "NumCodingGene"      "NumCommunity"      
## [13] "NumContig"          "NumEC"              "NumEST"            
## [16] "NumGene"            "NumGO"              "NumOrtholog"       
## [19] "NumOtherGene"       "NumPopSet"          "NumProteomics"     
## [22] "NumPseudogene"      "NumRNASeq"          "NumRTPCR"          
## [25] "NumSNP"             "NumTFBS"            "Organellar"        
## [28] "ReferenceStrain"    "MegaBP"             "PrimaryKey"        
## [31] "ProjectID"          "RecordClassName"    "SourceID"          
## [34] "SourceVersion"      "TaxonomyID"         "TaxonomyName"      
## [37] "URLGenome"          "URLGFF"             "URLProtein"        
## [40] "Coordinate_1_based" "Maintainer"         "SourceUrl"         
## [43] "Tags"               "BsgenomePkg"        "GrangesPkg"        
## [46] "OrganismdbiPkg"     "OrgdbPkg"           "TxdbPkg"           
## [49] "Taxon"              "Genus"              "Species"           
## [52] "Strain"             "BsgenomeFile"       "GrangesFile"       
## [55] "OrganismdbiFile"    "OrgdbFile"          "TxdbFile"          
## [58] "GenusSpecies"       "TaxonUnmodified"    "TaxonCanonical"    
## [61] "TaxonXref"
testing_panamensis <- "BSGenome.Leishmania.panamensis.MHOMCOL81L13.v53"
## testing_panamensis <- EuPathDB::make_eupath_bsgenome(entry=lp_entry, eu_version="v46")
library(as.character(testing_panamensis), character.only=TRUE)
## Loading required package: BSgenome
## Loading required package: Biostrings
## Loading required package: XVector
## 
## Attaching package: 'Biostrings'
## The following object is masked from 'package:base':
## 
##     strsplit
## Loading required package: rtracklayer
genome <- get0(as.character(testing_panamensis))

4 TODO:

Resequence samples: TMRC20002, TMRC20006, TMRC20004 (maybe TMRC20008 and TMRC20029)

5 Generate Expressionsets and Sample Estimation

The process of sample estimation takes two primary inputs:

  1. The sample sheet, which contains all the metadata we currently have on hand, including filenames for the outputs of #3 and #4 above.
  2. The gene annotations.

An expressionset is a data structure used in R to examine RNASeq data. It is comprised of annotations, metadata, and expression data. In the case of our processing pipeline, the location of the expression data is provided by the filenames in the metadata.

The first lines of the following block create the Expressionset. All of the following lines perform various normalizations and generate plots from it.

5.1 Notes

The following samples are much lower coverage:

  • TMRC20002
  • TMRC20006
  • TMRC20007
  • TMRC20008

20210610: I made some manual changes to the sample sheet which I downloaded, filling in some zymodeme with ‘unknown’

5.2 TODO:

  1. Do the multi-gene family removal right here instead of way down at the bottom
  2. Add zymodeme snps to the annotation later.
  3. Start phylogenetic analysis of variant table.
clinical_colors <- list(
##     "z1.0" = "#333333", ## Changed this to 'braz' to make it easier to find them.
    "z2.0" = "#555555",
    "z3.0" = "#777777",
    "z2.1" = "#874400",
    "z2.2" = "#0000cc",
    "z2.3" = "#cc0000",
    "z2.4" = "#df7000",
    "braz" = "#cc00cc",
    "unknown" = "#cbcbcb",
    "null" = "#000000")
sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical")
lp_expt <- create_expt(sample_sheet,
                       gene_info = all_lp_annot,
                       annotation_name = orgdb,
                       id_column = "hpglidentifier",
                       file_column = "lpanamensisv36hisatfile") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  subset_expt(nonzero = 8550) %>%
  subset_expt(coverage = 5000000) %>%
  set_expt_colors(clinical_colors) %>%
  semantic_expt_filter(semantic = c("amastin", "gp63", "leishmanolysin"),
                       semantic_column = "annot_gene_product") %>%
  sanitize_expt_metadata(columns = sanitize_columns) %>%
  set_expt_factors(columns = sanitize_columns, class = "factor")
## Reading the sample metadata.
## Dropped 11 rows from the sample metadata because the sample ID is blank.
## Did not find the condition column in the sample sheet.
## Filling it in as undefined.
## Did not find the batch column in the sample sheet.
## Filling it in as undefined.
## The sample definitions comprises: 110 rows(samples) and 66 columns(metadata fields).
## Warning in create_expt(sample_sheet, gene_info = all_lp_annot, annotation_name
## = orgdb, : Some samples were removed when cross referencing the samples against
## the count data.
## Matched 8778 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 8778 features and 105 samples.
## The samples (and read coverage) removed when filtering 8550 non-zero genes are:
## TMRC20002 TMRC20006 
##  11681227   6670348
## subset_expt(): There were 105, now there are 103 samples.
## The samples removed (and read coverage) when filtering samples with less than 5e+06 reads are:
## TMRC20004 TMRC20029 
##    564812   1658096
## subset_expt(): There were 103, now there are 101 samples.
## semantic_expt_filter(): Removed 68 genes.
libsizes <- plot_libsize(lp_expt)
dev <- pp("images/lp_expt_libsizes.png", width = 14, height = 9)
libsizes$plot
closed <- dev.off()
libsizes$plot

## I think samples 7,10 should be removed at minimum, probably also 9,11
nonzero <- plot_nonzero(lp_expt)
dev <- pp(file = "images/lp_nonzero.png", width=9, height=9)
nonzero$plot
## Warning: ggrepel: 81 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
closed <- dev.off()

lp_box <- plot_boxplot(lp_expt)
## 8122 entries are 0.  We are on a log scale, adding 1 to the data.
dev <- pp(file = "images/lp_expt_boxplot.png", width = 12, height = 9)
lp_box
closed <- dev.off()
lp_box

filter_plot <- plot_libsize_prepost(lp_expt)
filter_plot$lowgene_plot
## Warning: Using alpha for a discrete variable is not advised.

filter_plot$count_plot

table(pData(lp_expt)[["zymodemecategorical"]])
## 
##          braz notapplicable       unknown           z20           z21 
##             2             2             3             1             7 
##           z22           z23           z24           z30 
##            43            40             2             1
table(pData(lp_expt)[["clinicalresponse"]])
## 
##                                  cure                               failure 
##                                    38                                    38 
##                       laboratory line laboratory line miltefosine resistant 
##                                     1                                     1 
##                                    nd                      reference strain 
##                                    19                                     4

5.3 Distribution Visualization

Najib’s favorite plots are of course the PCA/TNSE. These are nice to look at in order to get a sense of the relationships between samples. They also provide a good opportunity to see what happens when one applies different normalizations, surrogate analyses, filters, etc. In addition, one may set different experimental factors as the primary ‘condition’ (usually the color of plots) and surrogate ‘batches’.

5.4 By Susceptilibity

Column ‘Q’ in the sample sheet, make a categorical version of it with these parameters:

  • 0 <= x <= 35 is resistant
  • 36 <= x <= 48 is ambiguous
  • 49 <= x is sensitive
fix_excel_percent <- function(numbers) {
  for (n in 1:length(numbers)) {
    pct <- grepl(x=numbers[n], pattern="\\%")
    new_number <- NA
    if (pct) {
      new_number <- as.numeric(gsub(x=numbers[n], pattern="\\%", replacement="")) / 100.0
    } else {
      new_number <- as.numeric(numbers[n])
    }
    numbers[n] <- new_number
  }
  return(as.numeric(numbers))
}

starting <- fix_excel_percent(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])
## Warning in fix_excel_percent(pData(lp_expt)
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sus_categorical <- starting
na_idx <- is.na(starting)
sum(na_idx)
## [1] 51
sus_categorical[na_idx] <- "unknown"

resist_idx <- starting <= 0.35
sus_categorical[resist_idx] <- "resistant"
indeterminant_idx <- starting >= 0.36 & starting <= 0.48
sus_categorical[indeterminant_idx] <- "ambiguous"
susceptible_idx <- starting >= 0.49
sus_categorical[susceptible_idx] <- "sensitive"

sus_categorical <- as.factor(sus_categorical)
pData(lp_expt)[["sus_category_historical"]] <- sus_categorical
table(sus_categorical)
## sus_categorical
## ambiguous resistant sensitive   unknown 
##         5        12        33        51
starting_current <- fix_excel_percent(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvcurrentdata"]])
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## [["susceptibilityinfectionreduction32ugmlsbvcurrentdata"]]): NAs introduced by
## coercion

## Warning in fix_excel_percent(pData(lp_expt)
## [["susceptibilityinfectionreduction32ugmlsbvcurrentdata"]]): NAs introduced by
## coercion
sus_categorical_current <- starting_current
na_idx <- is.na(starting_current)
sum(na_idx)
## [1] 54
sus_categorical_current[na_idx] <- "unknown"

resist_idx <- starting_current <= 0.35
sus_categorical_current[resist_idx] <- "resistant"
indeterminant_idx <- starting_current >= 0.36 & starting_current <= 0.48
sus_categorical_current[indeterminant_idx] <- "ambiguous"
susceptible_idx <- starting_current >= 0.49
sus_categorical_current[susceptible_idx] <- "sensitive"
sus_categorical_current <- as.factor(sus_categorical_current)

pData(lp_expt)[["sus_category_current"]] <- sus_categorical_current
table(sus_categorical_current)
## sus_categorical_current
## ambiguous resistant sensitive   unknown 
##         9         6        32        54
clinical_samples <- lp_expt %>%
  set_expt_batches(fact = sus_categorical_current) %>%
  set_expt_colors(clinical_colors)
table(pData(clinical_samples)[["condition"]])
## 
##    braz    null unknown    z2.0    z2.1    z2.2    z2.3    z2.4    z3.0 
##       2       2       3       1       7      43      40       2       1
clinical_norm <- normalize_expt(clinical_samples, norm = "quant", transform = "log2",
                                   convert = "cpm", filter = TRUE)
## Removing 134 low-count genes (8576 remaining).
## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.
zymo_pca <- plot_pca(clinical_norm, plot_title = "PCA of parasite expression values",
                     plot_labels = FALSE)
ggplt(zymo_pca$plot)
## [1] "ggplot.html"
dev <- pp(file = "images/zymo_pca_sus_shape.png")
zymo_pca$plot
closed <- dev.off()
zymo_pca$plot

only_two_types <- subset_expt(clinical_samples, subset = "condition=='z2.3'|condition=='z2.2'")
## subset_expt(): There were 101, now there are 83 samples.
only_two_norm <- sm(normalize_expt(only_two_types, norm = "quant", transform = "log2",
                                   convert = "cpm", batch = FALSE, filter = TRUE))
onlytwo_pca <- plot_pca(only_two_norm, plot_title = "PCA of z2.2 and z2.3 parasite expression values",
                     plot_labels = FALSE)
dev <- pp(file = "images/zymo_z2.2_z2.3_pca_sus_shape.pdf")
onlytwo_pca$plot
closed <- dev.off()
onlytwo_pca$plot

zymo_3dpca <- plot_3d_pca(zymo_pca)
zymo_3dpca$plot
clinical_n <- sm(normalize_expt(clinical_samples, transform = "log2",
                                convert = "cpm", batch = FALSE, filter = TRUE))
zymo_tsne <- plot_tsne(clinical_n, plot_title = "TSNE of parasite expression values")
## plot labels was not set and there are more than 100 samples, disabling it.
zymo_tsne$plot

clinical_nb <- normalize_expt(clinical_samples, convert = "cpm", transform = "log2",
                         filter = TRUE, batch = "svaseq")
## Removing 134 low-count genes (8576 remaining).
## Setting 748 low elements to zero.
## transform_counts: Found 748 values equal to 0, adding 1 to the matrix.
clinical_nb_pca <- plot_pca(clinical_nb, plot_title = "PCA of parasite expression values",
                            plot_labels = FALSE)
dev <- pp(file = "images/clinical_nb_pca_sus_shape.png")
clinical_nb_pca$plot
closed <- dev.off()
clinical_nb_pca$plot

clinical_nb_tsne <- plot_tsne(clinical_nb, plot_title = "TSNE of parasite expression values")
## plot labels was not set and there are more than 100 samples, disabling it.
clinical_nb_tsne$plot

corheat <- plot_corheat(clinical_norm, plot_title = "Correlation heatmap of parasite
                 expression values
")
corheat$plot

plot_sm(clinical_norm)$plot
## Performing correlation.

5.5 By Cure/Fail status

cf_colors <- list(
    "cure" = "#006f00",
    "fail" = "#9dffa0",
    "unknown" = "#cbcbcb",
    "notapplicable" = "#000000")
cf_expt <- set_expt_conditions(lp_expt, fact = "clinicalcategorical") %>%
  set_expt_batches(fact = sus_categorical_current) %>%
  set_expt_colors(cf_colors)
## Warning in set_expt_colors(., cf_colors): Colors for the following categories
## are not being used: notapplicable.
table(pData(cf_expt)[["condition"]])
## 
##    cure    fail unknown 
##      38      38      25
cf_norm <- normalize_expt(cf_expt, convert = "cpm", transform = "log2",
                          norm = "quant", filter = TRUE)
## Removing 134 low-count genes (8576 remaining).
## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.
start_cf <- plot_pca(cf_norm, plot_title = "PCA of parasite expression values",
                     plot_labels = FALSE)
dev <- pp(file = "images/cf_sus_shape.png")
start_cf$plot
closed <- dev.off()
start_cf$plot

cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
## subset_expt(): There were 101, now there are 76 samples.
cf_nb <- normalize_expt(cf_nb_input, convert = "cpm", transform = "log2",
                        filter = TRUE, batch = "svaseq")
## Removing 162 low-count genes (8548 remaining).
## Setting 117 low elements to zero.
## transform_counts: Found 117 values equal to 0, adding 1 to the matrix.
cf_nb_pca <- plot_pca(cf_nb, plot_title = "PCA of parasite expression values",
                      plot_labels = FALSE)
dev <- pp(file = "images/cf_sus_share_nb.png")
cf_nb_pca$plot
closed <- dev.off()
cf_nb_pca$plot

cf_norm <- normalize_expt(cf_expt, transform = "log2", convert = "cpm",
                          filter = TRUE, norm = "quant")
## Removing 134 low-count genes (8576 remaining).
## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.
test <- pca_information(cf_norm,
                        expt_factors = c("clinicalcategorical", "zymodemecategorical",
                                         "pathogenstrain", "passagenumber"),
                        num_components = 6, plot_pcas = TRUE)
## plot labels was not set and there are more than 100 samples, disabling it.
test$anova_p
##                           PC1      PC2    PC3       PC4       PC5       PC6
## clinicalcategorical 3.139e-01 0.457872 0.9691 7.839e-03 0.2264183 3.371e-01
## zymodemecategorical 9.358e-06 0.004581 0.6655 3.058e-02 0.0001343 1.206e-01
## pathogenstrain      4.747e-01 0.870333 0.6433 5.629e-05 0.0188863 2.316e-01
## passagenumber       9.502e-01 0.174448 0.4657 3.136e-02 0.8601857 5.429e-06
test$cor_heatmap

sus_colors <- list(
    "resistant" = "#8563a7",
    "sensitive" = "#8d0000",
    "ambiguous" = "#cbcbcb",
    "unknown" = "#555555")
sus_expt <- set_expt_conditions(lp_expt, fact = "sus_category_current") %>%
  set_expt_batches(fact = "clinicalcategorical") %>%
  set_expt_colors(colors = sus_colors)

##  subset_expt(subset = "batch!='z24'") %>%
##  subset_expt(subset = "batch!='z21'")

sus_norm <- normalize_expt(sus_expt, transform = "log2", convert = "cpm",
                           norm = "quant", filter = TRUE)
## Removing 134 low-count genes (8576 remaining).
## transform_counts: Found 2 values equal to 0, adding 1 to the matrix.
sus_pca <- plot_pca(sus_norm, plot_title = "PCA of parasite expression values",
                    plot_labels = FALSE)
dev <- pp(file = "images/sus_norm_pca.png")
sus_pca[["plot"]]
closed <- dev.off()
sus_pca[["plot"]]

sus_nb <- normalize_expt(sus_expt, transform = "log2", convert = "cpm",
                         batch = "svaseq", filter = TRUE)
## Removing 134 low-count genes (8576 remaining).
## Setting 447 low elements to zero.
## transform_counts: Found 447 values equal to 0, adding 1 to the matrix.
sus_nb_pca <- plot_pca(sus_nb, plot_title = "PCA of parasite expression values",
                       plot_labels = FALSE)
dev <- pp(file = "images/sus_nb_pca.png")
sus_nb_pca[["plot"]]
closed <- dev.off()
sus_nb_pca[["plot"]]

6 Zymodeme analyses

The following sections perform a series of analyses which seek to elucidate differences between the zymodemes 2.2 and 2.3 either through differential expression or variant profiles.

6.1 Differential expression

6.1.1 With respect to zymodeme attribution

TODO: Do this with and without sva and compare the results.

zy_expt <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
## subset_expt(): There were 101, now there are 83 samples.
zy_norm <- normalize_expt(zy_expt, filter = TRUE, convert = "cpm", norm = "quant")
## Removing 152 low-count genes (8558 remaining).
zy_de_nobatch <- all_pairwise(zy_expt, filter = TRUE, model_batch = FALSE)
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
zy_table_nobatch <- combine_de_tables(
    zy_de_nobatch,
    excel = glue::glue("excel/zy_tables_nobatch-v{ver}.xlsx"))
## Deleting the file excel/zy_tables_nobatch-v202206.xlsx before writing the tables.
zy_sig_nobatch <- extract_significant_genes(
    zy_table_nobatch,
    according_to = "deseq", current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_nobatch_deseq-v{ver}.xlsx"))
## Deleting the file excel/zy_sig_nobatch_deseq-v202206.xlsx before writing the tables.
## Going to attempt to create gmt files from these results.
## There is an error lurking in extract_significant_genes()
## in which it incorrectly returns genes when not explicitly setting the 'according_to' parameter.
zy_sig_test <- extract_significant_genes(
    zy_table_nobatch,
    current_id = "GID", required_id = "GID",
    gmt = "gmt/zymodeme_test.gmt",
    excel = "excel/zy_sig_nobatch_test.xlsx")
## Deleting the file excel/zy_sig_nobatch_test.xlsx before writing the tables.
## Going to attempt to create gmt files from these results.
## For now, limiting this to deseq.
first_test <- zy_sig_nobatch[["deseq"]][["ups"]][[1]]
second_test <- zy_sig_test[["deseq"]][["ups"]][[1]]
## I think I fixed it!
expect_equal(first_test, second_test)

zy_sig_nobatch_all <- extract_significant_genes(
    zy_table_nobatch,
    current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_nobatch_all-v{ver}.xlsx"))
## Deleting the file excel/zy_sig_nobatch_all-v202206.xlsx before writing the tables.
## Going to attempt to create gmt files from these results.
## For now, limiting this to deseq.
zy_de_sva <- all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq")
## Removing 0 low-count genes (8558 remaining).
## Setting 427 low elements to zero.
## transform_counts: Found 427 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
zy_table_sva <- combine_de_tables(
    zy_de_sva, excel = glue::glue("excel/zy_tables_sva-v{ver}.xlsx"))
## Deleting the file excel/zy_tables_sva-v202206.xlsx before writing the tables.
zy_sig_sva <- extract_significant_genes(
    zy_table_sva,
    according_to = "deseq",
    current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_sva-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_sva-v{ver}.xlsx"))
## Deleting the file excel/zy_sig_sva-v202206.xlsx before writing the tables.
## Going to attempt to create gmt files from these results.

6.1.2 Images of zymodeme DE

dev <- pp(file = "images/zymo_ma.png")
zy_table_sva[["plots"]][["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]]
closed <- dev.off()
zy_table_sva[["plots"]][["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]]

6.2 With respect to cure/failure

In contrast, we can search for genes which are differentially expressed with respect to cure/failure status.

##cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
cf_de <- all_pairwise(cf_nb_input, filter = TRUE, model_batch = "svaseq")
## Removing 0 low-count genes (8548 remaining).
## Setting 117 low elements to zero.
## transform_counts: Found 117 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
cf_table <- combine_de_tables(cf_de, excel = glue::glue("excel/cf_tables-v{ver}.xlsx"))
## Deleting the file excel/cf_tables-v202206.xlsx before writing the tables.
cf_sig <- extract_significant_genes(cf_table, excel = glue::glue("excel/cf_sig-v{ver}.xlsx"))
## Deleting the file excel/cf_sig-v202206.xlsx before writing the tables.
dev <- pp(file = "images/cf_ma.png")
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
closed <- dev.off()
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]

6.3 With respect to susceptibility

Finally, we can use our category of susceptibility and look for genes which change from sensitive to resistant. Keep in mind, though, that for the moment we have a lot of ambiguous and unknown strains.

sus_de_sva <- all_pairwise(sus_expt, filter = TRUE, model_batch = "svaseq")
## Removing 0 low-count genes (8576 remaining).
## Setting 447 low elements to zero.
## transform_counts: Found 447 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
sus_table_sva <- combine_de_tables(
    sus_de_sva,
    excel = glue::glue("excel/sus_tables_sva-v{ver}.xlsx"))
## Deleting the file excel/sus_tables_sva-v202206.xlsx before writing the tables.
sus_sig_sva <- extract_significant_genes(
    sus_table_sva, according_to = "deseq",
    excel = glue::glue("excel/sus_sig_sva-v{ver}.xlsx"))
## Deleting the file excel/sus_sig_sva-v202206.xlsx before writing the tables.
sus_de_nobatch <- all_pairwise(sus_expt, filter = TRUE, model_batch = FALSE)
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
sus_table_nobatch <- combine_de_tables(
    sus_de_nobatch,
    excel = glue::glue("excel/sus_tables_nobatch-v{ver}.xlsx"))
## Deleting the file excel/sus_tables_nobatch-v202206.xlsx before writing the tables.
sus_sig_nobatch <- extract_significant_genes(
    sus_table_nobatch, according_to = "deseq",
    excel = glue::glue("excel/sus_sig_nobatch-v{ver}.xlsx"))
## Deleting the file excel/sus_sig_nobatch-v202206.xlsx before writing the tables.

7 Compare Susceptibility to Zymodemes

Checking on my function to do the comparison first, thus the comparison of the nobatch vs. sva result for the susceptibility data.

Yes, the compare_de_results() function assumes that the results it compares contain identical sets of contrasts, which is explicitly not the case for these data. Thus I am making a simpler function, compare_de_tables() which handles this scenario.

sus_nobatch_sva <- compare_de_results(sus_table_nobatch, sus_table_sva)
## Testing method: limma.
## Adding method: limma to the set.
## Testing method: deseq.
## Adding method: deseq to the set.
## Testing method: edger.
## Adding method: edger to the set.
##  Starting method limma, table resistant_vs_ambiguous.
##  Starting method limma, table sensitive_vs_ambiguous.
##  Starting method limma, table unknown_vs_ambiguous.
##  Starting method limma, table sensitive_vs_resistant.
##  Starting method limma, table unknown_vs_resistant.
##  Starting method limma, table unknown_vs_sensitive.
##  Starting method deseq, table resistant_vs_ambiguous.
##  Starting method deseq, table sensitive_vs_ambiguous.
##  Starting method deseq, table unknown_vs_ambiguous.
##  Starting method deseq, table sensitive_vs_resistant.
##  Starting method deseq, table unknown_vs_resistant.
##  Starting method deseq, table unknown_vs_sensitive.
##  Starting method edger, table resistant_vs_ambiguous.
##  Starting method edger, table sensitive_vs_ambiguous.
##  Starting method edger, table unknown_vs_ambiguous.
##  Starting method edger, table sensitive_vs_resistant.
##  Starting method edger, table unknown_vs_resistant.
##  Starting method edger, table unknown_vs_sensitive.

7.1 Look for agreement between sensitivity and zymodemes

Remind myself, the data structures are (zy|sus)_(de|table|sig).

zy_df <- zy_table_sva[["data"]][["z23_vs_z22"]]
sus_df <- sus_table_sva[["data"]][["sensitive_vs_resistant"]]

both_df <- merge(zy_df, sus_df, by = "row.names")
plot_df <- both_df[, c("deseq_logfc.x", "deseq_logfc.y")]
rownames(plot_df) <- both_df[["Row.names"]]
colnames(plot_df) <- c("z23_vs_z22", "sensitive_vs_resistant")

compare <- plot_linear_scatter(plot_df)
## Warning in plot_multihistogram(df): NAs introduced by coercion
dev <- pp(file = "images/compare_sus_zy.png")
compare$scatter
closed <- dev.off()
compare$scatter

compare$cor
## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = -156, df = 8556, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8649 -0.8538
## sample estimates:
##     cor 
## -0.8595
knitr::kable(head(sus_sig_sva$deseq$ups$sensitive_vs_resistant, n = 20))
gid annotgeneproduct annotgenetype chromosome start end strand annotgeneentrezid annotgenename annotstrand annotchromosome annotcdslength length deseq_logfc deseq_adjp edger_logfc edger_adjp limma_logfc limma_adjp basic_nummed basic_denmed basic_numvar basic_denvar basic_logfc basic_t basic_p basic_adjp deseq_basemean deseq_lfcse deseq_stat deseq_p ebseq_fc ebseq_logfc ebseq_c1mean ebseq_c2mean ebseq_mean ebseq_var ebseq_postfc ebseq_ppee ebseq_ppde ebseq_adjp edger_logcpm edger_lr edger_p limma_ave limma_t limma_b limma_p limma_adjp_ihw deseq_adjp_ihw edger_adjp_ihw ebseq_adjp_ihw basic_adjp_ihw lfc_meta lfc_var lfc_varbymed p_meta p_var
LPAL13_080010800 LPAL13_080010800 hypothetical protein protein coding LpaL13_08 199409 199792 - reverse 8 384.0 383 20.320 0.0000 6.193 0.0655 1.5820 0.3819 -2.4080 -4.1720 4.869 1.3462 1.7640 2.875 0.0129 0.2759 7.815 1.3390 15.180 0.0000 1516.477 10.566 0.0000 15.15 12.76 4.649e+02 2.667 1.0000 0.0000 1.0000 -1.0340 11.680 0.0006 -3.2250 1.7450 -4.161 0.0841 4.650e-01 4.439e-48 5.587e-02 0.000e+00 2.787e-01 5.880 1.519e+01 2.583e+00 2.824e-02 2.339e-03
LPAL13_000035800 LPAL13_000035800 hypothetical protein protein coding LPAL13_SCAF000500 737 1006 - reverse Not Assigned 270.0 269 6.439 0.0146 6.339 0.0424 2.7710 0.6601 5.3420 -0.6307 16.890 18.9800 5.9730 3.109 0.0178 0.3120 2530.000 1.5380 4.188 0.0000 5.006 2.324 1040.3152 5208.30 4550.20 2.011e+07 5.489 0.9927 0.0073 0.9927 6.7330 15.680 0.0001 2.0600 0.9767 -5.320 0.3311 7.056e-01 1.638e-02 1.000e+00 1.059e-02 3.127e-01 6.697 8.388e+00 1.252e+00 1.104e-01 3.653e-02
LPAL13_000051300 LPAL13_000051300 hypothetical protein, conserved protein coding LPAL13_SCAF000772 11 2344 + forward Not Assigned 2334.0 2333 6.338 0.0169 5.875 0.1116 1.5040 0.7507 0.4368 -3.0740 11.077 10.6988 3.5100 2.406 0.0467 0.4505 100.500 1.5580 4.069 0.0000 5.701 2.511 40.6590 231.83 201.65 1.099e+05 5.803 0.7902 0.2098 0.7902 2.1570 8.694 0.0032 -1.3160 0.7534 -5.038 0.4530 9.051e-01 1.638e-02 1.016e-01 1.588e-01 4.557e-01 4.129 3.301e+00 7.996e-01 1.521e-01 6.792e-02
LPAL13_000040700 LPAL13_000040700 hypothetical protein, conserved protein coding LPAL13_SCAF000598 54 1067 + forward Not Assigned 1014.0 1013 5.828 0.0158 5.239 0.0996 1.9130 0.3864 -1.4860 -3.7180 7.371 3.4602 2.2310 2.484 0.0334 0.3941 16.030 1.4240 4.094 0.0000 22.963 4.521 1.4101 32.60 27.68 1.160e+03 4.141 0.9758 0.0242 0.9758 -0.2624 9.176 0.0025 -2.4010 1.7250 -4.168 0.0877 4.729e-01 1.329e-02 7.503e-02 1.695e-02 3.990e-01 3.950 1.390e+00 3.519e-01 3.007e-02 2.494e-03
LPAL13_320026300 LPAL13_320026300 hypothetical protein, conserved protein coding LpaL13_32 754268 755485 - reverse 32 1218.0 1217 5.815 0.0278 5.694 0.0655 3.4130 0.6600 4.9200 -2.2900 15.806 20.3610 7.2110 3.657 0.0091 0.2468 1278.000 1.5290 3.804 0.0001 6.746 2.754 402.5215 2715.39 2350.20 2.570e+06 6.912 0.0000 0.0000 0.0000 5.7510 12.290 0.0005 1.8270 0.9769 -5.144 0.3310 6.460e-01 2.039e-02 5.306e-02 0.000e+00 2.382e-01 6.561 8.993e+00 1.371e+00 1.105e-01 3.645e-02
LPAL13_000053200 LPAL13_000053200 hypothetical protein protein coding LPAL13_SCAF000804 5037 5249 - reverse Not Assigned 213.0 212 5.778 0.0201 5.471 0.1116 2.9810 0.4146 1.0020 -3.2220 8.306 8.5830 4.2250 3.250 0.0142 0.2874 65.110 1.4550 3.970 0.0001 7.862 2.975 17.8618 140.49 121.13 8.740e+03 5.786 0.2220 0.7780 0.2220 1.5190 8.712 0.0032 -0.9762 1.6440 -4.252 0.1034 4.022e-01 1.638e-02 1.058e-01 6.005e-01 2.825e-01 4.714 1.697e-02 3.601e-03 3.554e-02 3.456e-03
LPAL13_040019400 LPAL13_040019400 hypothetical protein protein coding LpaL13_04 440768 441127 - reverse 4 360.0 359 5.617 0.0011 5.667 0.0377 3.3550 0.1598 -0.2906 -3.3870 1.964 1.0915 3.0970 6.278 0.0002 0.0379 24.280 1.1290 4.974 0.0000 75.038 6.229 0.5691 43.45 36.68 2.570e+03 5.770 0.9286 0.0714 0.9286 0.1557 16.350 0.0001 -1.7790 2.6670 -3.051 0.0090 1.690e-01 8.222e-04 2.489e-02 4.933e-02 3.791e-02 4.880 1.579e-01 3.236e-02 3.005e-03 2.661e-05
LPAL13_000044900 LPAL13_000044900 actin-related protein 2, putative protein coding LPAL13_SCAF000645 507 1685 - reverse Not Assigned 1179.0 1178 5.110 0.0725 5.001 0.1465 3.3200 0.6565 3.9790 -2.6360 15.764 18.4621 6.6150 3.501 0.0107 0.2613 702.700 1.5800 3.235 0.0012 4.266 2.093 326.2122 1391.52 1223.32 5.911e+05 4.376 0.7596 0.2404 0.7596 4.8930 7.468 0.0063 1.1900 0.9848 -5.036 0.3271 6.443e-01 6.832e-02 1.149e-01 1.861e-01 2.613e-01 4.475 4.114e-01 9.195e-02 1.115e-01 3.486e-02
LPAL13_080010600 LPAL13_080010600 hypothetical protein, conserved protein coding LpaL13_08 195555 195749 - reverse 8 195.0 194 4.396 0.0794 5.726 0.0967 1.5700 0.3970 -2.1930 -3.8060 5.256 2.8515 1.6130 2.017 0.0749 0.5131 9.189 1.3980 3.145 0.0017 39.569 5.306 0.4700 18.98 16.06 6.054e+02 2.954 0.9980 0.0020 0.9980 -1.0000 9.357 0.0022 -3.1470 1.6900 -4.208 0.0942 4.868e-01 6.871e-02 8.252e-02 1.774e-03 5.141e-01 3.684 1.813e+00 4.920e-01 3.270e-02 2.839e-03
LPAL13_000017600 LPAL13_000017600 hypothetical protein, conserved protein coding LPAL13_SCAF000146 359 586 + forward Not Assigned 228.0 227 4.238 0.0088 4.215 0.0655 3.1330 0.4819 4.1660 -0.2452 4.997 7.6872 4.4110 3.679 0.0095 0.2495 522.000 0.9440 4.489 0.0000 5.282 2.401 195.8671 1034.62 902.19 5.173e+05 5.379 0.7636 0.2364 0.7636 4.4620 11.480 0.0007 2.3240 1.4670 -4.646 0.1456 4.648e-01 4.841e-03 5.587e-02 2.372e-01 2.457e-01 4.013 5.846e-01 1.457e-01 4.877e-02 7.032e-03
LPAL13_000011700 LPAL13_000011700 hypothetical protein protein coding LPAL13_SCAF000076 101 364 - reverse Not Assigned 264.0 263 3.781 0.2096 3.190 0.3457 0.9487 0.6462 -1.7460 -3.3690 6.611 6.7544 1.6230 1.406 0.2027 0.6676 10.640 1.5930 2.374 0.0176 2.193 1.133 11.5161 25.27 23.10 6.457e+02 1.612 0.9988 0.0012 0.9988 -0.7439 3.726 0.0536 -3.0780 1.0150 -4.759 0.3125 6.916e-01 1.926e-01 3.290e-01 1.080e-03 6.741e-01 2.359 1.277e+00 5.411e-01 1.279e-01 2.588e-02
LPAL13_300029400 LPAL13_300029400 hypothetical protein, conserved protein coding LpaL13_30 853953 854150 - reverse 30 198.0 197 3.622 0.0226 3.591 0.0836 2.3670 0.2956 1.6850 -0.7559 2.287 3.8595 2.4410 2.888 0.0270 0.3711 83.200 0.9295 3.897 0.0001 5.875 2.555 27.3700 160.86 139.78 1.550e+04 4.845 0.3256 0.6744 0.3256 1.8240 10.030 0.0015 -0.0441 2.0380 -3.760 0.0442 3.418e-01 2.039e-02 7.557e-02 4.613e-01 3.784e-01 3.388 2.178e-01 6.428e-02 1.529e-02 6.290e-04
LPAL13_000026500 LPAL13_000026500 hypothetical protein protein coding LPAL13_SCAF000301 144 494 - reverse Not Assigned 351.0 350 3.429 0.0458 3.350 0.1036 1.2590 0.5605 -0.1199 -1.8470 6.713 3.6573 1.7280 1.909 0.0892 0.5434 34.270 0.9714 3.530 0.0004 7.151 2.838 10.3735 74.24 64.16 6.426e+03 4.797 0.7472 0.2528 0.7472 0.7298 8.949 0.0028 -1.0640 1.2470 -4.684 0.2154 6.477e-01 4.823e-02 9.656e-02 1.640e-01 5.473e-01 2.538 6.052e-01 2.385e-01 7.286e-02 1.524e-02
LPAL13_350011800 LPAL13_350011800 hypothetical protein, conserved protein coding LpaL13_35 171009 171242 + forward 35 234.0 233 3.252 0.0237 3.240 0.0811 2.6250 0.3185 2.6390 -0.3719 3.096 4.1725 3.0110 3.383 0.0132 0.2798 147.500 0.8413 3.866 0.0001 5.933 2.569 55.1376 327.20 284.24 7.387e+04 5.539 0.7813 0.2187 0.7813 2.6450 10.220 0.0014 0.9678 1.9510 -3.912 0.0539 3.865e-01 1.748e-02 6.787e-02 1.500e-01 2.808e-01 3.353 7.114e-01 2.122e-01 1.848e-02 9.431e-04
LPAL13_000035500 LPAL13_000035500 hypothetical protein, conserved protein coding LPAL13_SCAF000492 7045 7410 + forward Not Assigned 366.0 365 3.046 0.0146 3.036 0.0655 2.2610 0.3307 4.1990 1.5110 3.729 2.4782 2.6880 3.694 0.0059 0.2117 405.500 0.7307 4.169 0.0000 5.884 2.557 159.4778 938.47 815.47 4.054e+05 5.969 0.2751 0.7249 0.2751 4.1070 12.180 0.0005 2.7790 1.9030 -4.229 0.0599 4.023e-01 1.329e-02 5.306e-02 7.251e-01 2.038e-01 2.983 2.599e-01 8.714e-02 2.014e-02 1.187e-03
LPAL13_000014000 LPAL13_000014000 hypothetical protein protein coding LPAL13_SCAF000119 655 942 + forward Not Assigned 288.0 287 2.993 0.0146 2.981 0.0655 2.6310 0.2234 2.3600 -0.1140 1.735 1.3946 2.4740 4.620 0.0020 0.1382 103.100 0.7231 4.139 0.0000 8.159 3.028 26.6764 217.73 187.56 1.404e+04 6.525 0.0002 0.9998 0.0002 2.1600 12.060 0.0005 0.8840 2.3480 -3.242 0.0209 2.562e-01 1.329e-02 5.539e-02 6.463e-01 1.382e-01 3.277 1.260e+00 3.844e-01 7.137e-03 1.413e-04
LPAL13_220019500 LPAL13_220019500 hypothetical protein protein coding LpaL13_22 578260 578538 + forward 22 279.0 278 2.980 0.0093 2.966 0.0485 1.9830 0.4115 3.3980 0.8174 2.962 3.1977 2.5810 3.263 0.0142 0.2874 250.100 0.6700 4.447 0.0000 4.103 2.037 124.2553 509.87 448.98 1.119e+05 4.080 0.7662 0.2338 0.7662 3.4140 14.020 0.0002 2.3500 1.6580 -4.492 0.1005 3.992e-01 5.746e-03 4.981e-02 2.346e-01 2.888e-01 2.626 2.924e-03 1.114e-03 3.356e-02 3.360e-03
LPAL13_170014500 LPAL13_170014500 hypothetical protein, conserved protein coding LpaL13_17 361708 362040 + forward 17 333.0 332 2.840 0.1999 2.302 0.4607 1.0220 0.6482 -1.4260 -2.4980 6.661 4.1852 1.0720 1.126 0.2916 0.7303 17.700 1.1800 2.406 0.0161 4.559 2.189 8.0539 36.75 32.22 1.905e+03 2.687 0.9969 0.0031 0.9969 -0.3072 2.484 0.1150 -2.5500 1.0080 -4.798 0.3161 7.529e-01 1.694e-01 4.442e-01 2.265e-03 7.341e-01 1.891 4.382e-01 2.317e-01 1.491e-01 2.337e-02
LPAL13_000011800 LPAL13_000011800 hypothetical protein, conserved protein coding LPAL13_SCAF000076 446 640 - reverse Not Assigned 195.0 194 2.721 0.2376 2.680 0.3032 0.4672 0.8177 -2.3460 -3.3070 4.451 3.4997 0.9602 1.130 0.2930 0.7312 8.105 1.1990 2.269 0.0232 7.713 2.947 2.0868 16.16 13.94 7.314e+02 2.428 0.9990 0.0010 0.9990 -1.0270 4.270 0.0388 -3.2160 0.5817 -4.948 0.5621 9.542e-01 2.120e-01 2.911e-01 1.274e-03 7.350e-01 1.757 1.182e+00 6.730e-01 2.080e-01 9.408e-02
LPAL13_170006400 LPAL13_170006400 receptor-type adenylate cyclase a protein coding LpaL13_17 43122 44198 - reverse 17 1077.0 1076 2.689 0.0001 2.676 0.0030 2.1970 0.1183 3.3390 0.9561 1.476 0.6494 2.3830 6.065 0.0001 0.0331 219.700 0.4767 5.641 0.0000 7.931 2.988 56.7322 450.03 387.93 1.589e+05 7.401 0.1579 0.8421 0.1579 3.2250 23.860 0.0000 2.5530 2.9490 -1.974 0.0040 1.139e-01 5.269e-05 2.187e-03 5.757e-01 3.310e-02 2.605 2.571e-01 9.872e-02 1.328e-03 5.289e-06 
knitr::kable(head(sus_sig_sva$deseq$downs$sensitive_vs_resistant, n = 20))
gid annotgeneproduct annotgenetype chromosome start end strand annotgeneentrezid annotgenename annotstrand annotchromosome annotcdslength length deseq_logfc deseq_adjp edger_logfc edger_adjp limma_logfc limma_adjp basic_nummed basic_denmed basic_numvar basic_denvar basic_logfc basic_t basic_p basic_adjp deseq_basemean deseq_lfcse deseq_stat deseq_p ebseq_fc ebseq_logfc ebseq_c1mean ebseq_c2mean ebseq_mean ebseq_var ebseq_postfc ebseq_ppee ebseq_ppde ebseq_adjp edger_logcpm edger_lr edger_p limma_ave limma_t limma_b limma_p limma_adjp_ihw deseq_adjp_ihw edger_adjp_ihw ebseq_adjp_ihw basic_adjp_ihw lfc_meta lfc_var lfc_varbymed p_meta p_var
LPAL13_000038500 LPAL13_000038500 hypothetical protein protein coding LPAL13_SCAF000575 39 251 + forward Not Assigned 213.0 212 -2.091 0.1269 -2.091 0.0996 -3.9290 0.0003 -2.2000 1.3670 4.1969 1.5731 -3.5680 -5.688 0.0001 0.0376 24.890 0.7462 -2.802 0.0051 0.1845 -2.4384 93.33 17.210 29.228 1832.8 0.2378 0.0574 0.9426 0.0574 0.1024 9.214 0.0024 -1.3780 -5.977 7.2490 0.0000 2.462e-04 1.284e-01 9.438e-02 6.095e-01 3.564e-02 -2.675 1.178e-01 -4.404e-02 2.492e-03 6.442e-06
LPAL13_230016400 LPAL13_230016400 na/h antiporter-like protein protein coding LpaL13_23 350725 355113 - reverse 23 4389.0 4388 -2.089 0.2707 -2.078 0.2760 -1.9580 0.1561 -3.1410 -0.6244 4.7278 17.5993 -2.5170 -1.434 0.2058 0.6696 20.530 0.9754 -2.141 0.0322 0.1066 -3.2300 214.06 22.806 53.004 28677.0 0.2063 0.0000 1.0000 0.0000 -0.7043 4.701 0.0302 -3.4410 -2.682 -2.7110 0.0086 1.520e-01 2.458e-01 2.629e-01 6.832e-01 6.750e-01 -2.117 2.036e-01 -9.618e-02 2.366e-02 1.715e-04
LPAL13_190021800 LPAL13_190021800 atp-dependent zinc metallopeptidase, putative protein coding LpaL13_19 593155 594951 - reverse 19 1797.0 1796 -1.963 0.2377 -1.978 0.2516 -2.4300 0.0426 -3.3430 -0.4277 3.3613 9.2643 -2.9150 -2.270 0.0660 0.4971 8.895 0.8652 -2.269 0.0233 0.0928 -3.4291 106.88 9.914 25.225 6086.5 0.2013 0.0000 0.0000 0.0000 -1.6050 5.099 0.0239 -3.5880 -3.928 -0.1367 0.0002 4.731e-02 2.448e-01 2.020e-01 0.000e+00 4.986e-01 -2.148 3.683e-02 -1.715e-02 1.580e-02 1.835e-04
LPAL13_000012100 LPAL13_000012100 hypothetical protein protein coding LPAL13_SCAF000080 1637 1894 - reverse Not Assigned 258.0 257 -1.877 0.2449 -1.872 0.2049 -3.1150 0.0264 -2.2920 1.0840 5.5781 0.1631 -3.3760 -7.520 0.0000 0.0000 26.840 0.8356 -2.246 0.0247 0.3138 -1.6723 58.98 18.498 24.890 783.4 0.3338 0.9778 0.0222 0.9778 0.2123 5.973 0.0145 -1.3610 -4.171 1.1960 0.0001 2.118e-02 2.215e-01 1.777e-01 1.551e-02 1.976e-05 -2.240 3.047e-02 -1.360e-02 1.310e-02 1.532e-04
LPAL13_310039200 LPAL13_310039200 hypothetical protein protein coding LpaL13_31 1301745 1301972 - reverse 31 228.0 227 -1.770 0.0844 -1.776 0.0732 -1.7070 0.0981 1.2990 3.2320 2.0151 0.5994 -1.9330 -4.789 0.0004 0.0527 166.700 0.5691 -3.110 0.0019 0.4179 -1.2586 334.46 139.780 170.519 32674.7 0.4749 0.9967 0.0033 0.9967 2.8240 10.740 0.0010 1.9850 -3.118 -1.6700 0.0024 1.150e-01 8.714e-02 5.587e-02 2.814e-03 5.352e-02 -1.799 1.653e-01 -9.190e-02 1.770e-03 4.572e-07
LPAL13_000012000 LPAL13_000012000 hypothetical protein protein coding LPAL13_SCAF000080 710 1159 - reverse Not Assigned 450.0 449 -1.681 0.2796 -1.688 0.2364 -3.2580 0.0296 0.2199 3.4740 7.2074 1.5132 -3.2540 -4.709 0.0002 0.0422 181.400 0.7966 -2.111 0.0348 0.2798 -1.8373 442.30 123.769 174.064 46363.5 0.3268 0.8804 0.1196 0.8804 2.9370 5.346 0.0208 1.2740 -4.120 1.3910 0.0001 3.473e-02 2.281e-01 2.235e-01 7.793e-02 4.307e-02 -2.118 1.032e-01 -4.870e-02 1.855e-02 3.052e-04
LPAL13_050005000 LPAL13_050005000 hypothetical protein protein coding LpaL13_05 3394 3612 - reverse 5 219.0 218 -1.554 0.2720 -1.564 0.2271 -3.2260 0.0035 -0.2027 2.7720 3.4982 0.1021 -2.9750 -8.370 0.0000 0.0000 81.790 0.7272 -2.137 0.0326 0.2880 -1.7959 208.35 59.995 83.419 9594.8 0.3145 0.9004 0.0996 0.9004 1.7790 5.530 0.0187 0.5335 -5.104 4.8070 0.0000 4.100e-03 2.462e-01 2.130e-01 1.002e-01 4.935e-06 -2.051 1.283e-01 -6.253e-02 1.709e-02 2.671e-04
LPAL13_170012500 LPAL13_170012500 unspecified product tRNA encoding LpaL13_17 undefined undefined + forward 17 0.0 undefined -1.485 0.0201 -1.491 0.0424 -1.6540 0.0385 -1.0590 0.6721 0.8350 2.0797 -1.7310 -2.835 0.0310 0.3895 15.070 0.3729 -3.982 0.0001 0.3008 -1.7331 61.24 18.414 25.177 1105.5 0.3901 0.9327 0.0673 0.9327 -0.5232 15.360 0.0001 -1.1000 -3.994 0.6436 0.0001 3.996e-02 1.638e-02 3.413e-02 5.251e-02 3.885e-01 -1.574 8.330e-02 -5.291e-02 9.438e-05 8.474e-10
LPAL13_140019300 LPAL13_140019300 bt1 family, putative protein coding LpaL13_14 530784 531350 + forward 14 567.0 566 -1.476 0.0930 -1.485 0.0763 -1.8490 0.0601 4.7670 6.6090 0.7180 2.1059 -1.8420 -3.014 0.0254 0.3632 1707.000 0.4855 -3.040 0.0024 0.2530 -1.9830 4355.82 1101.838 1615.624 3386453.6 0.2931 0.9143 0.0857 0.9143 6.1650 10.460 0.0012 5.4370 -3.611 -0.5668 0.0005 7.373e-02 8.987e-02 6.911e-02 8.629e-02 5.091e-01 -1.613 2.209e-02 -1.369e-02 1.356e-03 9.027e-07
LPAL13_220018100 LPAL13_220018100 60s ribosomal protein l14, putative protein coding LpaL13_22 517892 518419 + forward 22 528.0 527 -1.456 0.2696 -1.468 0.2244 -1.5060 0.1294 0.4183 2.5250 3.4619 4.9553 -2.1070 -2.180 0.0694 0.5057 74.590 0.6767 -2.152 0.0314 0.2603 -1.9417 449.24 116.932 169.401 89466.6 0.3722 0.0410 0.9590 0.0410 1.5570 5.583 0.0181 -0.0588 -2.855 -2.2410 0.0053 1.278e-01 2.753e-01 2.130e-01 7.234e-01 5.057e-01 -1.506 9.206e-02 -6.114e-02 1.826e-02 1.707e-04
LPAL13_180013900 LPAL13_180013900 hypothetical protein protein coding LpaL13_18 351792 352085 + forward 18 294.0 293 -1.440 0.0946 -1.448 0.0836 -1.9510 0.0264 -0.3401 1.7010 1.0858 0.2305 -2.0410 -7.589 0.0000 0.0012 37.000 0.4760 -3.025 0.0025 0.3809 -1.3925 88.46 33.687 42.335 963.5 0.3777 0.7646 0.2354 0.7646 0.6607 10.050 0.0015 0.0938 -4.174 1.4470 0.0001 2.118e-02 8.682e-02 7.101e-02 1.782e-01 1.368e-03 -1.615 4.067e-03 -2.518e-03 1.357e-03 1.484e-06
LPAL13_340039600 LPAL13_340039600 hypothetical protein protein coding LpaL13_34 1247554 1247757 - reverse 34 204.0 203 -1.419 0.2199 -1.431 0.1814 -2.4160 0.0601 1.1310 3.8380 3.8763 0.6060 -2.7070 -5.743 0.0000 0.0074 209.100 0.6098 -2.327 0.0199 0.2517 -1.9900 513.91 129.367 190.084 40331.1 0.2831 0.9820 0.0180 0.9820 3.1250 6.505 0.0108 2.0560 -3.546 -0.4705 0.0006 7.196e-02 1.904e-01 1.464e-01 1.837e-02 7.362e-03 -1.683 1.952e-02 -1.160e-02 1.044e-02 9.366e-05
LPAL13_350073400 LPAL13_350073400 hypothetical protein protein coding LpaL13_35 2342701 2342883 + forward 35 183.0 182 -1.341 0.1589 -1.348 0.1482 -1.5020 0.1297 -0.5515 1.4110 2.5225 1.5709 -1.9630 -3.363 0.0093 0.2480 40.680 0.5138 -2.611 0.0090 0.3126 -1.6775 115.82 36.202 48.773 5397.3 0.4095 0.8961 0.1039 0.8961 0.7744 7.245 0.0071 0.0322 -2.847 -2.2110 0.0054 1.575e-01 1.284e-01 1.214e-01 8.077e-02 2.445e-01 -1.411 5.483e-02 -3.884e-02 7.176e-03 3.349e-06
LPAL13_000052700 LPAL13_000052700 hypothetical protein, conserved protein coding LPAL13_SCAF000789 102 398 - reverse Not Assigned 297.0 296 -1.251 0.1863 -1.253 0.1435 -1.9880 0.0430 0.3101 1.3430 1.0364 1.9051 -1.0330 -1.746 0.1309 0.6042 48.600 0.5045 -2.481 0.0131 0.4705 -1.0876 113.76 53.524 63.035 6424.5 0.6426 0.9089 0.0911 0.9089 1.1240 7.523 0.0061 0.4429 -3.901 0.6130 0.0002 4.731e-02 1.947e-01 1.204e-01 5.946e-02 6.092e-01 -1.520 8.251e-03 -5.428e-03 6.462e-03 4.199e-05
LPAL13_000029000 LPAL13_000029000 hypothetical protein protein coding LPAL13_SCAF000368 992 1243 + forward Not Assigned 252.0 251 -1.238 0.0925 -1.239 0.0967 -1.8300 0.0147 -1.5320 0.4581 1.9348 1.2514 -1.9900 -3.836 0.0047 0.1996 12.530 0.4062 -3.047 0.0023 0.3505 -1.5124 44.08 15.445 19.967 375.7 0.3476 0.9773 0.0227 0.9773 -0.7822 9.360 0.0022 -1.3720 -4.501 2.0030 0.0000 1.073e-02 8.612e-02 7.537e-02 1.586e-02 2.006e-01 -1.424 9.720e-04 -6.826e-04 1.515e-03 1.681e-06
LPAL13_000036900 LPAL13_000036900 hypothetical protein, conserved protein coding LPAL13_SCAF000515 1206 1448 - reverse Not Assigned 243.0 242 -1.153 0.2053 -1.156 0.1945 -1.1620 0.1880 -1.2290 -0.2827 1.0582 2.2565 -0.9463 -1.479 0.1902 0.6584 13.110 0.4832 -2.386 0.0170 0.5931 -0.7537 30.05 17.817 19.749 299.5 0.5698 0.9967 0.0033 0.9967 -0.5727 6.208 0.0127 -1.3210 -2.529 -2.9270 0.0130 1.783e-01 1.906e-01 1.853e-01 2.492e-03 6.574e-01 -1.227 6.906e-02 -5.629e-02 1.426e-02 5.766e-06
LPAL13_310010700 LPAL13_310010700 unspecified product tRNA encoding LpaL13_31 undefined undefined + forward 31 0.0 undefined -1.145 0.2938 -1.148 0.2755 -2.1570 0.0178 -2.4410 -0.6141 1.3044 2.4921 -1.8270 -2.705 0.0352 0.4040 7.418 0.5550 -2.064 0.0390 0.3393 -1.5594 22.39 7.589 9.925 240.6 0.4424 0.9643 0.0357 0.9643 -1.4780 4.713 0.0299 -2.2720 -4.378 1.0750 0.0000 1.971e-02 2.998e-01 2.311e-01 2.482e-02 4.101e-01 -1.459 4.704e-02 -3.225e-02 2.300e-02 4.165e-04
LPAL13_320038700 LPAL13_320038700 hypothetical protein, conserved protein coding LpaL13_32 1175024 1175257 + forward 32 234.0 233 -1.036 0.0651 -1.047 0.0655 -1.2640 0.0264 2.5620 3.7120 0.4995 0.1452 -1.1500 -5.764 0.0001 0.0241 236.000 0.3119 -3.324 0.0009 0.5046 -0.9869 434.30 219.124 253.099 19812.8 0.5143 0.9845 0.0155 0.9845 3.3090 12.090 0.0005 3.0670 -4.199 1.6080 0.0001 2.077e-02 5.975e-02 5.306e-02 1.231e-02 2.321e-02 -1.175 2.077e-02 -1.768e-02 4.852e-04 1.724e-07
LPAL13_340039700 LPAL13_340039700 snare domain containing protein, putative protein coding LpaL13_34 1248192 1248947 - reverse 34 756.0 755 -1.028 0.1426 -1.041 0.0996 -1.3740 0.0601 4.5810 6.1530 0.6679 0.7584 -1.5730 -4.098 0.0049 0.1996 1247.000 0.3790 -2.713 0.0067 0.3481 -1.5226 2654.24 923.846 1197.066 926548.0 0.3772 0.9957 0.0043 0.9957 5.7080 9.187 0.0024 5.2520 -3.608 -0.5758 0.0005 1.000e+00 1.067e-01 8.876e-02 3.210e-03 2.006e-01 -1.184 9.372e-03 -7.915e-03 3.200e-03 1.001e-05
LPAL13_310008200 LPAL13_310008200 hypothetical protein protein coding LpaL13_31 92723 93040 - reverse 31 318.0 317 -1.019 0.0117 -1.031 0.0064 -0.8623 0.0529 4.3430 5.0130 0.5127 0.7282 -0.6702 -1.808 0.1176 0.5843 623.600 0.2344 -4.349 0.0000 0.6088 -0.7160 1217.36 741.101 816.299 205797.2 0.6286 0.9970 0.0030 0.9970 4.7200 21.410 0.0000 4.5300 -3.750 -0.0577 0.0003 5.132e-02 1.067e-02 4.389e-03 2.286e-03 5.909e-01 -1.061 7.325e-02 -6.901e-02 1.055e-04 2.815e-08 
sus_ma <- sus_table_sva[["plots"]][["sensitive_vs_resistant"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file = "images/sus_ma_sva.png")
sus_ma
closed <- dev.off()
sus_ma

## test <- ggplt(sus_ma)

7.2 Ontology searches

Now let us look for ontology categories which are increased in the 2.3 samples followed by the 2.2 samples.

## Gene categories more represented in the 2.3 group.
zy_go_up <- simple_goseq(sig_genes = zy_sig_sva[["deseq"]][["ups"]][[1]],
                         go_db = lp_go, length_db = lp_lengths)
## Found 12 go_db genes and 45 length_db genes out of 45.
## Testing that go categories are defined.
## Removing undefined categories.
## Gathering synonyms.
## Gathering category definitions.
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## Gene categories more represented in the 2.2 group.
zy_go_down <- simple_goseq(sig_genes = zy_sig_sva[["deseq"]][["downs"]][[1]],
                           go_db = lp_go, length_db = lp_lengths)
## Found 17 go_db genes and 83 length_db genes out of 83.
## Testing that go categories are defined.
## Removing undefined categories.
## Gathering synonyms.
## Gathering category definitions.
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"

7.2.1 A couple plots from the differential expression

7.2.1.1 Number of genes in agreement among DE methods, 2.3 more than 2.2

In the function ‘combined_de_tables()’ above, one of the tasks performed is to look at the agreement among DESeq2, limma, and edgeR. The following show a couple of these for the set of genes observed with a fold-change >= |2| and adjusted p-value <= 0.05.

zy_table_sva[["venns"]][[1]][["p_lfc1"]][["up_noweight"]]

7.2.1.2 Number of genes in agreement among DE methods, 2.2 more than 2.3

zy_table_sva[["venns"]][[1]][["p_lfc1"]][["down_noweight"]]

7.2.1.3 goseq ontology plots of groups of genes, 2.3 more than 2.2

zy_go_up[["pvalue_plots"]][["bpp_plot_over"]]

7.2.1.4 goseq ontology plots of groups of genes, 2.2 more than 2.3

zy_go_down[["pvalue_plots"]][["bpp_plot_over"]]

7.3 Zymodeme enzyme gene IDs

Najib read me an email listing off the gene names associated with the zymodeme classification. I took those names and cross referenced them against the Leishmania panamensis gene annotations and found the following:

They are:

  1. ALAT: LPAL13_120010900 – alanine aminotransferase
  2. ASAT: LPAL13_340013000 – aspartate aminotransferase
  3. G6PD: LPAL13_000054100 – glucase-6-phosphate 1-dehydrogenase
  4. NH: LPAL13_14006100, LPAL13_180018500 – inosine-guanine nucleoside hydrolase
  5. MPI: LPAL13_320022300 (maybe) – mannose phosphate isomerase (I chose phosphomannose isomerase)

Given these 6 gene IDs (NH has two gene IDs associated with it), I can do some looking for specific differences among the various samples.

7.3.1 Expression levels of zymodeme genes

The following creates a colorspace (red to green) heatmap showing the observed expression of these genes in every sample.

my_genes <- c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
              "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300",
              "other")
my_names <- c("ALAT", "ASAT", "G6PD", "NHv1", "NHv2", "MPI", "other")

zymo_expt <- exclude_genes_expt(zy_norm, ids = my_genes, method = "keep")
## remove_genes_expt(), before removal, there were 8558 genes, now there are 6.
## There are 83 samples which kept less than 90 percent counts.
## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##   0.13101   0.12475   0.13212   0.10576   0.12993   0.10996   0.11280   0.11629 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##   0.11537   0.11795   0.11463   0.11346   0.10972   0.10586   0.11013   0.12054 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##   0.12046   0.10627   0.10885   0.11452   0.12234   0.11244   0.11003   0.10613 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##   0.13059   0.11239   0.12013   0.11614   0.11254   0.13841   0.10009   0.12004 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##   0.12250   0.13474   0.12661   0.12320   0.13405   0.11729   0.13142   0.11794 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##   0.14322   0.11008   0.10298   0.12927   0.10836   0.12188   0.12063   0.11661 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##   0.11807   0.11032   0.11372   0.11096   0.12820   0.11525   0.13945   0.12844 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##   0.13858   0.11529   0.11351   0.12376   0.12761   0.12192   0.13680   0.11539 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##   0.11167   0.13379   0.12203   0.12670   0.11626   0.11655   0.11884   0.11886 
## TMRC20092 TMRC20082 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 
##   0.11555   0.10870   0.12198   0.11055   0.12326   0.11716   0.10977   0.09639 
## TMRC20081 TMRC20106 TMRC20095 
##   0.10449   0.09802   0.07963
zymo_heatmap <- plot_sample_heatmap(zymo_expt, row_label = my_names)
zymo_heatmap

7.4 Empirically observed Zymodeme genes from differential expression analysis

In contrast, the following plots take the set of genes which are shared among all differential expression methods (|lfc| >= 1.0 and adjp <= 0.05) and use them to make categories of genes which are increased in 2.3 or 2.2.

shared_zymo <- intersect_significant(zy_table_sva)
## Deleting the file excel/intersect_significant.xlsx before writing the tables.
up_shared <- shared_zymo[["ups"]][[1]][["data"]][["all"]]
rownames(up_shared)
##  [1] "LPAL13_000033300" "LPAL13_000012000" "LPAL13_000038500" "LPAL13_000012100"
##  [5] "LPAL13_310031300" "LPAL13_000038400" "LPAL13_050005000" "LPAL13_340039600"
##  [9] "LPAL13_310031000" "LPAL13_350063000" "LPAL13_310035500" "LPAL13_310039200"
## [13] "LPAL13_140019300" "LPAL13_180013900" "LPAL13_210015500" "LPAL13_340039700"
## [17] "LPAL13_170015400" "LPAL13_350013200" "LPAL13_250006300" "LPAL13_330024000"
## [21] "LPAL13_350073400" "LPAL13_140019100" "LPAL13_350012400" "LPAL13_210005000"
## [25] "LPAL13_320038700" "LPAL13_140019200" "LPAL13_240009700" "LPAL13_000052700"
## [29] "LPAL13_160014500" "LPAL13_230011200" "LPAL13_110007300" "LPAL13_330021800"
## [33] "LPAL13_250025700" "LPAL13_350073200" "LPAL13_040007800" "LPAL13_050009600"
## [37] "LPAL13_160014100" "LPAL13_230011500" "LPAL13_230011400" "LPAL13_310032500"
## [41] "LPAL13_020006700" "LPAL13_230011300" "LPAL13_310028500" "LPAL13_110015700"
## [45] "LPAL13_140015200"
upshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(up_shared), method = "keep")
## remove_genes_expt(), before removal, there were 8558 genes, now there are 45.
## There are 83 samples which kept less than 90 percent counts.
## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.3820    0.4541    0.1296    0.4152    0.1755    0.4444    0.5717    0.3505 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.4157    0.1778    0.4587    0.1596    0.4407    0.3439    0.1740    0.1533 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.3926    0.1854    0.1847    0.3676    0.1472    0.4299    0.1400    0.3947 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.1466    0.1622    0.2068    0.1817    0.1675    0.1413    0.1313    0.1621 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##    0.5195    0.1947    0.5633    0.5266    0.2058    0.4444    0.1630    0.6361 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##    0.1894    0.3108    0.3467    0.1652    0.1457    0.1431    0.1875    0.1627 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##    0.1889    0.4666    0.4353    0.3627    0.6329    0.1664    0.1810    0.6454 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##    0.1651    0.4781    0.4394    0.1535    0.5741    0.4565    0.1786    0.3500 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##    0.5010    0.1823    0.5202    0.1419    0.4296    0.1681    0.1501    0.1520 
## TMRC20092 TMRC20082 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 
##    0.1528    0.4339    0.5033    0.4915    0.1464    0.5274    0.1244    0.3250 
## TMRC20081 TMRC20106 TMRC20095 
##    0.1310    0.1474    0.3672

We can plot a quick heatmap to get a sense of the differences observed between the genes which are different between the two zymodemes.

7.4.1 Heatmap of zymodeme gene expression increased in 2.3 vs. 2.2

high_23_heatmap <- plot_sample_heatmap(upshared_expt, row_label = rownames(up_shared))
high_23_heatmap

7.4.2 Heatmap of zymodeme gene expression increased in 2.2 vs. 2.3

down_shared <- shared_zymo[["downs"]][[1]][["data"]][["all"]]
downshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(down_shared), method = "keep")
## remove_genes_expt(), before removal, there were 8558 genes, now there are 72.
## There are 83 samples which kept less than 90 percent counts.
## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.2722    0.2195    0.7385    0.2646    0.7297    0.2471    0.2381    0.2732 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.2400    0.7427    0.2245    0.7219    0.2075    0.2425    0.6361    0.6178 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.2076    0.7378    0.7128    0.2025    0.7281    0.2203    0.7615    0.1944 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.7718    0.8085    0.7476    0.7725    0.8176    0.7821    0.6668    0.6793 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##    0.2279    0.7993    0.2225    0.2098    0.6046    0.2204    0.6085    0.2555 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##    0.5997    0.1700    0.1866    0.6912    0.8300    0.6289    0.7540    0.7041 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##    0.6669    0.2101    0.2318    0.2134    0.2260    0.6841    0.7789    0.2244 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##    0.6208    0.1874    0.2059    0.6972    0.2434    0.1994    0.6962    0.1795 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##    0.2052    0.6935    0.2150    0.7554    0.2251    0.8318    0.6096    0.7053 
## TMRC20092 TMRC20082 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 
##    0.6574    0.2108    0.2167    0.1884    0.6159    0.2361    0.8149    0.1680 
## TMRC20081 TMRC20106 TMRC20095 
##    0.7189    1.0601    0.2706
high_22_heatmap <- plot_sample_heatmap(downshared_expt, row_label = rownames(down_shared))
high_22_heatmap

8 Combine macrophage infection with these promastigote samples

A recent suggestion included a query about the relationship of our amastigote TMRC2 samples which were the result of infecting a set of macrophages vs. these promastigote samples.

So far, we have kept these two experiments separate, now let us merge them.

macrophage_sheet <- "sample_sheets/tmrc2_macrophage_samples_202203.xlsx"
tmrc2_macrophage <- create_expt(macrophage_sheet,
                                file_column="lpanamensisv36hisatfile",
                                gene_info=all_lp_annot,
                                annotation="org.Lpanamensis.MHOMCOL81L13.v46.eg.db") %>%
  set_expt_conditions(fact="macrophagezymodeme") %>%
  set_expt_batches(fact="macrophagetreatment")
## Reading the sample metadata.
## The sample definitions comprises: 28 rows(samples) and 68 columns(metadata fields).
## Warning in create_expt(macrophage_sheet, file_column =
## "lpanamensisv36hisatfile", : Some samples were removed when cross referencing
## the samples against the count data.
## Matched 8778 annotations and counts.
## Bringing together the count matrix and gene information.
## Some annotations were lost in merging, setting them to 'undefined'.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 8778 features and 22 samples.
tmrc2_macrophage_norm <- normalize_expt(tmrc2_macrophage, transform="log2", convert="cpm", norm="quant", filter=TRUE)
## Removing 0 low-count genes (8778 remaining).
## transform_counts: Found 1678 values equal to 0, adding 1 to the matrix.
all_tmrc2 <- combine_expts(lp_expt, tmrc2_macrophage)

all_nosb <- all_tmrc2
pData(all_nosb)[["stage"]] <- "promastigote"
na_idx <- is.na(pData(all_nosb)[["macrophagetreatment"]])
pData(all_nosb)[na_idx, "macrophagetreatment"] <- "undefined"
all_nosb <- subset_expt(all_nosb, subset="macrophagetreatment!='inf_sb'")
## subset_expt(): There were 123, now there are 111 samples.
ama_idx <- pData(all_nosb)[["macrophagetreatment"]] == "inf"
pData(all_nosb)[ama_idx, "stage" ] <- "amastigote"

pData(all_nosb)[["batch"]] <- pData(all_nosb)[["stage"]]
all_norm <- normalize_expt(all_nosb, convert="cpm", norm="quant", transform="log2", filter=TRUE)
## Removing 130 low-count genes (8648 remaining).
## transform_counts: Found 22 values equal to 0, adding 1 to the matrix.
plot_pca(all_norm)$plot
## plot labels was not set and there are more than 100 samples, disabling it.

I think the above picture is sort of the opposite of what we want to compare in a DE analysis for this set of data, e.g. we want to compare promastigotes from amastigotes?

all_nosb <- set_expt_batches(all_nosb, fact="condition") %>%
  set_expt_conditions(fact="stage")
pro_ama <- all_pairwise(all_nosb, filter=TRUE, model_batch="svaseq")
## Removing 0 low-count genes (8648 remaining).
## Setting 3898 low elements to zero.
## transform_counts: Found 3898 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
pro_ama_table <- combine_de_tables(pro_ama, excel="excel/tmrc2_pro_vs_ama.xlsx")
## Deleting the file excel/tmrc2_pro_vs_ama.xlsx before writing the tables.

9 SNP profiles

Over the last couple of weeks, I redid all the variant searches with a newer, (I think) more sensitive and more specific variant tool. In addition I changed my script which interprets the results so that it is able to extract any tags from it, instead of just the one or two that my previous script handled. In addition, at least in theory it is now able to provide the set of amino acid substitutions for every gene in species without or with introns (not really relevant for Leishmania panamensis).

However, as of this writing, I have not re-performed the same tasks with the 2016 data, primarily because it will require remapping all of the samples. As a result, for the moment I cannot combine the older and newer samples. Thus, any of the following blocks which use the 2016 data are currently disabled.

old_expt <- create_expt("sample_sheets/tmrc2_samples_20191203.xlsx",
                        file_column = "tophat2file")
## Reading the sample metadata.
## Dropped 13 rows from the sample metadata because the sample ID is blank.
## The sample definitions comprises: 50 rows(samples) and 38 columns(metadata fields).
## Warning in create_expt("sample_sheets/tmrc2_samples_20191203.xlsx", file_column
## = "tophat2file"): Some samples were removed when cross referencing the samples
## against the count data.
## Matched 8841 annotations and counts.
## Bringing together the count matrix and gene information.
## Saving the expressionset to 'expt.rda'.
## The final expressionset has 8841 features and 33 samples.
##tt <- lp_expt[["expressionset"]]
##rownames(tt) <- gsub(pattern = "^exon_", replacement = "", x = rownames(tt))
##rownames(tt) <- gsub(pattern = "\\.E1$", replacement = "", x = rownames(tt))
##lp_expt$expressionset <- tt

tt <- old_expt$expressionset
rownames(tt) <- gsub(pattern = "^exon_", replacement = "", x = rownames(tt))
rownames(tt) <- gsub(pattern = "\\.1$", replacement = "", x = rownames(tt))
old_expt$expressionset <- tt
rm(tt)

9.1 Create the SNP expressionset

One other important caveat, we have a group of new samples which have not yet run through the variant search pipeline, so I need to remove them from consideration. Though it looks like they finished overnight…

## The next line drops the samples which are missing the SNP pipeline.
lp_snp <- subset_expt(lp_expt, subset="!is.na(pData(lp_expt)[['freebayessummary']])")
## subset_expt(): There were 101, now there are 101 samples.
new_snps <- count_expt_snps(lp_snp, annot_column = "freebayessummary", snp_column="PAIRED")
## New names:
## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
## New names:
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## • `DP` -> `DP...3`
## • `RO` -> `RO...8`
## • `AO` -> `AO...9`
## • `QR` -> `QR...12`
## • `QA` -> `QA...13`
## • `DP` -> `DP...42`
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## • `DP` -> `DP...42`
## • `RO` -> `RO...43`
## • `QR` -> `QR...44`
## • `AO` -> `AO...45`
## • `QA` -> `QA...46`
old_snps <- count_expt_snps(old_expt, annot_column = "bcftable", snp_column = 2)
## The rownames are missing the chromosome identifier,
## they probably came from an older version of this method.
nonzero_snps <- exprs(new_snps) != 0
colSums(nonzero_snps)
## tmrc20001 tmrc20065 tmrc20005 tmrc20007 tmrc20008 tmrc20027 tmrc20028 tmrc20032 
##         0     93649         0         0         0    351343    338580    146302 
## tmrc20040 tmrc20066 tmrc20039 tmrc20037 tmrc20038 tmrc20067 tmrc20068 tmrc20041 
##     58753     93615     25115     98958     97676     93954     96583     53184 
## tmrc20015 tmrc20009 tmrc20010 tmrc20016 tmrc20011 tmrc20012 tmrc20013 tmrc20017 
##     96398     15890     93816    146124     13914       456     94766     48288 
## tmrc20014 tmrc20018 tmrc20019 tmrc20070 tmrc20020 tmrc20021 tmrc20022 tmrc20025 
##     17245    140438     14829     97336     15484    101127     18143    364240 
## tmrc20024 tmrc20036 tmrc20069 tmrc20033 tmrc20026 tmrc20031 tmrc20076 tmrc20073 
##     18471     60087     18792     33663     15074     19139     18385     96169 
## tmrc20055 tmrc20079 tmrc20071 tmrc20078 tmrc20094 tmrc20042 tmrc20058 tmrc20072 
##     22246     96224     94353     18836     87878     19734     94524     50292 
## tmrc20059 tmrc20048 tmrc20057 tmrc20088 tmrc20056 tmrc20060 tmrc20077 tmrc20074 
##     94091     97164     48944     15594     22683     21506     18773     22132 
## tmrc20063 tmrc20053 tmrc20052 tmrc20064 tmrc20075 tmrc20051 tmrc20050 tmrc20049 
##     28254     20181    100709     93173     97982     94125     17200     16168 
## tmrc20062 tmrc20110 tmrc20080 tmrc20043 tmrc20083 tmrc20054 tmrc20085 tmrc20046 
##     93677     16997     96528     95623     21167     93603     89765     48608 
## tmrc20093 tmrc20089 tmrc20047 tmrc20090 tmrc20044 tmrc20045 tmrc20061 tmrc20105 
##     48254     90421     92637     91564     14861     50403    116906     86758 
## tmrc20108 tmrc20109 tmrc20098 tmrc20096 tmrc20097 tmrc20101 tmrc20092 tmrc20082 
##     97005     17932     92927     17534     46863     17753     16578    108121 
## tmrc20102 tmrc20099 tmrc20100 tmrc20091 tmrc20084 tmrc20087 tmrc20103 tmrc20104 
##     92380     91383     94381     15059     46548     14947     49368     94237 
## tmrc20086 tmrc20107 tmrc20081 tmrc20106 tmrc20095 
##     15813     95370     19533     18830     81200
## My old_snps is using an older annotation incorrectly, so fix it here:
Biobase::annotation(old_snps$expressionset) <- Biobase::annotation(new_snps$expressionset)
both_snps <- combine_expts(new_snps, old_snps)
both_norm <- normalize_expt(both_snps, transform = "log2", norm = "quant")
## transform_counts: Found 207502544 values equal to 0, adding 1 to the matrix.
## strains <- both_norm[["design"]][["strain"]]
both_strain <- set_expt_conditions(both_norm, fact = "strain")

The data structure ‘both_norm’ now contains our 2016 data along with the newer data collected since 2019.

9.2 Plot of SNP profiles for zymodemes

The following plot shows the SNP profiles of all samples (old and new) where the colors at the top show either the 2.2 strains (orange), 2.3 strains (green), the previous samples (purple), or the various lab strains (pink etc).

new_variant_heatmap <- plot_disheat(new_snps)
dev <- pp(file = "images/raw_snp_disheat.png", height=12, width=12)
new_variant_heatmap$plot
closed <- dev.off()
new_variant_heatmap$plot

The function get_snp_sets() takes the provided metadata factor (in this case ‘condition’) and looks for variants which are exclusive to each element in it. In this case, this is looking for differences between 2.2 and 2.3, as well as the set shared among them.

snp_sets <- get_snp_sets(both_snps, factor = "condition")
Biobase::annotation(old_expt$expressionset) = Biobase::annotation(lp_expt$expressionset)
both_expt <- combine_expts(lp_expt, old_expt)

snp_genes <- sm(snps_vs_genes(both_expt, snp_sets, expt_name_col = "chromosome"))
## I think we have some metrics here we can plot...
snp_subset <- snp_subset_genes(
  both_expt, both_snps,
  genes = c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
            "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300"))
zymo_heat <- plot_sample_heatmap(snp_subset, row_label = rownames(exprs(snp_subset)))
zymo_heat

9.3 Compare variants to DE genes

Najib has asked a few times about the relationship between variants and DE genes. In subsequent conversations I figured out what he really wants to learn is variants in the UTR (most likely 5’) which might affect expression of genes. The following explicitly does not help this question, but is a paralog: is there a relationship between variants in the CDS and differential expression?

vars_df <- data.frame(ID = names(snp_genes$summary_by_gene), variants = as.numeric(snp_genes$summary_by_gene))
## Error in data.frame(ID = names(snp_genes$summary_by_gene), variants = as.numeric(snp_genes$summary_by_gene)): object 'snp_genes' not found
vars_df[["variants"]] <- log2(vars_df[["variants"]] + 1)
## Error in eval(expr, envir, enclos): object 'vars_df' not found
vars_by_de_gene <- merge(zy_df, vars_df, by.x="row.names", by.y="ID")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'y' in selecting a method for function 'merge': object 'vars_df' not found
cor.test(vars_by_de_gene$deseq_logfc, vars_by_de_gene$variants)
## Error in cor.test(vars_by_de_gene$deseq_logfc, vars_by_de_gene$variants): object 'vars_by_de_gene' not found
variants_wrt_logfc <- plot_linear_scatter(vars_by_de_gene[, c("deseq_logfc", "variants")])
## Error in data.frame(df[, c(1, 2)]): object 'vars_by_de_gene' not found
variants_wrt_logfc$scatter
## Error in eval(expr, envir, enclos): object 'variants_wrt_logfc' not found
## It looks like there might be some genes of interest, even though this is not actually
## the question of interest.

Didn’t I create a set of densities by chromosome? Oh I think they come in from get_snp_sets()

9.4 SNPS associated with clinical response in the TMRC samples

clinical_sets <- get_snp_sets(new_snps, factor = "clinicalresponse")
## The factor cure has 38 rows.
## The factor failure has 38 rows.
## The factor laboratory line has only 1 row.
## The factor laboratory line miltefosine resistant has only 1 row.
## The factor nd has 19 rows.
## The factor reference strain has 4 rows.
density_vec <- clinical_sets[["density"]]
chromosome_idx <- grep(pattern = "LpaL", x = names(density_vec))
density_df <- as.data.frame(density_vec[chromosome_idx])
density_df[["chr"]] <- rownames(density_df)
colnames(density_df) <- c("density_vec", "chr")
ggplot(density_df, aes_string(x = "chr", y = "density_vec")) +
  ggplot2::geom_col() +
  ggplot2::theme(axis.text = ggplot2::element_text(size = 10, colour = "black"),
                 axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5))

## clinical_written <- write_variants(new_snps)

9.4.1 Cross reference these variants by gene

clinical_genes <- snps_vs_genes(lp_expt, clinical_sets, expt_name_col = "chromosome")

snp_density <- merge(as.data.frame(clinical_genes[["summary_by_gene"]]),
                     as.data.frame(fData(lp_expt)),
                     by = "row.names")
snp_density <- snp_density[, c(1, 2, 4, 15)]
colnames(snp_density) <- c("name", "snps", "product", "length")
snp_density[["product"]] <- tolower(snp_density[["product"]])
snp_density[["length"]] <- as.numeric(snp_density[["length"]])
snp_density[["density"]] <- snp_density[["snps"]] / snp_density[["length"]]
snp_idx <- order(snp_density[["density"]], decreasing = TRUE)
snp_density <- snp_density[snp_idx, ]

removers <- c("amastin", "gp63", "leishmanolysin")
for (r in removers) {
  drop_idx <- grepl(pattern = r, x = snp_density[["product"]])
  snp_density <- snp_density[!drop_idx, ]
}
## Filter these for [A|a]mastin gp63 Leishmanolysin
clinical_snps <- snps_intersections(lp_expt, clinical_sets, chr_column = "chromosome")

fail_ref_snps <- as.data.frame(clinical_snps[["inters"]][["failure, reference strain"]])
fail_ref_snps <- rbind(fail_ref_snps,
                       as.data.frame(clinical_snps[["inters"]][["failure"]]))
cure_snps <- as.data.frame(clinical_snps[["inters"]][["cure"]])

head(fail_ref_snps)
##                                       seqnames  start    end width strand
## chr_LpaL13-01_pos_110212_ref_T_alt_C LpaL13-01 110212 110213     2      +
## chr_LpaL13-01_pos_156486_ref_T_alt_C LpaL13-01 156486 156487     2      +
## chr_LpaL13-02_pos_143639_ref_T_alt_C LpaL13-02 143639 143640     2      +
## chr_LpaL13-02_pos_196792_ref_A_alt_C LpaL13-02 196792 196793     2      +
## chr_LpaL13-02_pos_197657_ref_T_alt_C LpaL13-02 197657 197658     2      +
## chr_LpaL13-02_pos_198494_ref_T_alt_C LpaL13-02 198494 198495     2      +
head(cure_snps)
##                                       seqnames  start    end width strand
## chr_LpaL13-01_pos_137363_ref_C_alt_A LpaL13-01 137363 137364     2      +
## chr_LpaL13-01_pos_140306_ref_C_alt_A LpaL13-01 140306 140307     2      +
## chr_LpaL13-01_pos_169299_ref_A_alt_G LpaL13-01 169299 169300     2      +
## chr_LpaL13-02_pos_71147_ref_G_alt_A  LpaL13-02  71147  71148     2      +
## chr_LpaL13-02_pos_76744_ref_A_alt_G  LpaL13-02  76744  76745     2      +
## chr_LpaL13-02_pos_76932_ref_G_alt_A  LpaL13-02  76932  76933     2      +
write.csv(file="csv/cure_variants.txt", x=rownames(cure_snps))
write.csv(file="csv/fail_variants.txt", x=rownames(fail_ref_snps))

annot <- fData(lp_expt)
clinical_interest <- as.data.frame(clinical_snps[["gene_summaries"]][["cure"]])
clinical_interest <- merge(clinical_interest,
                           as.data.frame(clinical_snps[["gene_summaries"]][["failure, reference strain"]]),
                           by = "row.names")
rownames(clinical_interest) <- clinical_interest[["Row.names"]]
clinical_interest[["Row.names"]] <- NULL
colnames(clinical_interest) <- c("cure_snps","fail_snps")
annot <- merge(annot, clinical_interest, by = "row.names")
rownames(annot) <- annot[["Row.names"]]
annot[["Row.names"]] <- NULL
fData(lp_expt$expressionset) <- annot

10 Zymodeme for new samples

The heatmap produced here should show the variants only for the zymodeme genes.

10.1 Hunt for snp clusters

I am thinking that if we find clusters of locations which are variant, that might provide some PCR testing possibilities.

## Drop the 2.1, 2.4, unknown, and null
pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
## subset_expt(): There were 101, now there are 83 samples.
new_sets <- get_snp_sets(pruned_snps, factor = "zymodemecategorical")
## The factor z22 has 43 rows.
## The factor z23 has 40 rows.
summary(new_sets)
##               Length Class      Mode     
## medians         3    data.frame list     
## possibilities   2    -none-     character
## intersections   3    -none-     list     
## chr_data      726    -none-     list     
## set_names       4    -none-     list     
## invert_names    4    -none-     list     
## density       726    -none-     numeric
## 1000000: 2.2
## 0100000: 2.3

summary(new_sets[["intersections"]][["10"]])
##    Length     Class      Mode 
##      3583 character character
write.csv(file="csv/variants_22.csv", x=new_sets[["intersections"]][["10"]])
summary(new_sets[["intersections"]][["01"]])
##    Length     Class      Mode 
##     81173 character character
write.csv(file="csv/variants_23.csv", x=new_sets[["intersections"]][["01"]])

Thus we see that there are 3,553 variants associated with 2.2 and 81,589 associated with 2.3.

10.1.1 A small function for searching for potential PCR primers

The following function uses the positional data to look for sequential mismatches associated with zymodeme in the hopes that there will be some regions which would provide good potential targets for a PCR-based assay.

sequential_variants <- function(snp_sets, conditions = NULL, minimum = 3, maximum_separation = 3) {
  if (is.null(conditions)) {
    conditions <- 1
  }
  intersection_sets <- snp_sets[["intersections"]]
  intersection_names <- snp_sets[["set_names"]]
  chosen_intersection <- 1
  if (is.numeric(conditions)) {
    chosen_intersection <- conditions
  } else {
    intersection_idx <- intersection_names == conditions
    chosen_intersection <- names(intersection_names)[intersection_idx]
  }

  possible_positions <- intersection_sets[[chosen_intersection]]
  position_table <- data.frame(row.names = possible_positions)
  pat <- "^chr_(.+)_pos_(.+)_ref_.*$"
  position_table[["chr"]] <- gsub(pattern = pat, replacement = "\\1", x = rownames(position_table))
  position_table[["pos"]] <- as.numeric(gsub(pattern = pat, replacement = "\\2", x = rownames(position_table)))
  position_idx <- order(position_table[, "chr"], position_table[, "pos"])
  position_table <- position_table[position_idx, ]
  position_table[["dist"]] <- 0

  last_chr <- ""
  for (r in 1:nrow(position_table)) {
    this_chr <- position_table[r, "chr"]
    if (r == 1) {
      position_table[r, "dist"] <- position_table[r, "pos"]
      last_chr <- this_chr
      next
    }
    if (this_chr == last_chr) {
      position_table[r, "dist"] <- position_table[r, "pos"] - position_table[r - 1, "pos"]
    } else {
      position_table[r, "dist"] <- position_table[r, "pos"]
    }
    last_chr <- this_chr
  }

  ## Working interactively here.

  doubles <- position_table[["dist"]] == 1
  doubles <- position_table[doubles, ]
  write.csv(doubles, "doubles.csv")

  one_away <- position_table[["dist"]] == 2
  one_away <- position_table[one_away, ]
  write.csv(one_away, "one_away.csv")

  two_away <- position_table[["dist"]] == 3
  two_away <- position_table[two_away, ]
  write.csv(two_away, "two_away.csv")

  combined <- rbind(doubles, one_away)
  combined <- rbind(combined, two_away)
  position_idx <- order(combined[, "chr"], combined[, "pos"])
  combined <- combined[position_idx, ]

  this_chr <- ""
  for (r in 1:nrow(combined)) {
    this_chr <- combined[r, "chr"]
    if (r == 1) {
      combined[r, "dist_pair"] <- combined[r, "pos"]
      last_chr <- this_chr
      next
    }
    if (this_chr == last_chr) {
      combined[r, "dist_pair"] <- combined[r, "pos"] - combined[r - 1, "pos"]
    } else {
      combined[r, "dist_pair"] <- combined[r, "pos"]
    }
    last_chr <- this_chr
  }

  dist_pair_maximum <- 1000
  dist_pair_minimum <- 200
  dist_pair_idx <- combined[["dist_pair"]] <= dist_pair_maximum &
    combined[["dist_pair"]] >= dist_pair_minimum
  remaining <- combined[dist_pair_idx, ]
  no_weak_idx <- grepl(pattern="ref_(G|C)", x=rownames(remaining))
  remaining <- remaining[no_weak_idx, ]

  print(head(table(position_table[["dist"]])))
  sequentials <- position_table[["dist"]] <= maximum_separation
  message("There are ", sum(sequentials), " candidate regions.")

  ## The following can tell me how many runs of each length occurred, that is not quite what I want.
  ## Now use run length encoding to find the set of sequential sequentials!
  rle_result <- rle(sequentials)
  rle_values <- rle_result[["values"]]
  ## The following line is equivalent to just leaving values alone:
  ## true_values <- rle_result[["values"]] == TRUE
  rle_lengths <- rle_result[["lengths"]]
  true_sequentials <- rle_lengths[rle_values]
  rle_idx <- cumsum(rle_lengths)[which(rle_values)]

  position_table[["last_sequential"]] <- 0
  count <- 0
  for (r in rle_idx) {
    count <- count + 1
    position_table[r, "last_sequential"] <- true_sequentials[count]
  }
  message("The maximum sequential set is: ", max(position_table[["last_sequential"]]), ".")

  wanted_idx <- position_table[["last_sequential"]] >= minimum
  wanted <- position_table[wanted_idx, c("chr", "pos")]
  return(wanted)
}

zymo22_sequentials <- sequential_variants(new_sets, conditions = "z22", minimum=1, maximum_separation=2)
dim(zymo22_sequentials)
## 7 candidate regions for zymodeme 2.2 -- thus I am betting that the reference strain is a 2.2
zymo23_sequentials <- sequential_variants(new_sets, conditions = "z23",
                                          minimum = 2, maximum_separation = 2)
dim(zymo23_sequentials)
## In contrast, there are lots (587) of interesting regions for 2.3!

10.1.2 Extract a promising region from the genome

The first 4 candidate regions from my set of remaining: * Chr Pos. Distance * LpaL13-15 238433 448 * LpaL13-18 142844 613 * LpaL13-29 830342 252 * LpaL13-33 1331507 843

Lets define a couple of terms: * Third: Each of the 4 above positions. * Second: Third - Distance * End: Third + PrimerLen * Start: Second - Primerlen

In each instance, these are the last positions, so we want to grab three things:

  • The entire region from End -> Start, this way we can have a quick sanity check.
  • Start -> Second.
  • (Third -> End) <- Reverse complemented
## * LpaL13-15 238433 448
first_candidate_chr <- genome[["LpaL13_15"]]
primer_length <- 22
amplicon_length <- 448
first_candidate_third <- 238433
first_candidate_second <- first_candidate_third - amplicon_length
first_candidate_start <- first_candidate_second - primer_length
first_candidate_end <- first_candidate_third + primer_length
first_candidate_region <- subseq(first_candidate_chr, first_candidate_start, first_candidate_end)
first_candidate_region
first_candidate_5p <- subseq(first_candidate_chr, first_candidate_start, first_candidate_second)
as.character(first_candidate_5p)
first_candidate_3p <- spgs::reverseComplement(subseq(first_candidate_chr, first_candidate_third, first_candidate_end))
first_candidate_3p

## * LpaL13-18 142844 613
second_candidate_chr <- genome[["LpaL13_18"]]
primer_length <- 22
amplicon_length <- 613
second_candidate_third <- 142844
second_candidate_second <- second_candidate_third - amplicon_length
second_candidate_start <- second_candidate_second - primer_length
second_candidate_end <- second_candidate_third + primer_length
second_candidate_region <- subseq(second_candidate_chr, second_candidate_start, second_candidate_end)
second_candidate_region
second_candidate_5p <- subseq(second_candidate_chr, second_candidate_start, second_candidate_second)
as.character(second_candidate_5p)
second_candidate_3p <- spgs::reverseComplement(subseq(second_candidate_chr, second_candidate_third, second_candidate_end))
second_candidate_3p


## * LpaL13-29 830342 252
third_candidate_chr <- genome[["LpaL13_29"]]
primer_length <- 22
amplicon_length <- 252
third_candidate_third <- 830342
third_candidate_second <- third_candidate_third - amplicon_length
third_candidate_start <- third_candidate_second - primer_length
third_candidate_end <- third_candidate_third + primer_length
third_candidate_region <- subseq(third_candidate_chr, third_candidate_start, third_candidate_end)
third_candidate_region
third_candidate_5p <- subseq(third_candidate_chr, third_candidate_start, third_candidate_second)
as.character(third_candidate_5p)
third_candidate_3p <- spgs::reverseComplement(subseq(third_candidate_chr, third_candidate_third, third_candidate_end))
third_candidate_3p
## You are a garbage polypyrimidine tract.
## Which is actually interesting if the mutations mess it up.


## * LpaL13-33 1331507 843
fourth_candidate_chr <- genome[["LpaL13_33"]]
primer_length <- 22
amplicon_length <- 843
fourth_candidate_third <- 1331507
fourth_candidate_second <- fourth_candidate_third - amplicon_length
fourth_candidate_start <- fourth_candidate_second - primer_length
fourth_candidate_end <- fourth_candidate_third + primer_length
fourth_candidate_region <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_end)
fourth_candidate_region
fourth_candidate_5p <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_second)
as.character(fourth_candidate_5p)
fourth_candidate_3p <- spgs::reverseComplement(subseq(fourth_candidate_chr, fourth_candidate_third, fourth_candidate_end))
fourth_candidate_3p

10.2 Go hunting for Sanger sequencing regions

I made a fun little function which should find regions which have lots of variants associated with a given experimental factor.

pheno <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
## subset_expt(): There were 101, now there are 83 samples.
pheno <- subset_expt(pheno, subset = "!is.na(pData(pheno)[['bcftable']])")
## subset_expt(): There were 83, now there are 55 samples.
pheno_snps <- sm(count_expt_snps(pheno, annot_column = "bcftable"))

fun_stuff <- snp_density_primers(
    pheno_snps,
    bsgenome = "BSGenome.Leishmania.panamensis.MHOMCOL81L13.v53",
    gff = "reference/TriTrypDB-53_LpanamensisMHOMCOL81L13.gff")
## Trying attempt: rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo = TRUE)
## Had a successful gff import with rtracklayer::import.gff3(gff, sequenceRegionsAsSeqinfo = TRUE)
## Returning a df with 16 columns and 35190 rows.
drop_scaffolds <- grepl(x = rownames(fun_stuff$favorites), pattern = "SCAF")
favorite_primer_regions <- fun_stuff[["favorites"]][!drop_scaffolds, ]
favorite_primer_regions[["bin"]] <- rownames(favorite_primer_regions)
library(dplyr)
## 
## Attaching package: 'dplyr'
## The following objects are masked from 'package:Biostrings':
## 
##     collapse, intersect, setdiff, setequal, union
## The following object is masked from 'package:XVector':
## 
##     slice
## The following object is masked from 'package:AnnotationDbi':
## 
##     select
## The following object is masked from 'package:hpgltools':
## 
##     combine
## The following object is masked from 'package:testthat':
## 
##     matches
## The following objects are masked from 'package:GenomicRanges':
## 
##     intersect, setdiff, union
## The following object is masked from 'package:GenomeInfoDb':
## 
##     intersect
## The following objects are masked from 'package:IRanges':
## 
##     collapse, desc, intersect, setdiff, slice, union
## The following objects are masked from 'package:S4Vectors':
## 
##     first, intersect, rename, setdiff, setequal, union
## The following object is masked from 'package:matrixStats':
## 
##     count
## The following object is masked from 'package:Biobase':
## 
##     combine
## The following objects are masked from 'package:BiocGenerics':
## 
##     combine, intersect, setdiff, union
## The following objects are masked from 'package:stats':
## 
##     filter, lag
## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union
favorite_primer_regions <- favorite_primer_regions %>%
  relocate(bin)

10.3 Combine this table with 2.2/2.3 genes

Here is my note from our meeting:

Cross reference primers to DE genes of 2.2/2.3 and/or resistance/suscpetible, add a column to the primer spreadsheet with the DE genes (in retrospect I am guessing this actually means to put the logFC as a column.

One nice thing, I did a semantic removal on the lp_expt, so the set of logFC/pvalues should not have any of the offending types; thus I should be able to automagically get rid of them in the merge.

logfc <- zy_table_sva[["data"]][["z23_vs_z22"]]
logfc_columns <- logfc[, c("deseq_logfc", "deseq_adjp")]
colnames(logfc_columns) <- c("z23_logfc", "z23_adjp")
new_table <- merge(favorite_primer_regions, logfc_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names")
sus <- sus_table_sva[["data"]][["sensitive_vs_resistant"]]
sus_columns <- sus[, c("deseq_logfc", "deseq_adjp")]
colnames(sus_columns) <- c("sus_logfc", "sus_adjp")
new_table <- merge(new_table, sus_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names") %>%
  relocate(bin)
written <- write_xlsx(data=new_table,
                      excel="excel/favorite_primers_xref_zy_sus.xlsx")

10.4 Make a heatmap describing the clustering of variants

We can cross reference the variants against the zymodeme status and plot a heatmap of the results and hopefully see how they separate.

snp_genes <- sm(snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"))

clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, normq = "quant") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  set_expt_colors(clinical_colors_v2)

zymo_heat <- plot_disheat(new_zymo_norm)
dev <- pp(file = "images/onlyz22_z23_snp_heatmap.pdf", width=12, height=12)
zymo_heat[["plot"]]
closed <- dev.off()
zymo_heat[["plot"]]

10.4.1 Annotated heatmap of variants

Now let us try to make a heatmap which includes some of the annotation data.

des <- both_norm[["design"]]
undef_idx <- is.na(des[["strain"]])
des[undef_idx, "strain"] <- "unknown"

##hmcols <- colorRampPalette(c("yellow","black","darkblue"))(256)
correlations <- hpgl_cor(exprs(both_norm))
## Warning in stats::cor(df, method = method, ...): the standard deviation is zero
na_idx <- is.na(correlations)
correlations[na_idx] <- 0

zymo_missing_idx <- is.na(des[["zymodemecategorical"]])
des[["zymodemecategorical"]] <- as.character(des[["zymodemecategorical"]])
des[["clinicalcategorical"]] <- as.character(des[["clinicalcategorical"]])
des[zymo_missing_idx, "zymodemecategorical"] <- "unknown"
mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c("zymodemecategorical", "clinicalcategorical")])

unknown_clinical <- is.na(col_data[["clinicalcategorical"]])
row_data <- as.data.frame(des[, c("strain")])
colnames(col_data) <- c("zymodeme", "outcome")
col_data[unknown_clinical, "outcome"] <- "undefined"

colnames(row_data) <- c("strain")
myannot <- list(
  "Col" = list("data" = col_data),
  "Row" = list("data" = row_data))
myclust <- list("cuth" = 1.0,
                "col" = BrewerClusterCol)
mylabs <- list(
  "Row" = list("nrow" = 4),
  "Col" = list("nrow" = 4))
hmcols <- colorRampPalette(c("darkblue", "beige"))(240)
zymo_annot_heat <- annHeatmap2(
    correlations,
    dendrogram = mydendro,
    annotation = myannot,
    cluster = myclust,
    labels = mylabs,
    ## The following controls if the picture is symmetric
    scale = "none",
    col = hmcols)
## Warning in breakColors(breaks, col): more colors than classes: ignoring 26 last
## colors
dev <- pp(file = "images/dendro_heatmap.png", height = 20, width = 20)
plot(zymo_annot_heat)
closed <- dev.off()
plot(zymo_annot_heat)

Print the larger heatmap so that all the labels appear. Keep in mind that as we get more samples, this image needs to continue getting bigger.

big heatmap

xref_prop <- table(pheno_snps[["conditions"]])
pheno_snps$conditions
##  [1] "z2.3" "z2.3" "z2.2" "z2.3" "z2.2" "z2.3" "z2.3" "z2.3" "z2.3" "z2.2"
## [11] "z2.3" "z2.2" "z2.3" "z2.3" "z2.2" "z2.2" "z2.3" "z2.2" "z2.2" "z2.3"
## [21] "z2.2" "z2.3" "z2.2" "z2.3" "z2.2" "z2.2" "z2.2" "z2.2" "z2.2" "z2.2"
## [31] "z2.2" "z2.3" "z2.2" "z2.3" "z2.3" "z2.2" "z2.2" "z2.3" "z2.2" "z2.3"
## [41] "z2.3" "z2.2" "z2.2" "z2.2" "z2.2" "z2.3" "z2.3" "z2.3" "z2.2" "z2.3"
## [51] "z2.3" "z2.3" "z2.3" "z2.2" "z2.2"
idx_tbl <- exprs(pheno_snps) > 5
new_tbl <- data.frame(row.names = rownames(exprs(pheno_snps)))
for (n in names(xref_prop)) {
  new_tbl[[n]] <- 0
  idx_cols <- which(pheno_snps[["conditions"]] == n)
  prop_col <- rowSums(idx_tbl[, idx_cols]) / xref_prop[n]
  new_tbl[n] <- prop_col
}
keepers <- grepl(x = rownames(new_tbl), pattern = "LpaL13")
new_tbl <- new_tbl[keepers, ]
new_tbl[["strong22"]] <- 1.001 - new_tbl[["z2.2"]]
new_tbl[["strong23"]] <- 1.001 - new_tbl[["z2.3"]]
s22_na <- new_tbl[["strong22"]] > 1
new_tbl[s22_na, "strong22"] <- 1
s23_na <- new_tbl[["strong23"]] > 1
new_tbl[s23_na, "strong23"] <- 1

new_tbl[["SNP"]] <- rownames(new_tbl)
new_tbl[["Chromosome"]] <- gsub(x = new_tbl[["SNP"]], pattern = "chr_(.*)_pos_.*", replacement = "\\1")
new_tbl[["Position"]] <- gsub(x = new_tbl[["SNP"]], pattern = ".*_pos_(\\d+)_.*", replacement = "\\1")
new_tbl <- new_tbl[, c("SNP", "Chromosome", "Position", "strong22", "strong23")]

library(CMplot)
## Much appreciate for using CMplot.
## Full description, Bug report, Suggestion and the latest codes:
## https://github.com/YinLiLin/CMplot
simplify <- new_tbl
simplify[["strong22"]] <- NULL

CMplot(simplify, bin.size = 100000)
##  SNP-Density Plotting.
##  Circular-Manhattan Plotting strong23.
##  Rectangular-Manhattan Plotting strong23.
##  QQ Plotting strong23.
##  Plots are stored in: /mnt/cbcb/fs01_abelew/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019
CMplot(new_tbl, plot.type="m", multracks=TRUE, threshold = c(0.01, 0.05),
       threshold.lwd=c(1,1), threshold.col=c("black","grey"),
       amplify=TRUE, bin.size=10000,
       chr.den.col=c("darkgreen", "yellow", "red"),
       signal.col=c("red", "green", "blue"),
       signal.cex=1, file="jpg", memo="", dpi=300, file.output=TRUE, verbose=TRUE)
##  Multracks-Manhattan Plotting strong22.
##  Multracks-Manhattan Plotting strong23.
##  Multraits-Rectangular Plotting...(finished 73%)
 Multraits-Rectangular Plotting...(finished 74%)
 Multraits-Rectangular Plotting...(finished 75%)
 Multraits-Rectangular Plotting...(finished 76%)
 Multraits-Rectangular Plotting...(finished 77%)
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 Multraits-Rectangular Plotting...(finished 86%)
 Multraits-Rectangular Plotting...(finished 87%)
 Multraits-Rectangular Plotting...(finished 88%)
 Multraits-Rectangular Plotting...(finished 89%)
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 Multraits-Rectangular Plotting...(finished 100%)
##  Plots are stored in: /mnt/cbcb/fs01_abelew/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019

SNP Density Circular Manhattan Rectangular Manhattan QQ

10.5 Try out MatrixEQTL

This tool looks a little opaque, but provides sample data with things that make sense to me and should be pretty easy to recapitulate in our data.

  1. covariates.txt: Columns are samples, rows are things from pData – the most likely ones of interest for our data would be zymodeme, sensitivity
  2. geneloc.txt: columns are ‘geneid’, ‘chr’, ‘left’, ‘right’. I guess I can assume left and right are start/stop; in which case this is trivially acquirable from fData.
  3. ge.txt: This appears to be a log(rpkm/cpm) table with rows as genes and columns as samples
  4. snpsloc.txt: columns are ‘snpid’, ‘chr’, ‘pos’
  5. snps.txt: columns are samples, rows are the ids from snsploc, values a 0,1,2. I assume 0 is identical and 1..12 are the various A->TGC T->AGC C->AGT G->ACT
## For this, let us use the 'new_snps' data structure.
## Caveat here: these need to be coerced to numbers.
my_covariates <- pData(new_snps)[, c("zymodemecategorical", "clinicalcategorical")]
for (col in colnames(my_covariates)) {
  my_covariates[[col]] <- as.numeric(as.factor(my_covariates[[col]]))
}
my_covariates <- t(my_covariates)

my_geneloc <- fData(lp_expt)[, c("gid", "chromosome", "start", "end")]
colnames(my_geneloc) <- c("geneid", "chr", "left", "right")

my_ge <- exprs(normalize_expt(lp_expt, transform = "log2", filter = TRUE, convert = "cpm"))
used_samples <- tolower(colnames(my_ge)) %in% colnames(exprs(new_snps))
my_ge <- my_ge[, used_samples]

my_snpsloc <- data.frame(rownames = rownames(exprs(new_snps)))
## Oh, caveat here: Because of the way I stored the data,
## I could have duplicate rows which presumably will make matrixEQTL sad
my_snpsloc[["chr"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\1",
                            x = rownames(my_snpsloc))
my_snpsloc[["pos"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\2",
                            x = rownames(my_snpsloc))
test <- duplicated(my_snpsloc)
## Each duplicated row would be another variant at that position;
## so in theory we would do a rle to number them I am guessing
## However, I do not have different variants so I think I can ignore this for the moment
## but will need to make my matrix either 0 or 1.
if (sum(test) > 0) {
  message("There are: ", sum(duplicated), " duplicated entries.")
  keep_idx <- ! test
  my_snpsloc <- my_snpsloc[keep_idx, ]
}

my_snps <- exprs(new_snps)
one_idx <- my_snps > 0
my_snps[one_idx] <- 1

## Ok, at this point I think I have all the pieces which this method wants...
## Oh, no I guess not; it actually wants the data as a set of filenames...
library(MatrixEQTL)
write.table(my_snps, "eqtl/snps.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snps, "eqtl/snps.tsv", )
write.table(my_snpsloc, "eqtl/snpsloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snpsloc, "eqtl/snpsloc.tsv")
write.table(as.data.frame(my_ge), "eqtl/ge.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_ge), "eqtl/ge.tsv")
write.table(as.data.frame(my_geneloc), "eqtl/geneloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_geneloc), "eqtl/geneloc.tsv")
write.table(as.data.frame(my_covariates), "eqtl/covariates.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_covariates), "eqtl/covariates.tsv")

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

# Genotype file name
SNP_file_name = "eqtl/snps.tsv"
snps_location_file_name = "eqtl/snpsloc.tsv"
expression_file_name = "eqtl/ge.tsv"
gene_location_file_name = "eqtl/geneloc.tsv"
covariates_file_name = "eqtl/covariates.tsv"
# Output file name
output_file_name_cis = tempfile()
output_file_name_tra = tempfile()
# Only associations significant at this level will be saved
pvOutputThreshold_cis = 0.1
pvOutputThreshold_tra = 0.1
# Error covariance matrix
# Set to numeric() for identity.
errorCovariance = numeric()
# errorCovariance = read.table("Sample_Data/errorCovariance.txt");
# Distance for local gene-SNP pairs
cisDist = 1e6
## Load genotype data
snps = SlicedData$new()
snps$fileDelimiter = "\t"      # the TAB character
snps$fileOmitCharacters = "NA" # denote missing values;
snps$fileSkipRows = 1          # one row of column labels
snps$fileSkipColumns = 1       # one column of row labels
snps$fileSliceSize = 2000      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name)
## Load gene expression data
gene = SlicedData$new()
gene$fileDelimiter = "\t"      # the TAB character
gene$fileOmitCharacters = "NA" # denote missing values;
gene$fileSkipRows = 1          # one row of column labels
gene$fileSkipColumns = 1       # one column of row labels
gene$fileSliceSize = 2000      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name)
## Load covariates
cvrt = SlicedData$new()
cvrt$fileDelimiter = "\t"      # the TAB character
cvrt$fileOmitCharacters = "NA" # denote missing values;
cvrt$fileSkipRows = 1          # one row of column labels
cvrt$fileSkipColumns = 1       # one column of row labels
if(length(covariates_file_name) > 0) {
  cvrt$LoadFile(covariates_file_name)
}
## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE)
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE)

me = Matrix_eQTL_main(
    snps = snps,
    gene = gene,
    cvrt = cvrt,
    output_file_name = output_file_name_tra,
    pvOutputThreshold = pvOutputThreshold_tra,
    useModel = useModel,
    errorCovariance = errorCovariance,
    verbose = TRUE,
    output_file_name.cis = output_file_name_cis,
    pvOutputThreshold.cis = pvOutputThreshold_cis,
    snpspos = snpspos,
    genepos = genepos,
    cisDist = cisDist,
    pvalue.hist = "qqplot",
    min.pv.by.genesnp = FALSE,
    noFDRsaveMemory = FALSE);
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  message(paste0("Saving to ", savefile))
  tmp <- sm(saveme(filename = savefile))
}
## If you wish to reproduce this exact build of hpgltools, invoke the following:
## > git clone http://github.com/abelew/hpgltools.git
## > git reset 12cc39929e5afa444a4ddf2f7b7a0472713666e3
## This is hpgltools commit: Wed Jun 22 15:35:44 2022 -0400: 12cc39929e5afa444a4ddf2f7b7a0472713666e3
## Saving to tmrc2_02sample_estimation_v202206.rda.xz
tmp <- loadme(filename = savefile)
---
title: "TMRC2 Comprehensive Data Analysis: 202204"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
   collapsed: false
   smooth_scroll: false
---

<style>
  body .main-container {
    max-width: 1600px;
  }
</style>

```{r options, include = FALSE}
library(hpgltools)
tt <- devtools::load_all("~/hpgltools")
knitr::opts_knit$set(progress = TRUE,
                     verbose = TRUE,
                     width = 90,
                     echo = TRUE)
knitr::opts_chunk$set(error = TRUE,
                      fig.width = 8,
                      fig.height = 8,
                      dpi = 96)
old_options <- options(digits = 4,
                       stringsAsFactors = FALSE,
                       knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202206"
rundate <- format(Sys.Date(), format = "%Y%m%d")

## tmp <- try(sm(loadme(filename = gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = previous_file))))
rmd_file <- glue::glue("tmrc2_02sample_estimation_v{ver}.Rmd")
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)

library(Heatplus)
```

```{r current_samplesheet}
sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_202206.xlsx")
```

# Introduction

This is mostly just a run of this worksheet to reacquaint myself with it.

This document is intended to provide a general overview of the TMRC2 samples
which have thus far been sequenced.  In some cases, this includes only those
samples starting in 2019; in other instances I am including our previous
(2015-2016) samples.

In all cases the processing performed was:

1.  Default trimming was performed.
2.  Hisat2 was used to map the remaining reads against the Leishmania
    panamensis genome revision 36.
3.  The alignments from hisat2 were used to count reads/gene against the
    revision 36 annotations with htseq.
4.  These alignments were also passed to the pileup functionality of samtools
    and the vcf/bcf utilities in order to make a matrix of all observed
    differences between each sample with respect to the reference.

The analyses in this document use the matrices of counts/gene from #3 and
variants/position from #4 in order to provide some images and metrics describing
the samples we have sequenced so far.

# Annotations

Everything which follows depends on the Existing TriTrypDB annotations revision
46, circa 2019.  The following block loads a database of these annotations and
turns it into a matrix where the rows are genes and columns are all the
annotation types provided by TriTrypDB.

The same database was used to create a matrix of orthologous genes between
L.panamensis and all of the other species in the TriTrypDB.

```{r annot}
tt <- sm(library(EuPathDB))
orgdb <- "org.Lpanamensis.MHOMCOL81L13.v46.eg.db"
tt <- sm(library(orgdb, character.only=TRUE))
pan_db <- org.Lpanamensis.MHOMCOL81L13.v46.eg.db
all_fields <- columns(pan_db)

all_lp_annot <- sm(load_orgdb_annotations(
    pan_db,
    keytype = "gid",
    fields = c("annot_gene_entrez_id", "annot_gene_name",
               "annot_strand", "annot_chromosome", "annot_cds_length",
               "annot_gene_product")))$genes

lp_go <- sm(load_orgdb_go(pan_db))
lp_lengths <- all_lp_annot[, c("gid", "annot_cds_length")]
colnames(lp_lengths)  <- c("ID", "length")
all_lp_annot[["annot_gene_product"]] <- tolower(all_lp_annot[["annot_gene_product"]])
orthos <- sm(EuPathDB::extract_eupath_orthologs(db = pan_db))
```

# Load a genome

```{r genome}
meta <- sm(EuPathDB::download_eupath_metadata(webservice="tritrypdb"))
lp_entry <- EuPathDB::get_eupath_entry(species="Leishmania panamensis", metadata=meta)
colnames(lp_entry)
testing_panamensis <- "BSGenome.Leishmania.panamensis.MHOMCOL81L13.v53"
## testing_panamensis <- EuPathDB::make_eupath_bsgenome(entry=lp_entry, eu_version="v46")
library(as.character(testing_panamensis), character.only=TRUE)
genome <- get0(as.character(testing_panamensis))
```

# TODO:

Resequence samples: TMRC20002, TMRC20006, TMRC20004 (maybe TMRC20008 and TMRC20029)

# Generate Expressionsets and Sample Estimation

The process of sample estimation takes two primary inputs:

1.  The sample sheet, which contains all the metadata we currently have on hand,
    including filenames for the outputs of #3 and #4 above.
2.  The gene annotations.

An expressionset is a data structure used in R to examine RNASeq data.  It
is comprised of annotations, metadata, and expression data.  In the case of our
processing pipeline, the location of the expression data is provided by the
filenames in the metadata.

The first lines of the following block create the Expressionset.  All of the
following lines perform various normalizations and generate plots from it.

## Notes

The following samples are much lower coverage:

* TMRC20002
* TMRC20006
* TMRC20007
* TMRC20008

20210610: I made some manual changes to the sample sheet which I
downloaded, filling in some zymodeme with 'unknown'

## TODO:

1.  Do the multi-gene family removal right here instead of way down at the bottom
2.  Add zymodeme snps to the annotation later.
3.  Start phylogenetic analysis of variant table.

```{r new_samples_hisat}
clinical_colors <- list(
##     "z1.0" = "#333333", ## Changed this to 'braz' to make it easier to find them.
    "z2.0" = "#555555",
    "z3.0" = "#777777",
    "z2.1" = "#874400",
    "z2.2" = "#0000cc",
    "z2.3" = "#cc0000",
    "z2.4" = "#df7000",
    "braz" = "#cc00cc",
    "unknown" = "#cbcbcb",
    "null" = "#000000")
sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical")
lp_expt <- create_expt(sample_sheet,
                       gene_info = all_lp_annot,
                       annotation_name = orgdb,
                       id_column = "hpglidentifier",
                       file_column = "lpanamensisv36hisatfile") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  subset_expt(nonzero = 8550) %>%
  subset_expt(coverage = 5000000) %>%
  set_expt_colors(clinical_colors) %>%
  semantic_expt_filter(semantic = c("amastin", "gp63", "leishmanolysin"),
                       semantic_column = "annot_gene_product") %>%
  sanitize_expt_metadata(columns = sanitize_columns) %>%
  set_expt_factors(columns = sanitize_columns, class = "factor")

libsizes <- plot_libsize(lp_expt)
dev <- pp("images/lp_expt_libsizes.png", width = 14, height = 9)
libsizes$plot
closed <- dev.off()
libsizes$plot

## I think samples 7,10 should be removed at minimum, probably also 9,11
nonzero <- plot_nonzero(lp_expt)
dev <- pp(file = "images/lp_nonzero.png", width=9, height=9)
nonzero$plot
closed <- dev.off()

lp_box <- plot_boxplot(lp_expt)
dev <- pp(file = "images/lp_expt_boxplot.png", width = 12, height = 9)
lp_box
closed <- dev.off()
lp_box

filter_plot <- plot_libsize_prepost(lp_expt)
filter_plot$lowgene_plot
filter_plot$count_plot

table(pData(lp_expt)[["zymodemecategorical"]])
table(pData(lp_expt)[["clinicalresponse"]])
```

## Distribution Visualization

Najib's favorite plots are of course the PCA/TNSE.  These are nice to look at in
order to get a sense of the relationships between samples.  They also provide a
good opportunity to see what happens when one applies different normalizations,
surrogate analyses, filters, etc.  In addition, one may set different
experimental factors as the primary 'condition' (usually the color of plots) and
surrogate 'batches'.

## By Susceptilibity

Column 'Q' in the sample sheet, make a categorical version of it with these parameters:

* 0 <= x <= 35 is resistant
* 36 <= x <= 48 is ambiguous
* 49 <= x is sensitive

```{r susceptibility_historical}
fix_excel_percent <- function(numbers) {
  for (n in 1:length(numbers)) {
    pct <- grepl(x=numbers[n], pattern="\\%")
    new_number <- NA
    if (pct) {
      new_number <- as.numeric(gsub(x=numbers[n], pattern="\\%", replacement="")) / 100.0
    } else {
      new_number <- as.numeric(numbers[n])
    }
    numbers[n] <- new_number
  }
  return(as.numeric(numbers))
}

starting <- fix_excel_percent(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])
sus_categorical <- starting
na_idx <- is.na(starting)
sum(na_idx)
sus_categorical[na_idx] <- "unknown"

resist_idx <- starting <= 0.35
sus_categorical[resist_idx] <- "resistant"
indeterminant_idx <- starting >= 0.36 & starting <= 0.48
sus_categorical[indeterminant_idx] <- "ambiguous"
susceptible_idx <- starting >= 0.49
sus_categorical[susceptible_idx] <- "sensitive"

sus_categorical <- as.factor(sus_categorical)
pData(lp_expt)[["sus_category_historical"]] <- sus_categorical
table(sus_categorical)
```

```{r susceptibility_current}
starting_current <- fix_excel_percent(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvcurrentdata"]])
sus_categorical_current <- starting_current
na_idx <- is.na(starting_current)
sum(na_idx)
sus_categorical_current[na_idx] <- "unknown"

resist_idx <- starting_current <= 0.35
sus_categorical_current[resist_idx] <- "resistant"
indeterminant_idx <- starting_current >= 0.36 & starting_current <= 0.48
sus_categorical_current[indeterminant_idx] <- "ambiguous"
susceptible_idx <- starting_current >= 0.49
sus_categorical_current[susceptible_idx] <- "sensitive"
sus_categorical_current <- as.factor(sus_categorical_current)

pData(lp_expt)[["sus_category_current"]] <- sus_categorical_current
table(sus_categorical_current)
```

```{r pre_questions}
clinical_samples <- lp_expt %>%
  set_expt_batches(fact = sus_categorical_current) %>%
  set_expt_colors(clinical_colors)
table(pData(clinical_samples)[["condition"]])

clinical_norm <- normalize_expt(clinical_samples, norm = "quant", transform = "log2",
                                   convert = "cpm", filter = TRUE)
zymo_pca <- plot_pca(clinical_norm, plot_title = "PCA of parasite expression values",
                     plot_labels = FALSE)
ggplt(zymo_pca$plot)
dev <- pp(file = "images/zymo_pca_sus_shape.png")
zymo_pca$plot
closed <- dev.off()
zymo_pca$plot

only_two_types <- subset_expt(clinical_samples, subset = "condition=='z2.3'|condition=='z2.2'")
only_two_norm <- sm(normalize_expt(only_two_types, norm = "quant", transform = "log2",
                                   convert = "cpm", batch = FALSE, filter = TRUE))
onlytwo_pca <- plot_pca(only_two_norm, plot_title = "PCA of z2.2 and z2.3 parasite expression values",
                     plot_labels = FALSE)
dev <- pp(file = "images/zymo_z2.2_z2.3_pca_sus_shape.pdf")
onlytwo_pca$plot
closed <- dev.off()
onlytwo_pca$plot

zymo_3dpca <- plot_3d_pca(zymo_pca)
zymo_3dpca$plot

clinical_n <- sm(normalize_expt(clinical_samples, transform = "log2",
                                convert = "cpm", batch = FALSE, filter = TRUE))
zymo_tsne <- plot_tsne(clinical_n, plot_title = "TSNE of parasite expression values")
zymo_tsne$plot

clinical_nb <- normalize_expt(clinical_samples, convert = "cpm", transform = "log2",
                         filter = TRUE, batch = "svaseq")
clinical_nb_pca <- plot_pca(clinical_nb, plot_title = "PCA of parasite expression values",
                            plot_labels = FALSE)
dev <- pp(file = "images/clinical_nb_pca_sus_shape.png")
clinical_nb_pca$plot
closed <- dev.off()
clinical_nb_pca$plot

clinical_nb_tsne <- plot_tsne(clinical_nb, plot_title = "TSNE of parasite expression values")
clinical_nb_tsne$plot

corheat <- plot_corheat(clinical_norm, plot_title = "Correlation heatmap of parasite
                 expression values
")
corheat$plot

plot_sm(clinical_norm)$plot
```

## By Cure/Fail status

```{r cf_status}
cf_colors <- list(
    "cure" = "#006f00",
    "fail" = "#9dffa0",
    "unknown" = "#cbcbcb",
    "notapplicable" = "#000000")
cf_expt <- set_expt_conditions(lp_expt, fact = "clinicalcategorical") %>%
  set_expt_batches(fact = sus_categorical_current) %>%
  set_expt_colors(cf_colors)
table(pData(cf_expt)[["condition"]])

cf_norm <- normalize_expt(cf_expt, convert = "cpm", transform = "log2",
                          norm = "quant", filter = TRUE)
start_cf <- plot_pca(cf_norm, plot_title = "PCA of parasite expression values",
                     plot_labels = FALSE)
dev <- pp(file = "images/cf_sus_shape.png")
start_cf$plot
closed <- dev.off()
start_cf$plot

cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
cf_nb <- normalize_expt(cf_nb_input, convert = "cpm", transform = "log2",
                        filter = TRUE, batch = "svaseq")
cf_nb_pca <- plot_pca(cf_nb, plot_title = "PCA of parasite expression values",
                      plot_labels = FALSE)
dev <- pp(file = "images/cf_sus_share_nb.png")
cf_nb_pca$plot
closed <- dev.off()
cf_nb_pca$plot

cf_norm <- normalize_expt(cf_expt, transform = "log2", convert = "cpm",
                          filter = TRUE, norm = "quant")
test <- pca_information(cf_norm,
                        expt_factors = c("clinicalcategorical", "zymodemecategorical",
                                         "pathogenstrain", "passagenumber"),
                        num_components = 6, plot_pcas = TRUE)
test$anova_p
test$cor_heatmap
```

```{r susceptibility_pca}
sus_colors <- list(
    "resistant" = "#8563a7",
    "sensitive" = "#8d0000",
    "ambiguous" = "#cbcbcb",
    "unknown" = "#555555")
sus_expt <- set_expt_conditions(lp_expt, fact = "sus_category_current") %>%
  set_expt_batches(fact = "clinicalcategorical") %>%
  set_expt_colors(colors = sus_colors)

##  subset_expt(subset = "batch!='z24'") %>%
##  subset_expt(subset = "batch!='z21'")

sus_norm <- normalize_expt(sus_expt, transform = "log2", convert = "cpm",
                           norm = "quant", filter = TRUE)
sus_pca <- plot_pca(sus_norm, plot_title = "PCA of parasite expression values",
                    plot_labels = FALSE)
dev <- pp(file = "images/sus_norm_pca.png")
sus_pca[["plot"]]
closed <- dev.off()
sus_pca[["plot"]]

sus_nb <- normalize_expt(sus_expt, transform = "log2", convert = "cpm",
                         batch = "svaseq", filter = TRUE)
sus_nb_pca <- plot_pca(sus_nb, plot_title = "PCA of parasite expression values",
                       plot_labels = FALSE)
dev <- pp(file = "images/sus_nb_pca.png")
sus_nb_pca[["plot"]]
closed <- dev.off()
sus_nb_pca[["plot"]]
```

# Zymodeme analyses

The following sections perform a series of analyses which seek to elucidate
differences between the zymodemes 2.2 and 2.3 either through differential
expression or variant profiles.

## Differential expression

### With respect to zymodeme attribution

TODO: Do this with and without sva and compare the results.

```{r zymo_de, fig.show = "hide"}
zy_expt <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
zy_norm <- normalize_expt(zy_expt, filter = TRUE, convert = "cpm", norm = "quant")

zy_de_nobatch <- all_pairwise(zy_expt, filter = TRUE, model_batch = FALSE)
zy_table_nobatch <- combine_de_tables(
    zy_de_nobatch,
    excel = glue::glue("excel/zy_tables_nobatch-v{ver}.xlsx"))

zy_sig_nobatch <- extract_significant_genes(
    zy_table_nobatch,
    according_to = "deseq", current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_nobatch_deseq-v{ver}.xlsx"))
## There is an error lurking in extract_significant_genes()
## in which it incorrectly returns genes when not explicitly setting the 'according_to' parameter.
zy_sig_test <- extract_significant_genes(
    zy_table_nobatch,
    current_id = "GID", required_id = "GID",
    gmt = "gmt/zymodeme_test.gmt",
    excel = "excel/zy_sig_nobatch_test.xlsx")
first_test <- zy_sig_nobatch[["deseq"]][["ups"]][[1]]
second_test <- zy_sig_test[["deseq"]][["ups"]][[1]]
## I think I fixed it!
expect_equal(first_test, second_test)

zy_sig_nobatch_all <- extract_significant_genes(
    zy_table_nobatch,
    current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_nobatch_all-v{ver}.xlsx"))

zy_de_sva <- all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq")
zy_table_sva <- combine_de_tables(
    zy_de_sva, excel = glue::glue("excel/zy_tables_sva-v{ver}.xlsx"))
zy_sig_sva <- extract_significant_genes(
    zy_table_sva,
    according_to = "deseq",
    current_id = "GID", required_id = "GID",
    gmt = glue::glue("gmt/zymodeme_sva-v{ver}.gmt"),
    excel = glue::glue("excel/zy_sig_sva-v{ver}.xlsx"))
```

### Images of zymodeme DE

```{r zymod_de_pictures}
dev <- pp(file = "images/zymo_ma.png")
zy_table_sva[["plots"]][["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]]
closed <- dev.off()
zy_table_sva[["plots"]][["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]]
```

## With respect to cure/failure

In contrast, we can search for genes which are differentially
expressed with respect to cure/failure status.

```{r curefail_de, fig.show = "hide"}
##cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
cf_de <- all_pairwise(cf_nb_input, filter = TRUE, model_batch = "svaseq")
cf_table <- combine_de_tables(cf_de, excel = glue::glue("excel/cf_tables-v{ver}.xlsx"))
cf_sig <- extract_significant_genes(cf_table, excel = glue::glue("excel/cf_sig-v{ver}.xlsx"))

dev <- pp(file = "images/cf_ma.png")
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
closed <- dev.off()
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]
```

## With respect to susceptibility

Finally, we can use our category of susceptibility and look for genes
which change from sensitive to resistant.  Keep in mind, though, that
for the moment we have a lot of ambiguous and unknown strains.

```{r curefail_de02, fig.show = "hide"}
sus_de_sva <- all_pairwise(sus_expt, filter = TRUE, model_batch = "svaseq")
sus_table_sva <- combine_de_tables(
    sus_de_sva,
    excel = glue::glue("excel/sus_tables_sva-v{ver}.xlsx"))
sus_sig_sva <- extract_significant_genes(
    sus_table_sva, according_to = "deseq",
    excel = glue::glue("excel/sus_sig_sva-v{ver}.xlsx"))

sus_de_nobatch <- all_pairwise(sus_expt, filter = TRUE, model_batch = FALSE)
sus_table_nobatch <- combine_de_tables(
    sus_de_nobatch,
    excel = glue::glue("excel/sus_tables_nobatch-v{ver}.xlsx"))
sus_sig_nobatch <- extract_significant_genes(
    sus_table_nobatch, according_to = "deseq",
    excel = glue::glue("excel/sus_sig_nobatch-v{ver}.xlsx"))
```

# Compare Susceptibility to Zymodemes

Checking on my function to do the comparison first, thus the comparison of the
nobatch vs. sva result for the susceptibility data.

Yes, the compare_de_results() function assumes that the results it compares contain
identical sets of contrasts, which is explicitly not the case for these data.  Thus
I am making a simpler function, compare_de_tables() which handles this scenario.

```{r compare_sus_zy}
sus_nobatch_sva <- compare_de_results(sus_table_nobatch, sus_table_sva)
```

## Look for agreement between sensitivity and zymodemes

Remind myself, the data structures are (zy|sus)_(de|table|sig).

```{r sensitive_vs_zymo}
zy_df <- zy_table_sva[["data"]][["z23_vs_z22"]]
sus_df <- sus_table_sva[["data"]][["sensitive_vs_resistant"]]

both_df <- merge(zy_df, sus_df, by = "row.names")
plot_df <- both_df[, c("deseq_logfc.x", "deseq_logfc.y")]
rownames(plot_df) <- both_df[["Row.names"]]
colnames(plot_df) <- c("z23_vs_z22", "sensitive_vs_resistant")

compare <- plot_linear_scatter(plot_df)
dev <- pp(file = "images/compare_sus_zy.png")
compare$scatter
closed <- dev.off()
compare$scatter
compare$cor
```


```{r zymod_de_pictures01}
knitr::kable(head(sus_sig_sva$deseq$ups$sensitive_vs_resistant, n = 20))

knitr::kable(head(sus_sig_sva$deseq$downs$sensitive_vs_resistant, n = 20))

sus_ma <- sus_table_sva[["plots"]][["sensitive_vs_resistant"]][["deseq_ma_plots"]][["plot"]]
dev <- pp(file = "images/sus_ma_sva.png")
sus_ma
closed <- dev.off()
sus_ma

## test <- ggplt(sus_ma)
```

## Ontology searches

Now let us look for ontology categories which are increased in the 2.3
samples followed by the 2.2 samples.

```{r go, sig.show = "hide"}
## Gene categories more represented in the 2.3 group.
zy_go_up <- simple_goseq(sig_genes = zy_sig_sva[["deseq"]][["ups"]][[1]],
                         go_db = lp_go, length_db = lp_lengths)

## Gene categories more represented in the 2.2 group.
zy_go_down <- simple_goseq(sig_genes = zy_sig_sva[["deseq"]][["downs"]][[1]],
                           go_db = lp_go, length_db = lp_lengths)
```

### A couple plots from the differential expression

#### Number of genes in agreement among DE methods, 2.3 more than 2.2

In the function 'combined_de_tables()' above, one of the tasks
performed is to look at the agreement among DESeq2, limma, and edgeR.
The following show a couple of these for the set of genes observed
with a fold-change >= |2| and adjusted p-value <= 0.05.

```{r de_plots}
zy_table_sva[["venns"]][[1]][["p_lfc1"]][["up_noweight"]]
```

#### Number of genes in agreement among DE methods, 2.2 more than 2.3

```{r de_plots01}
zy_table_sva[["venns"]][[1]][["p_lfc1"]][["down_noweight"]]
```

#### goseq ontology plots of groups of genes, 2.3 more than 2.2

```{r goseq_up}
zy_go_up[["pvalue_plots"]][["bpp_plot_over"]]
```

#### goseq ontology plots of groups of genes, 2.2 more than 2.3

```{r goseq_down}
zy_go_down[["pvalue_plots"]][["bpp_plot_over"]]
```

## Zymodeme enzyme gene IDs

Najib read me an email listing off the gene names associated with the zymodeme
classification.  I took those names and cross referenced them against the
Leishmania panamensis gene annotations and found the following:

They are:

1. ALAT: LPAL13_120010900 -- alanine aminotransferase
2. ASAT: LPAL13_340013000 -- aspartate aminotransferase
3. G6PD: LPAL13_000054100 -- glucase-6-phosphate 1-dehydrogenase
4. NH: LPAL13_14006100, LPAL13_180018500 -- inosine-guanine nucleoside hydrolase
5. MPI: LPAL13_320022300 (maybe) -- mannose phosphate isomerase (I chose phosphomannose isomerase)

Given these 6 gene IDs (NH has two gene IDs associated with it), I can do some
looking for specific differences among the various samples.

### Expression levels of zymodeme genes

The following creates a colorspace (red to green) heatmap showing the observed
expression of these genes in every sample.

```{r zymodemes}
my_genes <- c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
              "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300",
              "other")
my_names <- c("ALAT", "ASAT", "G6PD", "NHv1", "NHv2", "MPI", "other")

zymo_expt <- exclude_genes_expt(zy_norm, ids = my_genes, method = "keep")
zymo_heatmap <- plot_sample_heatmap(zymo_expt, row_label = my_names)
zymo_heatmap
```

## Empirically observed Zymodeme genes from differential expression analysis

In contrast, the following plots take the set of genes which are shared among
all differential expression methods (|lfc| >= 1.0 and adjp <= 0.05) and use them
to make categories of genes which are increased in 2.3 or 2.2.

```{r zymodeme_genes_empirical}
shared_zymo <- intersect_significant(zy_table_sva)
up_shared <- shared_zymo[["ups"]][[1]][["data"]][["all"]]
rownames(up_shared)
upshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(up_shared), method = "keep")
```

We can plot a quick heatmap to get a sense of the differences observed
between the genes which are different between the two zymodemes.

### Heatmap of zymodeme gene expression increased in 2.3 vs. 2.2

```{r zymoempup}
high_23_heatmap <- plot_sample_heatmap(upshared_expt, row_label = rownames(up_shared))
high_23_heatmap
```

### Heatmap of zymodeme gene expression increased in 2.2 vs. 2.3

```{r zymoemdown}
down_shared <- shared_zymo[["downs"]][[1]][["data"]][["all"]]
downshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(down_shared), method = "keep")
high_22_heatmap <- plot_sample_heatmap(downshared_expt, row_label = rownames(down_shared))
high_22_heatmap
```

# Combine macrophage infection with these promastigote samples

A recent suggestion included a query about the relationship of our
amastigote TMRC2 samples which were the result of infecting a set of
macrophages vs. these promastigote samples.

So far, we have kept these two experiments separate, now let us merge them.

```{r combine_macrophage}
macrophage_sheet <- "sample_sheets/tmrc2_macrophage_samples_202203.xlsx"
tmrc2_macrophage <- create_expt(macrophage_sheet,
                                file_column="lpanamensisv36hisatfile",
                                gene_info=all_lp_annot,
                                annotation="org.Lpanamensis.MHOMCOL81L13.v46.eg.db") %>%
  set_expt_conditions(fact="macrophagezymodeme") %>%
  set_expt_batches(fact="macrophagetreatment")

tmrc2_macrophage_norm <- normalize_expt(tmrc2_macrophage, transform="log2", convert="cpm", norm="quant", filter=TRUE)

all_tmrc2 <- combine_expts(lp_expt, tmrc2_macrophage)

all_nosb <- all_tmrc2
pData(all_nosb)[["stage"]] <- "promastigote"
na_idx <- is.na(pData(all_nosb)[["macrophagetreatment"]])
pData(all_nosb)[na_idx, "macrophagetreatment"] <- "undefined"
all_nosb <- subset_expt(all_nosb, subset="macrophagetreatment!='inf_sb'")
ama_idx <- pData(all_nosb)[["macrophagetreatment"]] == "inf"
pData(all_nosb)[ama_idx, "stage" ] <- "amastigote"

pData(all_nosb)[["batch"]] <- pData(all_nosb)[["stage"]]
all_norm <- normalize_expt(all_nosb, convert="cpm", norm="quant", transform="log2", filter=TRUE)
plot_pca(all_norm)$plot
```

I think the above picture is sort of the opposite of what we want to
compare in a DE analysis for this set of data, e.g. we want to compare
promastigotes from amastigotes?

```{r compare_pro_ama}
all_nosb <- set_expt_batches(all_nosb, fact="condition") %>%
  set_expt_conditions(fact="stage")
pro_ama <- all_pairwise(all_nosb, filter=TRUE, model_batch="svaseq")
pro_ama_table <- combine_de_tables(pro_ama, excel="excel/tmrc2_pro_vs_ama.xlsx")
```

# SNP profiles

Over the last couple of weeks, I redid all the variant searches with a
newer, (I think) more sensitive and more specific variant tool.  In
addition I changed my script which interprets the results so that it
is able to extract any tags from it, instead of just the one or two
that my previous script handled.  In addition, at least in theory it
is now able to provide the set of amino acid substitutions for every
gene in species without or with introns (not really relevant for
Leishmania panamensis).

However, as of this writing, I have not re-performed the same tasks
with the 2016 data, primarily because it will require remapping all of
the samples.  As a result, for the moment I cannot combine the older
and newer samples.  Thus, any of the following blocks which use the
2016 data are currently disabled.

```{r oldnew_variants, eval=TRUE}
old_expt <- create_expt("sample_sheets/tmrc2_samples_20191203.xlsx",
                        file_column = "tophat2file")

##tt <- lp_expt[["expressionset"]]
##rownames(tt) <- gsub(pattern = "^exon_", replacement = "", x = rownames(tt))
##rownames(tt) <- gsub(pattern = "\\.E1$", replacement = "", x = rownames(tt))
##lp_expt$expressionset <- tt

tt <- old_expt$expressionset
rownames(tt) <- gsub(pattern = "^exon_", replacement = "", x = rownames(tt))
rownames(tt) <- gsub(pattern = "\\.1$", replacement = "", x = rownames(tt))
old_expt$expressionset <- tt
rm(tt)
```

## Create the SNP expressionset

One other important caveat, we have a group of new samples which have
not yet run through the variant search pipeline, so I need to remove
them from consideration.  Though it looks like they finished overnight...

```{r count_expt_old_new}
## The next line drops the samples which are missing the SNP pipeline.
lp_snp <- subset_expt(lp_expt, subset="!is.na(pData(lp_expt)[['freebayessummary']])")
new_snps <- count_expt_snps(lp_snp, annot_column = "freebayessummary", snp_column="PAIRED")
old_snps <- count_expt_snps(old_expt, annot_column = "bcftable", snp_column = 2)

nonzero_snps <- exprs(new_snps) != 0
colSums(nonzero_snps)
```

```{r combine_old_snps, eval=TRUE}
## My old_snps is using an older annotation incorrectly, so fix it here:
Biobase::annotation(old_snps$expressionset) <- Biobase::annotation(new_snps$expressionset)
both_snps <- combine_expts(new_snps, old_snps)
both_norm <- normalize_expt(both_snps, transform = "log2", norm = "quant")

## strains <- both_norm[["design"]][["strain"]]
both_strain <- set_expt_conditions(both_norm, fact = "strain")
```

The data structure 'both_norm' now contains our 2016 data along with
the newer data collected since 2019.

## Plot of SNP profiles for zymodemes

The following plot shows the SNP profiles of all samples (old and new) where the
colors at the top show either the 2.2 strains (orange), 2.3 strains (green), the
previous samples (purple), or the various lab strains (pink etc).

```{r plotting_variants}
new_variant_heatmap <- plot_disheat(new_snps)
dev <- pp(file = "images/raw_snp_disheat.png", height=12, width=12)
new_variant_heatmap$plot
closed <- dev.off()
new_variant_heatmap$plot
```

The function get_snp_sets() takes the provided metadata factor (in
this case 'condition') and looks for variants which are exclusive to
each element in it.  In this case, this is looking for differences
between 2.2 and 2.3, as well as the set shared among them.

```{r get_snp_sets1, eval=FALSE}
snp_sets <- get_snp_sets(both_snps, factor = "condition")
Biobase::annotation(old_expt$expressionset) = Biobase::annotation(lp_expt$expressionset)
both_expt <- combine_expts(lp_expt, old_expt)

snp_genes <- sm(snps_vs_genes(both_expt, snp_sets, expt_name_col = "chromosome"))
## I think we have some metrics here we can plot...
snp_subset <- snp_subset_genes(
  both_expt, both_snps,
  genes = c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
            "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300"))
zymo_heat <- plot_sample_heatmap(snp_subset, row_label = rownames(exprs(snp_subset)))
zymo_heat
```

## Compare variants to DE genes

Najib has asked a few times about the relationship between variants
and DE genes.  In subsequent conversations I figured out what he
really wants to learn is variants in the UTR (most likely 5') which
might affect expression of genes.  The following explicitly does not
help this question, but is a paralog: is there a relationship between
variants in the CDS and differential expression?

```{r variants_vs_de}
vars_df <- data.frame(ID = names(snp_genes$summary_by_gene), variants = as.numeric(snp_genes$summary_by_gene))
vars_df[["variants"]] <- log2(vars_df[["variants"]] + 1)
vars_by_de_gene <- merge(zy_df, vars_df, by.x="row.names", by.y="ID")
cor.test(vars_by_de_gene$deseq_logfc, vars_by_de_gene$variants)
variants_wrt_logfc <- plot_linear_scatter(vars_by_de_gene[, c("deseq_logfc", "variants")])
variants_wrt_logfc$scatter
## It looks like there might be some genes of interest, even though this is not actually
## the question of interest.
```

Didn't I create a set of densities by chromosome?
Oh I think they come in from get_snp_sets()

## SNPS associated with clinical response in the TMRC samples

```{r snp_clinical}
clinical_sets <- get_snp_sets(new_snps, factor = "clinicalresponse")

density_vec <- clinical_sets[["density"]]
chromosome_idx <- grep(pattern = "LpaL", x = names(density_vec))
density_df <- as.data.frame(density_vec[chromosome_idx])
density_df[["chr"]] <- rownames(density_df)
colnames(density_df) <- c("density_vec", "chr")
ggplot(density_df, aes_string(x = "chr", y = "density_vec")) +
  ggplot2::geom_col() +
  ggplot2::theme(axis.text = ggplot2::element_text(size = 10, colour = "black"),
                 axis.text.x = ggplot2::element_text(angle = 90, vjust = 0.5))
## clinical_written <- write_variants(new_snps)
```

### Cross reference these variants by gene

```{r snp_classifications}
clinical_genes <- snps_vs_genes(lp_expt, clinical_sets, expt_name_col = "chromosome")

snp_density <- merge(as.data.frame(clinical_genes[["summary_by_gene"]]),
                     as.data.frame(fData(lp_expt)),
                     by = "row.names")
snp_density <- snp_density[, c(1, 2, 4, 15)]
colnames(snp_density) <- c("name", "snps", "product", "length")
snp_density[["product"]] <- tolower(snp_density[["product"]])
snp_density[["length"]] <- as.numeric(snp_density[["length"]])
snp_density[["density"]] <- snp_density[["snps"]] / snp_density[["length"]]
snp_idx <- order(snp_density[["density"]], decreasing = TRUE)
snp_density <- snp_density[snp_idx, ]

removers <- c("amastin", "gp63", "leishmanolysin")
for (r in removers) {
  drop_idx <- grepl(pattern = r, x = snp_density[["product"]])
  snp_density <- snp_density[!drop_idx, ]
}
## Filter these for [A|a]mastin gp63 Leishmanolysin
```


```{r snp_intersections}
clinical_snps <- snps_intersections(lp_expt, clinical_sets, chr_column = "chromosome")

fail_ref_snps <- as.data.frame(clinical_snps[["inters"]][["failure, reference strain"]])
fail_ref_snps <- rbind(fail_ref_snps,
                       as.data.frame(clinical_snps[["inters"]][["failure"]]))
cure_snps <- as.data.frame(clinical_snps[["inters"]][["cure"]])

head(fail_ref_snps)
head(cure_snps)
write.csv(file="csv/cure_variants.txt", x=rownames(cure_snps))
write.csv(file="csv/fail_variants.txt", x=rownames(fail_ref_snps))

annot <- fData(lp_expt)
clinical_interest <- as.data.frame(clinical_snps[["gene_summaries"]][["cure"]])
clinical_interest <- merge(clinical_interest,
                           as.data.frame(clinical_snps[["gene_summaries"]][["failure, reference strain"]]),
                           by = "row.names")
rownames(clinical_interest) <- clinical_interest[["Row.names"]]
clinical_interest[["Row.names"]] <- NULL
colnames(clinical_interest) <- c("cure_snps","fail_snps")
annot <- merge(annot, clinical_interest, by = "row.names")
rownames(annot) <- annot[["Row.names"]]
annot[["Row.names"]] <- NULL
fData(lp_expt$expressionset) <- annot
```

# Zymodeme for new samples

The heatmap produced here should show the variants only for the zymodeme genes.

## Hunt for snp clusters

I am thinking that if we find clusters of locations which are variant, that
might provide some PCR testing possibilities.

```{r new_zymo}
## Drop the 2.1, 2.4, unknown, and null
pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
new_sets <- get_snp_sets(pruned_snps, factor = "zymodemecategorical")
summary(new_sets)
## 1000000: 2.2
## 0100000: 2.3

summary(new_sets[["intersections"]][["10"]])
write.csv(file="csv/variants_22.csv", x=new_sets[["intersections"]][["10"]])
summary(new_sets[["intersections"]][["01"]])
write.csv(file="csv/variants_23.csv", x=new_sets[["intersections"]][["01"]])
```

Thus we see that there are 3,553 variants associated with 2.2 and 81,589 associated with 2.3.

### A small function for searching for potential PCR primers

The following function uses the positional data to look for sequential
mismatches associated with zymodeme in the hopes that there will be
some regions which would provide good potential targets for a
PCR-based assay.

```{r sequential_search, eval=FALSE}
sequential_variants <- function(snp_sets, conditions = NULL, minimum = 3, maximum_separation = 3) {
  if (is.null(conditions)) {
    conditions <- 1
  }
  intersection_sets <- snp_sets[["intersections"]]
  intersection_names <- snp_sets[["set_names"]]
  chosen_intersection <- 1
  if (is.numeric(conditions)) {
    chosen_intersection <- conditions
  } else {
    intersection_idx <- intersection_names == conditions
    chosen_intersection <- names(intersection_names)[intersection_idx]
  }

  possible_positions <- intersection_sets[[chosen_intersection]]
  position_table <- data.frame(row.names = possible_positions)
  pat <- "^chr_(.+)_pos_(.+)_ref_.*$"
  position_table[["chr"]] <- gsub(pattern = pat, replacement = "\\1", x = rownames(position_table))
  position_table[["pos"]] <- as.numeric(gsub(pattern = pat, replacement = "\\2", x = rownames(position_table)))
  position_idx <- order(position_table[, "chr"], position_table[, "pos"])
  position_table <- position_table[position_idx, ]
  position_table[["dist"]] <- 0

  last_chr <- ""
  for (r in 1:nrow(position_table)) {
    this_chr <- position_table[r, "chr"]
    if (r == 1) {
      position_table[r, "dist"] <- position_table[r, "pos"]
      last_chr <- this_chr
      next
    }
    if (this_chr == last_chr) {
      position_table[r, "dist"] <- position_table[r, "pos"] - position_table[r - 1, "pos"]
    } else {
      position_table[r, "dist"] <- position_table[r, "pos"]
    }
    last_chr <- this_chr
  }

  ## Working interactively here.

  doubles <- position_table[["dist"]] == 1
  doubles <- position_table[doubles, ]
  write.csv(doubles, "doubles.csv")

  one_away <- position_table[["dist"]] == 2
  one_away <- position_table[one_away, ]
  write.csv(one_away, "one_away.csv")

  two_away <- position_table[["dist"]] == 3
  two_away <- position_table[two_away, ]
  write.csv(two_away, "two_away.csv")

  combined <- rbind(doubles, one_away)
  combined <- rbind(combined, two_away)
  position_idx <- order(combined[, "chr"], combined[, "pos"])
  combined <- combined[position_idx, ]

  this_chr <- ""
  for (r in 1:nrow(combined)) {
    this_chr <- combined[r, "chr"]
    if (r == 1) {
      combined[r, "dist_pair"] <- combined[r, "pos"]
      last_chr <- this_chr
      next
    }
    if (this_chr == last_chr) {
      combined[r, "dist_pair"] <- combined[r, "pos"] - combined[r - 1, "pos"]
    } else {
      combined[r, "dist_pair"] <- combined[r, "pos"]
    }
    last_chr <- this_chr
  }

  dist_pair_maximum <- 1000
  dist_pair_minimum <- 200
  dist_pair_idx <- combined[["dist_pair"]] <= dist_pair_maximum &
    combined[["dist_pair"]] >= dist_pair_minimum
  remaining <- combined[dist_pair_idx, ]
  no_weak_idx <- grepl(pattern="ref_(G|C)", x=rownames(remaining))
  remaining <- remaining[no_weak_idx, ]

  print(head(table(position_table[["dist"]])))
  sequentials <- position_table[["dist"]] <= maximum_separation
  message("There are ", sum(sequentials), " candidate regions.")

  ## The following can tell me how many runs of each length occurred, that is not quite what I want.
  ## Now use run length encoding to find the set of sequential sequentials!
  rle_result <- rle(sequentials)
  rle_values <- rle_result[["values"]]
  ## The following line is equivalent to just leaving values alone:
  ## true_values <- rle_result[["values"]] == TRUE
  rle_lengths <- rle_result[["lengths"]]
  true_sequentials <- rle_lengths[rle_values]
  rle_idx <- cumsum(rle_lengths)[which(rle_values)]

  position_table[["last_sequential"]] <- 0
  count <- 0
  for (r in rle_idx) {
    count <- count + 1
    position_table[r, "last_sequential"] <- true_sequentials[count]
  }
  message("The maximum sequential set is: ", max(position_table[["last_sequential"]]), ".")

  wanted_idx <- position_table[["last_sequential"]] >= minimum
  wanted <- position_table[wanted_idx, c("chr", "pos")]
  return(wanted)
}

zymo22_sequentials <- sequential_variants(new_sets, conditions = "z22", minimum=1, maximum_separation=2)
dim(zymo22_sequentials)
## 7 candidate regions for zymodeme 2.2 -- thus I am betting that the reference strain is a 2.2
zymo23_sequentials <- sequential_variants(new_sets, conditions = "z23",
                                          minimum = 2, maximum_separation = 2)
dim(zymo23_sequentials)
## In contrast, there are lots (587) of interesting regions for 2.3!
```

### Extract a promising region from the genome

The first 4 candidate regions from my set of remaining:
* Chr       Pos.   Distance
* LpaL13-15 238433 448
* LpaL13-18 142844 613
* LpaL13-29 830342 252
* LpaL13-33 1331507 843

Lets define a couple of terms:
* Third: Each of the 4 above positions.
* Second: Third - Distance
* End: Third + PrimerLen
* Start: Second - Primerlen

In each instance, these are the last positions, so we want to grab three things:

* The entire region from End -> Start, this way we can have a quick sanity check.
* Start -> Second.
* (Third -> End) <- Reverse complemented

```{r extract_bsgenome, eval=FALSE}
## * LpaL13-15 238433 448
first_candidate_chr <- genome[["LpaL13_15"]]
primer_length <- 22
amplicon_length <- 448
first_candidate_third <- 238433
first_candidate_second <- first_candidate_third - amplicon_length
first_candidate_start <- first_candidate_second - primer_length
first_candidate_end <- first_candidate_third + primer_length
first_candidate_region <- subseq(first_candidate_chr, first_candidate_start, first_candidate_end)
first_candidate_region
first_candidate_5p <- subseq(first_candidate_chr, first_candidate_start, first_candidate_second)
as.character(first_candidate_5p)
first_candidate_3p <- spgs::reverseComplement(subseq(first_candidate_chr, first_candidate_third, first_candidate_end))
first_candidate_3p

## * LpaL13-18 142844 613
second_candidate_chr <- genome[["LpaL13_18"]]
primer_length <- 22
amplicon_length <- 613
second_candidate_third <- 142844
second_candidate_second <- second_candidate_third - amplicon_length
second_candidate_start <- second_candidate_second - primer_length
second_candidate_end <- second_candidate_third + primer_length
second_candidate_region <- subseq(second_candidate_chr, second_candidate_start, second_candidate_end)
second_candidate_region
second_candidate_5p <- subseq(second_candidate_chr, second_candidate_start, second_candidate_second)
as.character(second_candidate_5p)
second_candidate_3p <- spgs::reverseComplement(subseq(second_candidate_chr, second_candidate_third, second_candidate_end))
second_candidate_3p


## * LpaL13-29 830342 252
third_candidate_chr <- genome[["LpaL13_29"]]
primer_length <- 22
amplicon_length <- 252
third_candidate_third <- 830342
third_candidate_second <- third_candidate_third - amplicon_length
third_candidate_start <- third_candidate_second - primer_length
third_candidate_end <- third_candidate_third + primer_length
third_candidate_region <- subseq(third_candidate_chr, third_candidate_start, third_candidate_end)
third_candidate_region
third_candidate_5p <- subseq(third_candidate_chr, third_candidate_start, third_candidate_second)
as.character(third_candidate_5p)
third_candidate_3p <- spgs::reverseComplement(subseq(third_candidate_chr, third_candidate_third, third_candidate_end))
third_candidate_3p
## You are a garbage polypyrimidine tract.
## Which is actually interesting if the mutations mess it up.


## * LpaL13-33 1331507 843
fourth_candidate_chr <- genome[["LpaL13_33"]]
primer_length <- 22
amplicon_length <- 843
fourth_candidate_third <- 1331507
fourth_candidate_second <- fourth_candidate_third - amplicon_length
fourth_candidate_start <- fourth_candidate_second - primer_length
fourth_candidate_end <- fourth_candidate_third + primer_length
fourth_candidate_region <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_end)
fourth_candidate_region
fourth_candidate_5p <- subseq(fourth_candidate_chr, fourth_candidate_start, fourth_candidate_second)
as.character(fourth_candidate_5p)
fourth_candidate_3p <- spgs::reverseComplement(subseq(fourth_candidate_chr, fourth_candidate_third, fourth_candidate_end))
fourth_candidate_3p
```

## Go hunting for Sanger sequencing regions

I made a fun little function which should find regions which have lots of variants
associated with a given experimental factor.

```{r sanger_fun}
pheno <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
pheno <- subset_expt(pheno, subset = "!is.na(pData(pheno)[['bcftable']])")
pheno_snps <- sm(count_expt_snps(pheno, annot_column = "bcftable"))

fun_stuff <- snp_density_primers(
    pheno_snps,
    bsgenome = "BSGenome.Leishmania.panamensis.MHOMCOL81L13.v53",
    gff = "reference/TriTrypDB-53_LpanamensisMHOMCOL81L13.gff")
drop_scaffolds <- grepl(x = rownames(fun_stuff$favorites), pattern = "SCAF")
favorite_primer_regions <- fun_stuff[["favorites"]][!drop_scaffolds, ]
favorite_primer_regions[["bin"]] <- rownames(favorite_primer_regions)
library(dplyr)
favorite_primer_regions <- favorite_primer_regions %>%
  relocate(bin)
```

## Combine this table with 2.2/2.3 genes

Here is my note from our meeting:

Cross reference primers to DE genes of 2.2/2.3 and/or resistance/suscpetible,
add a column to the primer spreadsheet with the DE genes (in retrospect I am guessing
this actually means to put the logFC as a column.

One nice thing, I did a semantic removal on the lp_expt, so the set of logFC/pvalues
should not have any of the offending types; thus I should be able to automagically
get rid of them in the merge.

```{r xref_primers_deg}
logfc <- zy_table_sva[["data"]][["z23_vs_z22"]]
logfc_columns <- logfc[, c("deseq_logfc", "deseq_adjp")]
colnames(logfc_columns) <- c("z23_logfc", "z23_adjp")
new_table <- merge(favorite_primer_regions, logfc_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names")
sus <- sus_table_sva[["data"]][["sensitive_vs_resistant"]]
sus_columns <- sus[, c("deseq_logfc", "deseq_adjp")]
colnames(sus_columns) <- c("sus_logfc", "sus_adjp")
new_table <- merge(new_table, sus_columns,
                   by.x = "closest_gene_before_id", by.y = "row.names") %>%
  relocate(bin)
written <- write_xlsx(data=new_table,
                      excel="excel/favorite_primers_xref_zy_sus.xlsx")
```


## Make a heatmap describing the clustering of variants

We can cross reference the variants against the zymodeme status and
plot a heatmap of the results and hopefully see how they separate.

```{r zymo_heatmaps}
snp_genes <- sm(snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"))

clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, normq = "quant") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  set_expt_colors(clinical_colors_v2)

zymo_heat <- plot_disheat(new_zymo_norm)
dev <- pp(file = "images/onlyz22_z23_snp_heatmap.pdf", width=12, height=12)
zymo_heat[["plot"]]
closed <- dev.off()
zymo_heat[["plot"]]
```

### Annotated heatmap of variants

Now let us try to make a heatmap which includes some of the annotation data.

```{r zymo_heat_panel_genes}
des <- both_norm[["design"]]
undef_idx <- is.na(des[["strain"]])
des[undef_idx, "strain"] <- "unknown"

##hmcols <- colorRampPalette(c("yellow","black","darkblue"))(256)
correlations <- hpgl_cor(exprs(both_norm))
na_idx <- is.na(correlations)
correlations[na_idx] <- 0

zymo_missing_idx <- is.na(des[["zymodemecategorical"]])
des[["zymodemecategorical"]] <- as.character(des[["zymodemecategorical"]])
des[["clinicalcategorical"]] <- as.character(des[["clinicalcategorical"]])
des[zymo_missing_idx, "zymodemecategorical"] <- "unknown"
mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c("zymodemecategorical", "clinicalcategorical")])

unknown_clinical <- is.na(col_data[["clinicalcategorical"]])
row_data <- as.data.frame(des[, c("strain")])
colnames(col_data) <- c("zymodeme", "outcome")
col_data[unknown_clinical, "outcome"] <- "undefined"

colnames(row_data) <- c("strain")
myannot <- list(
  "Col" = list("data" = col_data),
  "Row" = list("data" = row_data))
myclust <- list("cuth" = 1.0,
                "col" = BrewerClusterCol)
mylabs <- list(
  "Row" = list("nrow" = 4),
  "Col" = list("nrow" = 4))
hmcols <- colorRampPalette(c("darkblue", "beige"))(240)
zymo_annot_heat <- annHeatmap2(
    correlations,
    dendrogram = mydendro,
    annotation = myannot,
    cluster = myclust,
    labels = mylabs,
    ## The following controls if the picture is symmetric
    scale = "none",
    col = hmcols)

dev <- pp(file = "images/dendro_heatmap.png", height = 20, width = 20)
plot(zymo_annot_heat)
closed <- dev.off()
plot(zymo_annot_heat)
```

Print the larger heatmap so that all the labels appear.  Keep in mind
that as we get more samples, this image needs to continue getting
bigger.

![big heatmap](images/dendro_heatmap.png)


```{r theresa_idea}
xref_prop <- table(pheno_snps[["conditions"]])
pheno_snps$conditions
idx_tbl <- exprs(pheno_snps) > 5
new_tbl <- data.frame(row.names = rownames(exprs(pheno_snps)))
for (n in names(xref_prop)) {
  new_tbl[[n]] <- 0
  idx_cols <- which(pheno_snps[["conditions"]] == n)
  prop_col <- rowSums(idx_tbl[, idx_cols]) / xref_prop[n]
  new_tbl[n] <- prop_col
}
keepers <- grepl(x = rownames(new_tbl), pattern = "LpaL13")
new_tbl <- new_tbl[keepers, ]
new_tbl[["strong22"]] <- 1.001 - new_tbl[["z2.2"]]
new_tbl[["strong23"]] <- 1.001 - new_tbl[["z2.3"]]
s22_na <- new_tbl[["strong22"]] > 1
new_tbl[s22_na, "strong22"] <- 1
s23_na <- new_tbl[["strong23"]] > 1
new_tbl[s23_na, "strong23"] <- 1

new_tbl[["SNP"]] <- rownames(new_tbl)
new_tbl[["Chromosome"]] <- gsub(x = new_tbl[["SNP"]], pattern = "chr_(.*)_pos_.*", replacement = "\\1")
new_tbl[["Position"]] <- gsub(x = new_tbl[["SNP"]], pattern = ".*_pos_(\\d+)_.*", replacement = "\\1")
new_tbl <- new_tbl[, c("SNP", "Chromosome", "Position", "strong22", "strong23")]

library(CMplot)
simplify <- new_tbl
simplify[["strong22"]] <- NULL

CMplot(simplify, bin.size = 100000)

CMplot(new_tbl, plot.type="m", multracks=TRUE, threshold = c(0.01, 0.05),
       threshold.lwd=c(1,1), threshold.col=c("black","grey"),
       amplify=TRUE, bin.size=10000,
       chr.den.col=c("darkgreen", "yellow", "red"),
       signal.col=c("red", "green", "blue"),
       signal.cex=1, file="jpg", memo="", dpi=300, file.output=TRUE, verbose=TRUE)
```

![SNP Density](SNP-Density.ratio.jpg)
![Circular Manhattan](Circular-Manhattan.ratio.jpg)
![Rectangular Manhattan](Rectangular-Manhattan.ratio.jpg)
![QQ](QQplot.ratio.jpg)

## Try out MatrixEQTL

This tool looks a little opaque, but provides sample data with things
that make sense to me and should be pretty easy to recapitulate in our
data.

1.  covariates.txt: Columns are samples, rows are things from pData -- the
    most likely ones of interest for our data would be zymodeme,
    sensitivity
2.  geneloc.txt: columns are 'geneid', 'chr', 'left', 'right'.  I
    guess I can assume left and right are start/stop; in which case
    this is trivially acquirable from fData.
3.  ge.txt: This appears to be a log(rpkm/cpm) table with rows as genes and
    columns as samples
4.  snpsloc.txt: columns are 'snpid', 'chr', 'pos'
5.  snps.txt: columns are samples, rows are the ids from snsploc,
    values a 0,1,2.  I assume 0 is identical and 1..12 are the various
    A->TGC T->AGC C->AGT G->ACT

```{r matrixeqtl, eval=FALSE}
## For this, let us use the 'new_snps' data structure.
## Caveat here: these need to be coerced to numbers.
my_covariates <- pData(new_snps)[, c("zymodemecategorical", "clinicalcategorical")]
for (col in colnames(my_covariates)) {
  my_covariates[[col]] <- as.numeric(as.factor(my_covariates[[col]]))
}
my_covariates <- t(my_covariates)

my_geneloc <- fData(lp_expt)[, c("gid", "chromosome", "start", "end")]
colnames(my_geneloc) <- c("geneid", "chr", "left", "right")

my_ge <- exprs(normalize_expt(lp_expt, transform = "log2", filter = TRUE, convert = "cpm"))
used_samples <- tolower(colnames(my_ge)) %in% colnames(exprs(new_snps))
my_ge <- my_ge[, used_samples]

my_snpsloc <- data.frame(rownames = rownames(exprs(new_snps)))
## Oh, caveat here: Because of the way I stored the data,
## I could have duplicate rows which presumably will make matrixEQTL sad
my_snpsloc[["chr"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\1",
                            x = rownames(my_snpsloc))
my_snpsloc[["pos"]] <- gsub(pattern = "^chr_(.+)_pos(.+)_ref_.*$", replacement = "\\2",
                            x = rownames(my_snpsloc))
test <- duplicated(my_snpsloc)
## Each duplicated row would be another variant at that position;
## so in theory we would do a rle to number them I am guessing
## However, I do not have different variants so I think I can ignore this for the moment
## but will need to make my matrix either 0 or 1.
if (sum(test) > 0) {
  message("There are: ", sum(duplicated), " duplicated entries.")
  keep_idx <- ! test
  my_snpsloc <- my_snpsloc[keep_idx, ]
}

my_snps <- exprs(new_snps)
one_idx <- my_snps > 0
my_snps[one_idx] <- 1

## Ok, at this point I think I have all the pieces which this method wants...
## Oh, no I guess not; it actually wants the data as a set of filenames...
library(MatrixEQTL)
write.table(my_snps, "eqtl/snps.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snps, "eqtl/snps.tsv", )
write.table(my_snpsloc, "eqtl/snpsloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(my_snpsloc, "eqtl/snpsloc.tsv")
write.table(as.data.frame(my_ge), "eqtl/ge.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_ge), "eqtl/ge.tsv")
write.table(as.data.frame(my_geneloc), "eqtl/geneloc.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_geneloc), "eqtl/geneloc.tsv")
write.table(as.data.frame(my_covariates), "eqtl/covariates.tsv", na = "NA", col.names = TRUE, row.names = TRUE, sep = "\t", quote = TRUE)
## readr::write_tsv(as.data.frame(my_covariates), "eqtl/covariates.tsv")

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

# Genotype file name
SNP_file_name = "eqtl/snps.tsv"
snps_location_file_name = "eqtl/snpsloc.tsv"
expression_file_name = "eqtl/ge.tsv"
gene_location_file_name = "eqtl/geneloc.tsv"
covariates_file_name = "eqtl/covariates.tsv"
# Output file name
output_file_name_cis = tempfile()
output_file_name_tra = tempfile()
# Only associations significant at this level will be saved
pvOutputThreshold_cis = 0.1
pvOutputThreshold_tra = 0.1
# Error covariance matrix
# Set to numeric() for identity.
errorCovariance = numeric()
# errorCovariance = read.table("Sample_Data/errorCovariance.txt");
# Distance for local gene-SNP pairs
cisDist = 1e6
## Load genotype data
snps = SlicedData$new()
snps$fileDelimiter = "\t"      # the TAB character
snps$fileOmitCharacters = "NA" # denote missing values;
snps$fileSkipRows = 1          # one row of column labels
snps$fileSkipColumns = 1       # one column of row labels
snps$fileSliceSize = 2000      # read file in slices of 2,000 rows
snps$LoadFile(SNP_file_name)
## Load gene expression data
gene = SlicedData$new()
gene$fileDelimiter = "\t"      # the TAB character
gene$fileOmitCharacters = "NA" # denote missing values;
gene$fileSkipRows = 1          # one row of column labels
gene$fileSkipColumns = 1       # one column of row labels
gene$fileSliceSize = 2000      # read file in slices of 2,000 rows
gene$LoadFile(expression_file_name)
## Load covariates
cvrt = SlicedData$new()
cvrt$fileDelimiter = "\t"      # the TAB character
cvrt$fileOmitCharacters = "NA" # denote missing values;
cvrt$fileSkipRows = 1          # one row of column labels
cvrt$fileSkipColumns = 1       # one column of row labels
if(length(covariates_file_name) > 0) {
  cvrt$LoadFile(covariates_file_name)
}
## Run the analysis
snpspos = read.table(snps_location_file_name, header = TRUE, stringsAsFactors = FALSE)
genepos = read.table(gene_location_file_name, header = TRUE, stringsAsFactors = FALSE)

me = Matrix_eQTL_main(
    snps = snps,
    gene = gene,
    cvrt = cvrt,
    output_file_name = output_file_name_tra,
    pvOutputThreshold = pvOutputThreshold_tra,
    useModel = useModel,
    errorCovariance = errorCovariance,
    verbose = TRUE,
    output_file_name.cis = output_file_name_cis,
    pvOutputThreshold.cis = pvOutputThreshold_cis,
    snpspos = snpspos,
    genepos = genepos,
    cisDist = cisDist,
    pvalue.hist = "qqplot",
    min.pv.by.genesnp = FALSE,
    noFDRsaveMemory = FALSE);
```



```{r saveme}
if (!isTRUE(get0("skip_load"))) {
  pander::pander(sessionInfo())
  message(paste0("This is hpgltools commit: ", get_git_commit()))
  message(paste0("Saving to ", savefile))
  tmp <- sm(saveme(filename = savefile))
}
```

```{r loadme_after, eval = FALSE}
tmp <- loadme(filename = savefile)
```
