sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_202203.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:

Default trimming was performed.
Hisat2 was used to map the remaining reads against the Leishmania panamensis genome revision 36.
The alignments from hisat2 were used to count reads/gene against the revision 36 annotations with htseq.
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))

hisat_annot <- all_lp_annot

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:

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

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

sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "zymodemecategorical")
lp_expt <- create_expt(sample_sheet,
                       gene_info = hisat_annot,
                       annotation = orgdb,
                       id_column = "hpglidentifier",
                       file_column = "lpanamensisv36hisatfile") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  subset_expt(nonzero = 8550) %>%
  subset_expt(coverage = 5000000) %>%
  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 they were 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 64 columns(metadata fields).

## Warning in create_expt(sample_sheet, gene_info = hisat_annot, annotation =
## 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 rows and 104 columns.

## The samples (and read coverage) removed when filtering 8550 non-zero genes are:

## TMRC20002 TMRC20006 
##  11681227   6670348

## subset_expt(): There were 104, now there are 102 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 102, now there are 100 samples.

## semantic_expt_filter(): Removed 68 genes.

libsizes <- plot_libsize(lp_expt)
dev <- pp(file = "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: 80 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

closed <- dev.off()

lp_box <- plot_boxplot(lp_expt)

## 8022 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"]])

## 
## notapplicable       unknown           z10           z20           z21 
##             2             4             1             1             7 
##           z22           z23           z24           z30 
##            43            39             2             1

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

## 
##                                  cure                               failure 
##                                    38                                    37 
##                       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

starting <- as.numeric(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])

## Warning: NAs introduced by coercion

sus_categorical <- starting
na_idx <- is.na(starting)
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"

pData(lp_expt)[["sus_category"]] <- sus_categorical
table(sus_categorical)

## sus_categorical
## ambiguous resistant sensitive   unknown 
##         5        12        32        51

clinical_colors <- list(
    "z1.0" = "#333333",
    "z2.0" = "#555555",
    "z3.0" = "#777777",
    "z2.1" = "#874400",
    "z2.2" = "#0000cc",
    "z2.3" = "#cc0000",
    "z2.4" = "#df7000",
    "unknown" = "#cbcbcb",
    "null" = "#000000")
clinical_samples <- lp_expt %>%
  set_expt_batches(fact = sus_categorical) %>%
  set_expt_colors(clinical_colors)
table(pData(clinical_samples)[["condition"]])

## 
##    null unknown    z1.0    z2.0    z2.1    z2.2    z2.3    z2.4    z3.0 
##       2       4       1       1       7      43      39       2       1

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

## Removing 135 low-count genes (8575 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 100, now there are 82 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")
zymo_tsne$plot

## Warning: ggrepel: 78 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

clinical_nb <- normalize_expt(clinical_samples, convert = "cpm", transform = "log2",
                         filter = TRUE, batch = "svaseq")

## Removing 135 low-count genes (8575 remaining).

## Setting 702 low elements to zero.

## transform_counts: Found 702 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")
clinical_nb_tsne$plot

## Warning: ggrepel: 84 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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) %>%
  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      37      25

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

## Removing 135 low-count genes (8575 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 <- normalize_expt(cf_expt, convert = "cpm", transform = "log2",
                        norm = "quant", filter = TRUE, batch = "svaseq")

## Warning in normalize_expt(cf_expt, convert = "cpm", transform = "log2", :
## Quantile normalization and sva do not always play well together.

## Removing 135 low-count genes (8575 remaining).

## Setting 230 low elements to zero.

## transform_counts: Found 230 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 135 low-count genes (8575 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)
test$anova_p

##                           PC1      PC2    PC3       PC4      PC5       PC6
## clinicalcategorical 3.412e-01 0.451405 0.9566 7.640e-03 0.237474 3.565e-01
## zymodemecategorical 1.159e-05 0.006049 0.7837 3.332e-02 0.004994 4.047e-01
## pathogenstrain      5.311e-01 0.872529 0.6709 4.969e-05 0.021346 2.287e-01
## passagenumber       9.481e-01 0.173679 0.4688 3.298e-02 0.856872 7.004e-06

test$cor_heatmap

sus_colors <- list(
    "resistant" = "#8563a7",
    "sensitive" = "#8d0000",
    "ambiguous" = "#cbcbcb",
    "unknown" = "#000000")
sus_expt <- set_expt_conditions(lp_expt, fact = "sus_category") %>%
  set_expt_batches(fact = "zymodemecategorical") %>%
  set_expt_colors(colors = sus_colors) %>%
  subset_expt(subset = "batch!='z24'") %>%
  subset_expt(subset = "batch!='z21'")

## subset_expt(): There were 100, now there are 98 samples.

## subset_expt(): There were 98, now there are 91 samples.

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

## Removing 136 low-count genes (8574 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 136 low-count genes (8574 remaining).

## Setting 319 low elements to zero.

## transform_counts: Found 319 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 100, now there are 82 samples.

zy_norm <- normalize_expt(zy_expt, filter = TRUE, convert = "cpm", norm = "quant")

## Removing 153 low-count genes (8557 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"),
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"))

## Deleting the file excel/zy_tables_nobatch-v202203.xlsx before writing the tables.

zy_sig_nobatch <- extract_significant_genes(
    zy_table_nobatch,
    excel = glue::glue("excel/zy_sig_nobatch-v{ver}.xlsx"))

## Deleting the file excel/zy_sig_nobatch-v202203.xlsx before writing the tables.

zy_de_sva <- all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq")

## Removing 0 low-count genes (8557 remaining).

## Setting 379 low elements to zero.

## transform_counts: Found 379 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"),
    gmt = glue::glue("gmt/zymodeme_sva-v{ver}.gmt"))

## Deleting the file excel/zy_tables_sva-v202203.xlsx before writing the tables.

zy_sig_sva <- extract_significant_genes(
    zy_table_sva,
    excel = glue::glue("excel/zy_sig_sva-v{ver}.xlsx"))

## Deleting the file excel/zy_sig_sva-v202203.xlsx before writing the tables.

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_de <- all_pairwise(cf_expt, filter = TRUE, model_batch = "svaseq")

## Removing 0 low-count genes (8575 remaining).

## Setting 342 low elements to zero.

## transform_counts: Found 342 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-v202203.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-v202203.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 (8574 remaining).

## Setting 319 low elements to zero.

## transform_counts: Found 319 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-v202203.xlsx before writing the tables.

sus_sig_sva <- extract_significant_genes(sus_table_sva, excel = glue::glue("excel/sus_sig_sva-v{ver}.xlsx"))

## Deleting the file excel/sus_sig_sva-v202203.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-v202203.xlsx before writing the tables.

sus_sig_nobatch <- extract_significant_genes(sus_table_nobatch, excel = glue::glue("excel/sus_sig_nobatch-v{ver}.xlsx"))

## Deleting the file excel/sus_sig_nobatch-v202203.xlsx before writing the tables.

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

	gid	annotgeneproduct	annotgenetype	chromosome	start	end	strand	annotstrand	annotchromosome	annotcdslength	length	deseq_logfc	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	ebseq_fc	ebseq_logfc	ebseq_c1mean	ebseq_c2mean	ebseq_mean	ebseq_var	ebseq_postfc	ebseq_ppee	ebseq_ppde	ebseq_adjp	edger_logcpm	edger_lr	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_000017600	LPAL13_000017600	hypothetical protein, conserved	protein coding	LPAL13_SCAF000146	359	586	+	forward	Not Assigned	228.0	227	6.547	6.529	0e+00	6.128	0.0310	4.2400	-1.2170	5.0723	2.5415	5.457	8.906	0.0000	0.0000	519.70	0.7000	9.353	76.859	6.264	12.7294	979.13	709.44	4.389e+05	61.504	0e+00	1.0000	0e+00	4.4180	48.32	2.2140	2.782	-2.3820	0.0066	3.096e-02	1.875e-17	3.072e-09	9.877e-01	1.163e-07	6.914	3.355e+00	4.852e-01	2.201e-03	1.454e-05
LPAL13_000053200	LPAL13_000053200	hypothetical protein	protein coding	LPAL13_SCAF000804	5037	5249	-	reverse	Not Assigned	213.0	212	8.731	9.955	0e+00	5.774	0.0178	0.9899	-4.2030	8.5553	0.1296	5.193	9.698	0.0000	0.0000	67.36	1.1270	7.744	13050.655	13.672	0.0000	130.50	94.08	8.140e+03	37.001	1e+00	0.0000	1e+00	1.5320	43.07	-0.9734	3.055	-2.1450	0.0030	1.779e-02	8.850e-12	2.053e-08	0.000e+00	1.076e-08	8.372	2.811e-01	3.358e-02	9.907e-04	2.944e-06
LPAL13_300029400	LPAL13_300029400	hypothetical protein, conserved	protein coding	LpaL13_30	853953	854150	-	reverse	30	198.0	197	6.277	6.183	0e+00	4.801	0.0019	1.5890	-2.6050	1.8478	1.8507	4.194	9.069	0.0000	0.0000	75.01	0.7892	7.954	62.349	5.962	2.0413	127.89	92.77	9.658e+03	23.324	0e+00	0.0000	0e+00	1.6430	40.92	-0.1934	3.987	0.1668	0.0001	1.930e-03	2.256e-12	4.812e-08	0.000e+00	1.106e-06	5.765	3.125e-01	5.420e-02	4.570e-05	6.265e-09
LPAL13_200050100	LPAL13_200050100	hypothetical protein	protein coding	LpaL13_20.1	1627529	1627717	+	forward	20.1	189.0	188	5.368	5.317	0e+00	4.517	0.0018	2.2500	-1.9170	1.7009	2.3837	4.168	8.277	0.0000	0.0000	95.95	0.6436	8.341	24.141	4.593	8.0134	193.68	141.87	2.250e+04	17.216	0e+00	1.0000	0e+00	2.0230	44.48	0.4662	4.017	0.4812	0.0001	1.775e-03	1.226e-13	1.380e-08	9.462e-01	8.648e-06	5.074	4.967e-01	9.788e-02	4.113e-05	5.076e-09
LPAL13_350011800	LPAL13_350011800	hypothetical protein, conserved	protein coding	LpaL13_35	171009	171242	+	forward	35	234.0	233	5.123	5.098	0e+00	4.387	0.0061	2.6570	-1.2270	2.8101	1.1071	3.885	9.083	0.0000	0.0000	149.50	0.6430	7.967	31.867	4.994	9.1908	293.19	213.94	5.991e+04	23.747	0e+00	1.0000	0e+00	2.6270	40.29	0.8868	3.525	-0.6383	0.0007	6.089e-03	2.039e-12	6.334e-08	9.839e-01	3.867e-08	4.911	5.696e-01	1.160e-01	2.244e-04	1.511e-07
LPAL13_000014000	LPAL13_000014000	hypothetical protein	protein coding	LPAL13_SCAF000119	655	942	+	forward	Not Assigned	288.0	287	4.062	4.051	0e+00	3.815	0.0065	2.3230	-1.1330	1.7770	1.8434	3.456	7.525	0.0000	0.0000	105.30	0.5694	7.134	16.711	4.063	11.6917	195.53	144.23	1.334e+04	13.065	0e+00	1.0000	0e+00	2.1540	35.52	0.8292	3.497	-0.7034	0.0007	6.535e-03	6.166e-10	3.971e-07	9.877e-01	1.239e-05	4.193	6.941e-01	1.655e-01	2.457e-04	1.811e-07
LPAL13_000035500	LPAL13_000035500	hypothetical protein, conserved	protein coding	LPAL13_SCAF000492	7045	7410	+	forward	Not Assigned	366.0	365	4.413	4.405	0e+00	3.938	0.0199	4.1930	0.7673	4.0955	0.5624	3.426	8.098	0.0000	0.0000	422.60	0.5579	7.910	18.069	4.175	49.2030	889.21	654.79	3.850e+05	17.450	0e+00	1.0000	0e+00	4.1270	41.20	2.7430	2.995	-1.9030	0.0036	1.995e-02	2.853e-12	4.751e-08	9.877e-01	6.885e-08	4.507	7.460e-01	1.655e-01	1.186e-03	4.220e-06
LPAL13_220019500	LPAL13_220019500	hypothetical protein	protein coding	LpaL13_22	578260	578538	+	forward	22	279.0	278	3.718	3.714	0e+00	3.291	0.0179	3.3820	0.2987	2.5264	0.8041	3.084	8.002	0.0000	0.0000	261.80	0.5013	7.416	13.780	3.784	31.6002	435.57	322.83	7.699e+04	12.711	0e+00	1.0000	0e+00	3.4410	38.94	2.3750	3.052	-1.7380	0.0030	1.789e-02	8.091e-11	1.063e-07	9.877e-01	1.937e-07	3.803	4.787e-01	1.259e-01	9.987e-04	2.992e-06
LPAL13_000040700	LPAL13_000040700	hypothetical protein, conserved	protein coding	LPAL13_SCAF000598	54	1067	+	forward	Not Assigned	1014.0	1013	6.480	6.870	0e+00	3.317	0.0300	-0.9737	-4.0110	5.9817	0.5233	3.038	6.245	0.0000	0.0000	18.06	0.9817	6.600	222.255	7.796	0.1376	32.79	23.68	8.650e+02	9.563	0e+00	0.0000	0e+00	-0.1662	31.45	-2.1520	2.798	-2.6350	0.0063	2.998e-02	1.239e-08	1.674e-06	0.000e+00	6.797e-06	5.577	6.409e-01	1.149e-01	2.102e-03	1.326e-05
LPAL13_040019400	LPAL13_040019400	hypothetical protein	protein coding	LpaL13_04	440768	441127	-	reverse	4	360.0	359	5.430	5.214	0e+00	3.374	0.0051	-0.5295	-3.3990	1.9225	1.0580	2.869	7.404	0.0000	0.0000	21.87	0.8319	6.528	42.408	5.406	0.8143	34.95	25.42	1.781e+03	8.602	0e+00	0.0000	0e+00	-0.0039	31.59	-1.8600	3.598	-1.0640	0.0005	5.066e-03	1.768e-08	1.525e-06	0.000e+00	2.947e-06	4.736	1.342e-02	2.834e-03	1.755e-04	9.240e-08
LPAL13_080009800	LPAL13_080009800	tuzin, putative	protein coding	LpaL13_08	184254	185207	+	forward	8	954.0	953	3.180	3.173	0e+00	3.034	0.0002	7.1410	4.2800	1.6073	0.4533	2.861	9.558	0.0000	0.0000	2813.00	0.4560	6.974	8.758	3.131	623.6144	5462.00	4111.75	1.567e+07	8.975	0e+00	1.0000	0e+00	6.8490	36.92	5.9470	4.756	3.3470	0.0000	2.262e-04	1.394e-09	2.968e-07	1.000e+00	4.813e-09	3.291	2.770e-01	8.418e-02	2.533e-06	1.924e-11
LPAL13_170012200	LPAL13_170012200	hypothetical protein	protein coding	LpaL13_17	289959	290222	-	reverse	17	264.0	263	3.639	3.632	0e+00	3.209	0.0047	3.1250	0.3654	3.6675	0.2477	2.759	7.402	0.0000	0.0000	201.30	0.5657	6.434	13.492	3.754	31.6019	426.49	316.29	9.550e+04	12.528	0e+00	1.0000	0e+00	3.0710	29.59	1.6540	3.631	-0.1810	0.0005	4.688e-03	3.019e-08	3.157e-06	9.877e-01	5.432e-07	3.697	4.184e-01	1.132e-01	1.573e-04	7.422e-08
LPAL13_000011700	LPAL13_000011700	hypothetical protein	protein coding	LPAL13_SCAF000076	101	364	-	reverse	Not Assigned	264.0	263	6.172	7.411	0e+00	3.208	0.0103	-1.4660	-4.2030	6.0405	0.1296	2.738	6.037	0.0000	0.0000	12.04	1.2090	5.105	2517.131	11.298	0.0000	25.16	18.14	5.252e+02	7.745	1e+00	0.0000	1e+00	-0.6559	25.26	-2.8610	3.299	-1.7560	0.0014	1.033e-02	1.271e-05	1.956e-05	0.000e+00	2.712e-05	5.348	1.400e+00	2.619e-01	4.669e-04	6.529e-07
LPAL13_170014500	LPAL13_170014500	hypothetical protein, conserved	protein coding	LpaL13_17	361708	362040	+	forward	17	333.0	332	5.301	5.138	5e-04	3.262	0.0082	-0.6603	-3.3280	6.1076	1.4218	2.668	4.750	0.0000	0.0004	19.30	1.0890	4.868	44.410	5.473	0.9966	44.69	32.50	1.669e+03	10.387	0e+00	0.0000	0e+00	-0.2263	17.46	-2.4060	3.400	-1.4310	0.0010	8.225e-03	3.589e-05	4.608e-04	0.000e+00	4.433e-04	4.539	2.966e-03	6.535e-04	3.475e-04	3.314e-07
LPAL13_130006100	LPAL13_130006100	hypothetical protein	protein coding	LpaL13_13	26114	26335	-	reverse	13	222.0	221	3.071	3.067	0e+00	2.609	0.0028	3.4680	0.9263	0.9553	0.3111	2.541	10.670	0.0000	0.0000	222.00	0.3907	7.859	8.019	3.003	48.3185	387.52	292.86	5.261e+04	7.668	0e+00	1.0000	0e+00	3.2120	48.23	2.5630	3.845	0.4668	0.0002	2.764e-03	3.330e-12	3.072e-09	9.877e-01	9.132e-10	2.914	4.009e-03	1.376e-03	7.543e-05	1.707e-08
LPAL13_080010600	LPAL13_080010600	hypothetical protein, conserved	protein coding	LpaL13_08	195555	195749	-	reverse	8	195.0	194	6.047	7.280	0e+00	2.934	0.0085	-1.7800	-4.2030	4.6829	0.1296	2.423	6.023	0.0000	0.0000	10.18	1.1040	5.478	2040.429	10.995	0.0000	20.39	14.70	5.085e+02	6.497	1e+00	0.0000	1e+00	-0.9281	27.21	-3.0370	3.384	-1.6290	0.0011	8.518e-03	2.895e-06	8.016e-06	0.000e+00	1.934e-05	5.079	1.849e+00	3.641e-01	3.547e-04	3.773e-07
LPAL13_190012600	LPAL13_190012600	hypothetical protein, conserved	protein coding	LpaL13_19	254293	254577	-	reverse	19	285.0	284	3.390	3.380	0e+00	2.779	0.0041	1.9840	-0.4375	1.6084	0.3135	2.421	8.668	0.0000	0.0000	88.51	0.4592	7.383	9.447	3.240	16.3299	154.35	115.83	8.229e+03	7.973	0e+00	1.0000	0e+00	1.9080	40.19	1.0670	3.689	-0.0955	0.0004	4.098e-03	8.848e-11	6.334e-08	9.839e-01	2.046e-08	3.222	2.466e-02	7.653e-03	1.290e-04	4.995e-08
LPAL13_240030500	LPAL13_240030500	hypothetical protein	protein coding	LpaL13_24	828719	828913	+	forward	24	195.0	194	2.269	2.263	1e-04	2.495	0.0171	3.2970	0.9109	1.1727	3.9478	2.386	3.939	0.0016	0.0094	209.20	0.4572	4.963	4.499	2.170	78.1961	351.82	275.46	3.701e+04	4.394	4e-04	0.9996	4e-04	3.1130	21.31	2.3430	3.072	-1.7090	0.0028	1.713e-02	2.434e-05	9.179e-05	9.839e-01	9.385e-03	2.351	1.967e-01	8.365e-02	9.425e-04	2.652e-06
LPAL13_210015300	LPAL13_210015300	core histone h2a/h2b/h3/h4, putative	protein coding	LpaL13_21	324301	324699	+	forward	21	399.0	398	2.631	2.630	0e+00	2.495	0.0000	8.7400	6.3960	1.7349	0.5224	2.344	7.430	0.0000	0.0000	10480.00	0.4531	5.805	5.971	2.578	2793.2520	16679.76	12804.45	1.102e+08	6.107	6e-04	0.9994	6e-04	8.7460	32.65	7.9550	5.659	6.7950	0.0000	1.857e-05	5.080e-07	8.927e-07	1.000e+00	7.219e-07	2.578	6.120e-02	2.375e-02	6.718e-08	1.026e-14
LPAL13_000018000	LPAL13_000018000	hypothetical protein	protein coding	LPAL13_SCAF000166	2	412	+	forward	Not Assigned	411.0	410	2.722	2.720	0e+00	2.699	0.0005	6.7350	4.4450	1.8807	0.1285	2.291	8.574	0.0000	0.0000	2186.00	0.4386	6.206	6.058	2.599	662.2272	4012.04	3077.21	5.392e+06	6.156	0e+00	1.0000	0e+00	6.4850	31.02	5.6940	4.503	2.3910	0.0000	4.738e-04	9.868e-08	1.793e-06	1.000e+00	3.431e-08	2.799	2.065e-01	7.377e-02	6.769e-06	1.369e-10

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

	gid	annotgeneproduct	annotgenetype	chromosome	start	end	strand	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_000033300	LPAL13_000033300	hypothetical protein, conserved	protein coding	LPAL13_SCAF000463	551	811	+	forward	Not Assigned	261.0	260	-4.051	0.0061	-4.016	0.0043	-7.322	0.0000	-3.5060	3.4470	11.5862	0.0607	-6.954	-11.300	0.000	0.0000	121.800	1.2180	-3.326	0.0009	0.1453	-2.7829	327.09	47.516	125.536	2.384e+04	0.1538	0.0000	0.0000	0.0000	2.3120	12.050	0.0005	-0.9159	-7.608	13.830	0.0000	4.800e-08	6.163e-03	4.547e-03	0.000e+00	9.132e-10	-5.130	0.000e+00	0.000e+00	4.663e-04	1.964e-07
LPAL13_000012000	LPAL13_000012000	hypothetical protein	protein coding	LPAL13_SCAF000080	710	1159	-	reverse	Not Assigned	450.0	449	-2.336	0.0027	-2.341	0.0008	-4.464	0.0000	0.0134	3.9290	8.0366	0.1673	-3.916	-7.492	0.000	0.0000	192.500	0.6472	-3.610	0.0003	0.2451	-2.0288	472.57	115.799	215.362	5.531e+04	0.2569	0.0256	0.9744	0.0256	2.9810	15.950	0.0001	1.2880	-5.889	7.857	0.0000	9.491e-06	2.776e-03	8.784e-04	9.642e-01	6.194e-07	-2.949	1.112e-01	-3.770e-02	1.238e-04	2.606e-08
LPAL13_310035500	LPAL13_310035500	hypothetical protein	protein coding	LpaL13_31	1198439	1198957	-	reverse	31	519.0	518	-2.338	0.0144	-2.275	0.0084	-3.574	0.0000	-4.2500	-0.4918	3.5831	0.4599	-3.759	-9.581	0.000	0.0000	6.416	0.7801	-2.998	0.0027	0.2685	-1.8968	18.64	5.000	8.807	3.123e+02	0.3178	0.0000	0.0000	0.0000	-1.8410	10.410	0.0013	-3.0790	-6.859	8.631	0.0000	6.246e-07	1.464e-02	8.343e-03	0.000e+00	2.135e-09	-2.737	1.446e-03	-5.285e-04	1.326e-03	1.855e-06
LPAL13_000038400	LPAL13_000038400	expression-site associated gene (esag3), putative	protein coding	LPAL13_SCAF000573	101	1360	+	forward	Not Assigned	1260.0	1259	-2.765	0.0000	-2.770	0.0000	-3.929	0.0000	4.4930	8.2090	3.4271	0.0364	-3.716	-11.030	0.000	0.0000	3380.000	0.5381	-5.138	0.0000	0.1798	-2.4753	9214.88	1657.147	3766.282	1.711e+07	0.1852	0.0000	0.0000	0.0000	7.1120	32.140	0.0000	5.7960	-5.888	7.804	0.0000	9.491e-06	1.189e-05	1.301e-06	0.000e+00	1.467e-09	-3.225	3.470e-02	-1.076e-02	1.202e-07	1.930e-14
LPAL13_000038500	LPAL13_000038500	hypothetical protein	protein coding	LPAL13_SCAF000575	39	251	+	forward	Not Assigned	213.0	212	-2.121	0.0035	-2.122	0.0029	-3.738	0.0000	-2.3030	1.3340	5.2036	0.6833	-3.638	-7.672	0.000	0.0000	26.610	0.6026	-3.520	0.0004	0.2673	-1.9035	78.06	20.858	36.822	2.446e+03	0.2929	0.0000	0.0000	0.0000	0.1404	12.980	0.0003	-1.3510	-6.185	7.971	0.0000	3.998e-06	3.577e-03	3.128e-03	0.000e+00	1.852e-07	-2.640	5.880e-02	-2.227e-02	2.486e-04	4.976e-08
LPAL13_000012100	LPAL13_000012100	hypothetical protein	protein coding	LPAL13_SCAF000080	1637	1894	-	reverse	Not Assigned	258.0	257	-1.856	0.0220	-1.857	0.0148	-3.524	0.0001	-2.3030	1.1290	6.1207	0.6501	-3.431	-6.841	0.000	0.0000	28.960	0.6589	-2.817	0.0049	0.2846	-1.8128	67.24	19.131	32.556	1.831e+03	0.3140	0.0044	0.9956	0.0044	0.2662	9.014	0.0027	-1.2890	-5.297	4.881	0.0000	5.231e-05	2.451e-02	1.576e-02	9.864e-01	1.721e-06	-2.230	1.642e-01	-7.365e-02	2.511e-03	5.907e-06
LPAL13_350063000	LPAL13_350063000	hypothetical protein	protein coding	LpaL13_35	1964328	1964543	-	reverse	35	216.0	215	-2.559	0.0000	-2.543	0.0000	-3.538	0.0000	-2.2250	1.1420	2.2723	0.2255	-3.367	-11.090	0.000	0.0000	19.820	0.4499	-5.688	0.0000	0.1795	-2.4782	56.77	10.179	23.180	6.353e+02	0.1953	0.0000	1.0000	0.0000	-0.2880	35.320	0.0000	-1.3500	-7.436	12.440	0.0000	7.763e-08	1.032e-06	4.255e-07	9.837e-01	1.087e-10	-2.909	3.081e-02	-1.059e-02	5.244e-09	4.549e-17
LPAL13_310031300	LPAL13_310031300	hypothetical protein, conserved	protein coding	LpaL13_31	1084772	1085059	-	reverse	31	288.0	287	-2.167	0.0014	-2.154	0.0005	-3.643	0.0000	-1.1870	2.1410	4.7045	0.7839	-3.328	-7.142	0.000	0.0000	58.050	0.5669	-3.824	0.0001	0.2604	-1.9414	145.08	37.766	67.713	6.939e+03	0.2781	0.0017	0.9983	0.0017	1.2390	17.170	0.0000	-0.1278	-5.796	7.053	0.0000	1.234e-05	1.591e-03	5.510e-04	9.461e-01	7.219e-07	-2.655	1.068e-02	-4.023e-03	5.529e-05	4.648e-09
LPAL13_340039600	LPAL13_340039600	hypothetical protein	protein coding	LpaL13_34	1247554	1247757	-	reverse	34	204.0	203	-2.051	0.0007	-2.057	0.0002	-3.258	0.0000	1.2010	4.2610	3.9576	0.1030	-3.060	-8.289	0.000	0.0000	217.400	0.5061	-4.054	0.0001	0.2144	-2.2213	583.13	125.041	252.879	5.809e+04	0.2195	0.0000	1.0000	0.0000	3.1390	19.420	0.0000	2.0330	-5.351	5.772	0.0000	4.394e-05	7.642e-04	2.040e-04	9.837e-01	1.314e-07	-2.392	8.856e-03	-3.703e-03	2.053e-05	6.950e-10
LPAL13_310031000	LPAL13_310031000	hypothetical protein, conserved	protein coding	LpaL13_31	1075172	1075459	-	reverse	31	288.0	287	-2.295	0.0000	-2.280	0.0000	-3.464	0.0000	-1.9610	0.9507	3.4017	0.5352	-2.912	-7.413	0.000	0.0000	19.960	0.4292	-5.346	0.0000	0.2853	-1.8096	57.60	16.423	27.913	1.029e+03	0.3065	0.3131	0.6869	0.3131	-0.1515	32.140	0.0000	-1.3750	-8.102	14.910	0.0000	2.353e-08	5.107e-06	1.361e-06	6.903e-01	3.619e-07	-2.666	3.846e-03	-1.442e-03	3.477e-08	2.336e-15
LPAL13_050005000	LPAL13_050005000	hypothetical protein	protein coding	LpaL13_05	3394	3612	-	reverse	5	219.0	218	-1.866	0.0063	-1.872	0.0028	-2.904	0.0001	-0.0969	2.6670	3.2891	0.1597	-2.764	-7.999	0.000	0.0000	85.410	0.5630	-3.315	0.0009	0.2818	-1.8274	188.15	53.009	90.723	7.118e+03	0.2904	0.0000	1.0000	0.0000	1.7980	13.070	0.0003	0.5391	-5.289	5.442	0.0000	5.260e-05	6.475e-03	2.996e-03	9.837e-01	1.589e-07	-2.191	1.613e-02	-7.365e-03	4.060e-04	2.186e-07
LPAL13_310039200	LPAL13_310039200	hypothetical protein	protein coding	LpaL13_31	1301745	1301972	-	reverse	31	228.0	227	-2.301	0.0000	-2.306	0.0000	-2.812	0.0000	1.3090	3.7370	1.5959	0.2098	-2.428	-9.247	0.000	0.0000	176.700	0.4034	-5.705	0.0000	0.2858	-1.8069	411.30	117.541	199.519	3.648e+04	0.2969	0.1929	0.8071	0.1929	2.8660	39.350	0.0000	2.0740	-6.830	11.860	0.0000	6.265e-07	1.038e-06	9.147e-08	7.975e-01	4.626e-09	-2.573	6.751e-02	-2.624e-02	4.329e-09	3.987e-17
LPAL13_140019300	LPAL13_140019300	bt1 family, putative	protein coding	LpaL13_14	530784	531350	+	forward	14	567.0	566	-2.510	0.0000	-2.513	0.0000	-2.614	0.0000	4.6760	7.0960	0.6202	1.0309	-2.420	-7.436	0.000	0.0000	1816.000	0.3720	-6.748	0.0000	0.1845	-2.4381	4939.72	911.476	2035.637	5.489e+06	0.1903	0.0000	1.0000	0.0000	6.2150	54.810	0.0000	5.4710	-6.779	11.630	0.0000	7.374e-07	4.947e-09	2.095e-10	9.839e-01	3.561e-05	-2.653	2.149e-01	-8.103e-02	4.350e-10	5.483e-19
LPAL13_290026200	LPAL13_290026200	hypothetical protein	protein coding	LpaL13_29	864413	864616	+	forward	29	204.0	203	-1.213	0.2383	-1.208	0.2126	-1.935	0.0251	-2.6990	-0.5708	6.4336	4.6401	-2.128	-2.761	0.011	0.0401	17.660	0.7810	-1.553	0.1204	0.5295	-0.9173	41.11	21.762	27.161	2.397e+03	0.5777	0.0298	0.9702	0.0298	-0.5283	2.692	0.1009	-2.6160	-2.886	-2.199	0.0049	2.508e-02	2.428e-01	2.274e-01	9.689e-01	2.852e-02	-1.453	2.977e-02	-2.048e-02	7.540e-02	3.823e-03
LPAL13_340039700	LPAL13_340039700	snare domain containing protein, putative	protein coding	LpaL13_34	1248192	1248947	-	reverse	34	756.0	755	-1.719	0.0000	-1.725	0.0000	-2.070	0.0000	4.6020	6.7060	0.7166	0.0945	-2.104	-11.950	0.000	0.0000	1294.000	0.3254	-5.284	0.0000	0.2740	-1.8679	3160.85	866.003	1506.426	1.487e+06	0.2781	0.0000	1.0000	0.0000	5.7220	34.070	0.0000	5.2630	-6.365	9.800	0.0000	2.306e-06	5.995e-06	6.924e-07	9.457e-01	3.673e-11	-1.837	5.236e-03	-2.851e-03	4.673e-08	4.775e-15
LPAL13_050016500	LPAL13_050016500	unspecified product	snoRNA encoding	LpaL13_05	undefined	undefined	+	forward	5	0.0	undefined	-1.016	0.1700	-1.019	0.1707	-2.380	0.0010	-1.4980	0.6056	3.5984	1.0071	-2.103	-4.703	0.000	0.0005	25.390	0.5724	-1.775	0.0759	0.6736	-0.5700	46.03	31.000	35.194	3.534e+03	0.7127	0.6951	0.3049	0.6951	0.1057	3.190	0.0741	-1.1770	-4.210	1.357	0.0001	1.044e-03	1.925e-01	1.708e-01	3.502e-01	4.953e-04	-1.401	2.983e-01	-2.130e-01	5.002e-02	1.873e-03
LPAL13_350073400	LPAL13_350073400	hypothetical protein	protein coding	LpaL13_35	2342701	2342883	+	forward	35	183.0	182	-1.554	0.0005	-1.552	0.0007	-2.039	0.0003	-0.3040	1.7540	1.1191	0.8306	-2.058	-6.341	0.000	0.0001	43.080	0.3775	-4.116	0.0000	0.2995	-1.7395	115.91	34.704	57.366	6.357e+03	0.3249	0.0001	0.9999	0.0001	0.8098	16.630	0.0000	0.1382	-4.713	3.261	0.0000	2.544e-04	6.145e-04	6.959e-04	9.877e-01	4.784e-05	-1.712	4.240e-04	-2.476e-04	3.099e-05	3.748e-10
LPAL13_140019100	LPAL13_140019100	bt1 family, putative	protein coding	LpaL13_14	525164	525514	+	forward	14	351.0	350	-2.003	0.0000	-2.005	0.0000	-2.191	0.0000	3.9600	6.0150	0.4574	0.5082	-2.055	-8.601	0.000	0.0000	858.600	0.2946	-6.800	0.0000	0.2486	-2.0079	2079.27	516.963	952.955	7.515e+05	0.2529	0.0000	1.0000	0.0000	5.1340	56.280	0.0000	4.6820	-7.777	16.160	0.0000	3.613e-08	4.149e-09	1.412e-10	9.462e-01	2.947e-06	-2.142	5.201e-02	-2.428e-02	7.718e-12	4.514e-23
LPAL13_170015400	LPAL13_170015400	hypothetical protein, conserved	protein coding	LpaL13_17	395975	396307	+	forward	17	333.0	332	-1.856	0.0000	-1.858	0.0000	-2.241	0.0000	1.2050	3.2550	1.2016	0.1241	-2.051	-9.255	0.000	0.0000	140.300	0.3158	-5.878	0.0000	0.3282	-1.6075	285.57	93.708	147.249	1.387e+04	0.3329	0.0000	1.0000	0.0000	2.5240	37.910	0.0000	2.0000	-6.406	10.020	0.0000	2.301e-06	4.439e-07	1.771e-07	9.505e-01	4.813e-09	-2.008	5.444e-04	-2.711e-04	3.957e-09	9.756e-18
LPAL13_350013200	LPAL13_350013200	hypothetical protein, conserved	protein coding	LpaL13_35	223837	224070	+	forward	35	234.0	233	-1.172	0.0087	-1.166	0.0108	-1.919	0.0001	-2.0980	-0.2277	1.1372	0.7142	-1.870	-6.030	0.000	0.0001	10.050	0.3668	-3.195	0.0014	0.3700	-1.4343	26.02	9.623	14.199	2.850e+02	0.3934	0.6600	0.3400	0.6600	-1.1170	9.796	0.0017	-1.9060	-4.911	3.190	0.0000	1.487e-04	8.718e-03	1.073e-02	3.895e-01	6.322e-05	-1.389	3.277e-02	-2.360e-02	1.050e-03	8.518e-07

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)

6.4 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 16 go_db genes and 54 length_db genes out of 54.

## Testing that go categories are defined.

## Removing undefined categories.

## Gathering synonyms.

## Gathering category definitions.

## 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 14 go_db genes and 69 length_db genes out of 69.

## Testing that go categories are defined.

## Removing undefined categories.

## Gathering synonyms.

## Gathering category definitions.

6.4.1 A couple plots from the differential expression

6.4.1.1 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"]]

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

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

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

zy_go_up$pvalue_plots$bpp_plot_over

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

zy_go_down$pvalue_plots$bpp_plot_over

6.5 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 = -184, df = 8555, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.8974 -0.8889
## sample estimates:
##     cor 
## -0.8932

6.6 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:

ALAT: LPAL13_120010900 – alanine aminotransferase
ASAT: LPAL13_340013000 – aspartate aminotransferase
G6PD: LPAL13_000054100 – glucase-6-phosphate 1-dehydrogenase
NH: LPAL13_14006100, LPAL13_180018500 – inosine-guanine nucleoside hydrolase
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.

6.6.1 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")

## Before removal, there were 8557 genes, now there are 6.

## There are 82 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##   0.13106   0.12479   0.13215   0.10580   0.12996   0.11000   0.11284   0.11633 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##   0.11542   0.11799   0.11468   0.11350   0.10977   0.10591   0.11017   0.12058 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##   0.12051   0.10631   0.10890   0.11456   0.12238   0.11248   0.11007   0.10617 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##   0.13062   0.11243   0.12016   0.11617   0.11258   0.13845   0.10014   0.12008 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##   0.12254   0.13478   0.12666   0.12325   0.13409   0.11733   0.13147   0.11798 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##   0.14327   0.11013   0.10303   0.12930   0.10841   0.12191   0.12067   0.11666 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##   0.11811   0.11035   0.11376   0.11102   0.12824   0.11529   0.13949   0.12848 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##   0.13862   0.11534   0.11355   0.12380   0.12766   0.12197   0.13684   0.11544 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##   0.11171   0.13383   0.12207   0.12673   0.11631   0.11659   0.11887   0.11890 
## TMRC20092 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 TMRC20081 
##   0.11560   0.12202   0.11059   0.12329   0.11720   0.10982   0.09643   0.10454 
## TMRC20106 TMRC20095 
##   0.09806   0.07967

zymo_heatmap <- plot_sample_heatmap(zymo_expt, row_label = my_names)
zymo_heatmap

6.7 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_000012100" "LPAL13_000038500"
##  [5] "LPAL13_000038400" "LPAL13_310031300" "LPAL13_340039600" "LPAL13_050005000"
##  [9] "LPAL13_310035500" "LPAL13_350063000" "LPAL13_310039200" "LPAL13_310031000"
## [13] "LPAL13_140019300" "LPAL13_340039700" "LPAL13_210015500" "LPAL13_180013900"
## [17] "LPAL13_290026200" "LPAL13_170015400" "LPAL13_250006300" "LPAL13_350073400"
## [21] "LPAL13_140019100" "LPAL13_350013200" "LPAL13_330024000" "LPAL13_230016400"
## [25] "LPAL13_190021800" "LPAL13_000025600" "LPAL13_050016500" "LPAL13_240009700"
## [29] "LPAL13_350012400" "LPAL13_320038700" "LPAL13_140019200" "LPAL13_210005000"
## [33] "LPAL13_270034100" "LPAL13_330021800" "LPAL13_250025700" "LPAL13_160014500"
## [37] "LPAL13_350073200" "LPAL13_230011200" "LPAL13_330021900" "LPAL13_000029000"
## [41] "LPAL13_000052700" "LPAL13_040007800" "LPAL13_160014100" "LPAL13_230011500"
## [45] "LPAL13_050009600" "LPAL13_350044000" "LPAL13_310032500" "LPAL13_230021300"
## [49] "LPAL13_310028500" "LPAL13_230011400" "LPAL13_000045100" "LPAL13_110015700"
## [53] "LPAL13_020006700" "LPAL13_230011300"

upshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(up_shared), method = "keep")

## Before removal, there were 8557 genes, now there are 54.

## There are 82 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.4022    0.4755    0.1522    0.4940    0.2219    0.5758    0.6687    0.3800 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.4351    0.1991    0.5105    0.1854    0.4787    0.3959    0.1980    0.1569 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.4504    0.2511    0.2042    0.4298    0.1761    0.4740    0.1812    0.4677 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.1619    0.2123    0.2509    0.2256    0.2379    0.1455    0.1369    0.2264 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##    0.6241    0.2358    0.6004    0.6076    0.2670    0.5106    0.2142    0.8190 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##    0.2498    0.3678    0.4334    0.2370    0.1527    0.1542    0.2969    0.2037 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##    0.2452    0.5647    0.5705    0.3901    0.7473    0.2485    0.2185    0.7600 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##    0.2046    0.5946    0.5257    0.1767    0.6792    0.5156    0.1920    0.4461 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##    0.6512    0.2151    0.6212    0.1516    0.4455    0.1965    0.1640    0.1785 
## TMRC20092 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 TMRC20081 
##    0.1704    0.5580    0.5427    0.1727    0.6571    0.1335    0.3428    0.1390 
## TMRC20106 TMRC20095 
##    0.1648    0.3513

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

6.7.1 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

6.7.2 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")

## Before removal, there were 8557 genes, now there are 69.

## There are 82 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.2261    0.1929    0.6787    0.2301    0.6685    0.2192    0.2085    0.2431 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.2056    0.6963    0.1930    0.6481    0.1728    0.2123    0.5794    0.5614 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1713    0.6687    0.6606    0.1685    0.6627    0.1884    0.6924    0.1596 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.6842    0.7270    0.6946    0.7052    0.7336    0.7036    0.6256    0.6185 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 
##    0.1958    0.7237    0.1925    0.1740    0.5447    0.1900    0.5565    0.2265 
## TMRC20072 TMRC20059 TMRC20048 TMRC20088 TMRC20060 TMRC20077 TMRC20074 TMRC20063 
##    0.5573    0.1440    0.1603    0.6268    0.7714    0.5807    0.6744    0.6443 
## TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 
##    0.5808    0.1824    0.1988    0.1857    0.1962    0.6232    0.7064    0.1942 
## TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20089 
##    0.5611    0.1575    0.1765    0.6377    0.2071    0.1641    0.6399    0.1561 
## TMRC20090 TMRC20044 TMRC20105 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 
##    0.1757    0.6315    0.1857    0.6987    0.1985    0.7427    0.5672    0.6418 
## TMRC20092 TMRC20099 TMRC20100 TMRC20087 TMRC20104 TMRC20086 TMRC20107 TMRC20081 
##    0.5959    0.1843    0.1557    0.5600    0.2037    0.7286    0.1433    0.6779 
## TMRC20106 TMRC20095 
##    0.9969    0.2280

high_22_heatmap <- plot_sample_heatmap(downshared_expt, row_label = rownames(down_shared))
high_22_heatmap

7 SNP profiles

Now I will combine our previous samples and our new samples in the hopes of finding variant positions which help elucidate currently unknown aspects of either group via their clustering to known samples from the other group. In other words, we do not know the zymodeme annotations for the old samples nor the strain identities (or the shortcut ‘chronic vs. self-healing’) for the new samples. I hope to make educated guesses given the variant profiles. There are some differences in how the previous and current data sets were analyzed (though I have since redone the old samples so it should be trivial to remove those differences now).

I added our 2016 data to a specific TMRC2 sample sheet, dated 20191203. Thus I will load the data here. That previous data was mapped using tophat, so I will also need to make some changes to the gene names to accomodate the two mappings.

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 they were 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 rows and 33 columns.

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)

7.1 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)[['bcftable']])")

## subset_expt(): There were 100, now there are 67 samples.

new_snps <- sm(count_expt_snps(lp_snp, annot_column = "bcftable"))
old_snps <- sm(count_expt_snps(old_expt, annot_column = "bcftable", snp_column = 2))

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

## tmrc20001 tmrc20065 tmrc20005 tmrc20007 tmrc20008 tmrc20027 tmrc20028 tmrc20032 
##     31443     73441      1965      2133      2786    291022    290524    116072 
## tmrc20040 tmrc20066 tmrc20039 tmrc20037 tmrc20038 tmrc20067 tmrc20068 tmrc20041 
##     34648     69275      4048     79861     80805     75518     74157     34972 
## tmrc20015 tmrc20009 tmrc20010 tmrc20016 tmrc20011 tmrc20012 tmrc20013 tmrc20017 
##     95041      7410     87192     95605      5297        10     89292      6689 
## tmrc20014 tmrc20018 tmrc20019 tmrc20070 tmrc20020 tmrc20021 tmrc20022 tmrc20025 
##      6440     82235      3021     78538      3209     88148      2608    279253 
## tmrc20024 tmrc20036 tmrc20069 tmrc20033 tmrc20026 tmrc20031 tmrc20073 tmrc20055 
##      4981     32060      3304      4443      2851      3150     78772      2819 
## tmrc20079 tmrc20071 tmrc20078 tmrc20042 tmrc20058 tmrc20072 tmrc20059 tmrc20048 
##     78013     74977      3287      2490     78849     31425     77883     76892 
## tmrc20057 tmrc20056 tmrc20060 tmrc20077 tmrc20063 tmrc20053 tmrc20052 tmrc20064 
##     32003      2977      2770      3091      1592      2836     77467     75335 
## tmrc20051 tmrc20050 tmrc20062 tmrc20080 tmrc20043 tmrc20054 tmrc20046 tmrc20047 
##     75845      3399     75667     81666     77526     77155     31433     75963 
## tmrc20044 tmrc20045 tmrc20061 
##      3132     30107     96058

## 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", convert = "cpm", filter = TRUE)

## Removing 0 low-count genes (670128 remaining).

## transform_counts: Found 61643658 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.

7.2 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).

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

## The factor z2.3 has 27 rows.

## The factor z2.2 has 28 rows.

## The factor unknown has 4 rows.

## The factor z1.0 has only 1 row.

## The factor z3.0 has only 1 row.

## The factor z2.0 has only 1 row.

## The factor z2.1 has 3 rows.

## The factor z2.4 has only 1 row.

## The factor null has only 1 row.

## The factor sh has 13 rows.

## The factor chr has 14 rows.

## The factor inf has 6 rows.

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 <- sm(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

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

7.3 SNPS associated with clinical response in the TMRC samples

clinical_sets <- get_snp_sets(new_snps, factor = "clinicalresponse")

## The factor cure has 26 rows.

## The factor failure has 21 rows.

## The factor laboratory line has only 1 row.

## The factor laboratory line miltefosine resistant has only 1 row.

## The factor nd has 14 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)

7.3.1 Cross reference these variants by gene

clinical_genes <- sm(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"]])
cure_snps <- as.data.frame(clinical_snps[["inters"]][["cure"]])

head(fail_ref_snps)

##                                           seqnames  start    end width strand
## chr_LpaL13-10_pos_327353_ref_T_alt_C     LpaL13-10 327353 327354     2      +
## chr_LpaL13-13_pos_167047_ref_G_alt_C     LpaL13-13 167047 167048     2      +
## chr_LpaL13-15_pos_42885_ref_A_alt_G      LpaL13-15  42885  42886     2      +
## chr_LpaL13-20.1_pos_111781_ref_T_alt_C LpaL13-20.1 111781 111782     2      +
## chr_LpaL13-20.1_pos_85158_ref_C_alt_G  LpaL13-20.1  85158  85159     2      +
## chr_LpaL13-20.2_pos_48545_ref_T_alt_C  LpaL13-20.2  48545  48546     2      +

head(cure_snps)

##                                           seqnames  start    end width strand
## chr_LpaL13-08_pos_184791_ref_T_alt_A     LpaL13-08 184791 184792     2      +
## chr_LpaL13-20.1_pos_369935_ref_C_alt_T LpaL13-20.1 369935 369936     2      +
## chr_LpaL13-20.1_pos_370282_ref_C_alt_T LpaL13-20.1 370282 370283     2      +
## chr_LpaL13-20.1_pos_371356_ref_T_alt_C LpaL13-20.1 371356 371357     2      +
## chr_LpaL13-20.1_pos_380785_ref_A_alt_G LpaL13-20.1 380785 380786     2      +
## chr_LpaL13-20.1_pos_382801_ref_A_alt_C LpaL13-20.1 382801 382802     2      +

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

8 Zymodeme for new samples

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

8.1 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 67, now there are 55 samples.

new_sets <- get_snp_sets(pruned_snps, factor = "zymodemecategorical")

## The factor z22 has 28 rows.

## The factor z23 has 27 rows.

summary(new_sets)

##               Length Class      Mode     
## medians         3    data.frame list     
## possibilities   2    -none-     character
## intersections   3    -none-     list     
## chr_data      695    -none-     list     
## set_names       4    -none-     list     
## invert_names    4    -none-     list     
## density       695    -none-     numeric

## 1000000: 2.2
## 0100000: 2.3

summary(new_sets[["intersections"]][["10"]])

##    Length     Class      Mode 
##       890 character character

summary(new_sets[["intersections"]][["01"]])

##    Length     Class      Mode 
##     76189 character character

Thus we see that there are 511 variants associated with 2.2 and 49,790 associated with 2.3.

8.1.1 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!

8.1.2 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

8.2 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 100, now there are 82 samples.

pheno <- subset_expt(pheno, subset = "!is.na(pData(pheno)[['bcftable']])")

## subset_expt(): There were 82, 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)

8.3 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")

8.4 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.

## pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
snp_genes <- sm(snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"))

##new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE)
##new_zymo_norm <- set_expt_conditions(new_zymo_norm, fact = "zymodemecategorical")
clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE) %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  set_expt_colors(clinical_colors_v2)

## Removing 0 low-count genes (568627 remaining).

## transform_counts: Found 28953155 values equal to 0, adding 1 to the matrix.

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"]]

8.4.1 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))

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 29 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%)
 Multraits-Rectangular Plotting...(finished 90%)
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 Multraits-Rectangular Plotting...(finished 93%)
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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

$Circular Manhattan$ Rectangular Manhattan

8.5 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.

covariates.txt: Columns are samples, rows are things from pData – the most likely ones of interest for our data would be zymodeme, sensitivity
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.
ge.txt: This appears to be a log(rpkm/cpm) table with rows as genes and columns as samples
snpsloc.txt: columns are ‘snpid’, ‘chr’, ‘pos’
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 64dc311322615b4a8bfef4517ab3c4ba0d0b0e59

## This is hpgltools commit: Mon Apr 4 16:43:54 2022 -0400: 64dc311322615b4a8bfef4517ab3c4ba0d0b0e59

## Saving to tmrc2_02sample_estimation_v202203.rda.xz

tmp <- loadme(filename = savefile)

---
title: "TMRC2 Comprehensive Data Analysis: 202201"
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 <- "202203"
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_202203.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))

hisat_annot <- all_lp_annot
```

# 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}
sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "zymodemecategorical")
lp_expt <- create_expt(sample_sheet,
                       gene_info = hisat_annot,
                       annotation = orgdb,
                       id_column = "hpglidentifier",
                       file_column = "lpanamensisv36hisatfile") %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  subset_expt(nonzero = 8550) %>%
  subset_expt(coverage = 5000000) %>%
  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(file = "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}
starting <- as.numeric(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])
sus_categorical <- starting
na_idx <- is.na(starting)
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"

pData(lp_expt)[["sus_category"]] <- sus_categorical
table(sus_categorical)
```

```{r pre_questions}
clinical_colors <- list(
    "z1.0" = "#333333",
    "z2.0" = "#555555",
    "z3.0" = "#777777",
    "z2.1" = "#874400",
    "z2.2" = "#0000cc",
    "z2.3" = "#cc0000",
    "z2.4" = "#df7000",
    "unknown" = "#cbcbcb",
    "null" = "#000000")
clinical_samples <- lp_expt %>%
  set_expt_batches(fact = sus_categorical) %>%
  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) %>%
  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 <- normalize_expt(cf_expt, convert = "cpm", transform = "log2",
                        norm = "quant", 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" = "#000000")
sus_expt <- set_expt_conditions(lp_expt, fact = "sus_category") %>%
  set_expt_batches(fact = "zymodemecategorical") %>%
  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"),
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt"))
zy_sig_nobatch <- extract_significant_genes(
    zy_table_nobatch,
    excel = glue::glue("excel/zy_sig_nobatch-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"),
    gmt = glue::glue("gmt/zymodeme_sva-v{ver}.gmt"))
zy_sig_sva <- extract_significant_genes(
    zy_table_sva,
    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_de <- all_pairwise(cf_expt, 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, 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, excel = glue::glue("excel/sus_sig_nobatch-v{ver}.xlsx"))
```

```{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
```

## 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
```

## 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
```

# SNP profiles

Now I will combine our previous samples and our new samples in the
hopes of finding variant positions which help elucidate currently
unknown aspects of either group via their clustering to known samples
from the other group. In other words, we do not know the zymodeme
annotations for the old samples nor the strain identities (or the
shortcut 'chronic vs. self-healing') for the new samples. I hope to
make educated guesses given the variant profiles. There are some
differences in how the previous and current data sets were analyzed
(though I have since redone the old samples so it should be trivial to
remove those differences now).

I added our 2016 data to a specific TMRC2 sample sheet,
dated 20191203.  Thus I will load the data here.  That previous data
was mapped using tophat, so I will also need to make some changes to
the gene names to accomodate the two mappings.

```{r oldnew_variants}
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)[['bcftable']])")
new_snps <- sm(count_expt_snps(lp_snp, annot_column = "bcftable"))
old_snps <- sm(count_expt_snps(old_expt, annot_column = "bcftable", snp_column = 2))

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

## 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", convert = "cpm", filter = TRUE)

## 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}
old_new_variant_heatmap <- plot_disheat(both_norm)
dev <- pp(file = "images/raw_snp_disheat.png", height=12, width=12)
old_new_variant_heatmap$plot
closed <- dev.off()
old_new_variant_heatmap$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}
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 <- sm(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
```

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 <- sm(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"]])
cure_snps <- as.data.frame(clinical_snps[["inters"]][["cure"]])

head(fail_ref_snps)
head(cure_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"]])
summary(new_sets[["intersections"]][["01"]])
```

Thus we see that there are 511 variants associated with 2.2 and 49,790 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}
## pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
snp_genes <- sm(snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"))

##new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE)
##new_zymo_norm <- set_expt_conditions(new_zymo_norm, fact = "zymodemecategorical")
clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE) %>%
  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))

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)
```


TMRC2 Comprehensive Data Analysis: 202201

atb abelew@gmail.com

2022-04-07