TMRC2 Analyses

1 Introduction

This document intends to provide finalized analyses of the TMRC2 samples.

These are sequences from an array of Leishmania strains collected over time by CIDEIM. My primary goal in these analyses is to look for potential differences among them which have the potential to elucidate different outcomes for patients, drug sensitivity, and any other aspects which may prove helpful.

1.1 Relevant Metadata

The metadata factors of most interest in this group are:

1. Pathogen/Pathogen strain: I think this may often be a number which refers to the person from whom the parasite was taken. In a few cases it is not Leishmania panamensis; but in a vast majority of cases it is panamensis and explicitly not the Panama strain.
2. Clinical response: Usually this is Cure/Failure, referring to what happened to the patient. This is of course confounded because different infected with the same strain may respond differently. Some samples have not yet been determined (ND), and there are a few reference strains, including some which are known to be resistant to the antimonial drug treatment and/or miltefosine.
3. The zymodeme categorical: This uses the phenotyping characterizations as defined in (10.4269/ajtmh.1998.59.86). My understanding of the method is that the authors used electrophoretic mobility shifts among a set of 13 proteins (GPI, ES, PEP-1, PGM, ACP, PEP-D, ALAT, ASAT, MPI, G6PD, NH, 6PGD, SOD) in order to define 17 categories of Leishmania Viannia. A significant portion of the strains collected by CIDEIM are either 2.1, 2.2, or 2.3 (tables 4 and 5), and in this study almost all strains are either 2.2 or 2.3.
4. Susceptibility: This is provided as a percentage of parasites cleared when treated with 32 ug/mL Sb(V). There is one column for a previous screening, and one for a newer screening.
5. Passage number: In theory this would be very interesting for the mutation analyses later on in this document, but it appears that pretty much every strain listed is on its second passage.

1.2 Libraries, sequencing, mapping, quantification

The samples used in these analyses were all collected, purified, and libraries generated by the scientists/doctors at CIDEIM. The sequencing libraries were generated via the TruSeq non-stranded library kit and sequenced either at JHU or UMD; earlier samples were single-ended, but most were paired.

All samples were trimmed with trimomatic using the same set of parameters. All mapping was performed with hisat2 version 2.2.1. Quantifications were also performed with salmon version 1.2.0. The reference genome used was L.panamensis (MHOM/COL/81/L13), the TriTrypDB version 36 reference was used.

1.3 Types of analyses included

This document is limited primary to a series of RNAseq analyses to look for variance among the metadata factors of primary interest. It also includes a series of pseudo-variant searches which seek to provide some clarification vis a vis the relative lineages of these strains.

2 Metadata collection

The primary metadata source for this experiment comes from our shared online sample sheet, which has been copied to sample_sheets/tmrc2_samples.xlsx. In our working repository we have multiple versions of this which change over time.

sample_sheet <- "sample_sheets/tmrc2_samples.xlsx"

3 Annotation Collection

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
## rownames(hisat_annot) <- paste0("exon_", rownames(hisat_annot), ".E1")

4 Load a genome

meta <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")

## Unable to find species names for 2 species.

## Leishmania sp. Ghana MHOM/GH/2012/GH5, Leishmania sp. Namibia MPRO/NA/1975/252/LV425

## Appending to an existing file: EuPathDB/metadata/biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/GRanges_biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/OrgDb_biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/TxDb_biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/OrganismDbi_biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/BSgenome_biocv3.14_tritrypdbv56_metadata.csv

## Appending to an existing file: EuPathDB/metadata/biocv3.14_tritrypdbv56_invalid_metadata.csv

## Appending to an existing file: EuPathDB/metadata/GRanges_biocv3.14_tritrypdbv56_invalid_metadata.csv

## Appending to an existing file: EuPathDB/metadata/OrgDb_biocv3.14_tritrypdbv56_invalid_metadata.csv

## Appending to an existing file: EuPathDB/metadata/TxDb_biocv3.14_tritrypdbv56_invalid_metadata.csv

## Appending to an existing file: EuPathDB/metadata/OrganismDbi_biocv3.14_tritrypdbv56_invalid_metadata.csv

## Appending to an existing file: EuPathDB/metadata/BSgenome_biocv3.14_tritrypdbv56_invalid_metadata.csv

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))

5 TODO:

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

6 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.

6.1 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’

6.2 TODO:

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

sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "zymodemecategorical")
lp_expt <- sm(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")

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

## TMRC20002 TMRC20006 
##  11681227   6670348

## subset_expt(): There were 75, now there are 73 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 73, now there are 71 samples.

## semantic_expt_filter(): Removed 68 genes.

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

## Warning in pp(file = "images/lp_expt_libsizes.png", image = libsizes$plot, :
## There is no device to shut down.

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

## Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

## Warning in pp(file = "images/lp_nonzero.png", image = nonzero$plot, width = 9, :
## There is no device to shut down.

## Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

lp_box <- plot_boxplot(lp_expt)

## 5364 entries are 0.  We are on a log scale, adding 1 to the data.

pp(file = "images/lp_expt_boxplot.png", image = lp_box, width = 12, height = 9)

## Warning in pp(file = "images/lp_expt_boxplot.png", image = lp_box, width = 12, :
## There is no device to shut down.

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

6.3 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’.

6.4 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

clinical_colors <- list(
    ##    "z2.1" = "#0000cc",
    ##    "z2.3" = "#874400",
    ##    "z2.2" = "#df7000",
    ##    "z2.4" = "#cc0000",
    "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)

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

## Warning in pp(file = "images/zymo_pca_sus_shape.png", image = zymo_pca$plot):
## There is no device to shut down.

only_two_types <- subset_expt(clinical_samples, subset = "condition=='z2.3'|condition=='z2.2'")

## subset_expt(): There were 71, now there are 59 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)
pp(file = "images/zymo_z2.2_z2.3_pca_sus_shape.pdf", image = 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")

## Removing 142 low-count genes (8568 remaining).

## batch_counts: Before batch/surrogate estimation, 1008 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 3380 entries are 0<x<1: 1%.

## Setting 370 low elements to zero.

## transform_counts: Found 370 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)
pp(file = "images/clinical_nb_pca_sus_shape.png", image = clinical_nb_pca$plot)

## Warning in pp(file = "images/clinical_nb_pca_sus_shape.png", image =
## clinical_nb_pca$plot): There is no device to shut down.

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

## Warning: ggrepel: 43 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.

6.5 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.

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

## Removing 142 low-count genes (8568 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)
pp(file = "images/cf_sus_shape.png", image = start_cf$plot)

## Warning in pp(file = "images/cf_sus_shape.png", image = start_cf$plot): There is
## no device to shut down.

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 142 low-count genes (8568 remaining).

## batch_counts: Before batch/surrogate estimation, 2 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 4130 entries are 0<x<1: 1%.

## Setting 154 low elements to zero.

## transform_counts: Found 154 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)
pp(file = "images/cf_sus_share_nb.png", image = cf_nb_pca$plot)

## Warning in pp(file = "images/cf_sus_share_nb.png", image = cf_nb_pca$plot):
## There is no device to shut down.

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

## Removing 142 low-count genes (8568 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 2.850e-01 0.3311343 0.47800 1.728e-03 0.6934731 0.4209
## zymodemecategorical 4.608e-08 0.0009469 0.33206 1.487e-02 0.0009543 0.2642
## pathogenstrain      7.092e-01 0.7763033 0.84356 4.512e-06 0.0181051 0.5619
## passagenumber       8.896e-01 0.2377294 0.04096 2.795e-02 0.2229372 0.4130

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 71, now there are 70 samples.

## subset_expt(): There were 70, now there are 67 samples.

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

## Removing 143 low-count genes (8567 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)
pp(file = "images/sus_norm_pca.png", image = sus_pca[["plot"]])

## Warning in pp(file = "images/sus_norm_pca.png", image = sus_pca[["plot"]]):
## There is no device to shut down.

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

## Removing 143 low-count genes (8567 remaining).

## batch_counts: Before batch/surrogate estimation, 939 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 3146 entries are 0<x<1: 1%.

## Setting 208 low elements to zero.

## transform_counts: Found 208 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)
pp(file = "images/sus_nb_pca.png", image = sus_nb_pca[["plot"]])

## Warning in pp(file = "images/sus_nb_pca.png", image = sus_nb_pca[["plot"]]):
## There is no device to shut down.

7 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.

7.1 Differential expression

7.1.1 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 71, now there are 59 samples.

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

## Removing 159 low-count genes (8551 remaining).

zy_de_nobatch <- sm(all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq"))
zy_de <- sm(all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq"))

zy_table_nobatch <- sm(combine_de_tables(
    zy_de_nobatch, excel = glue::glue("excel/zy_tables_nobatch-v{ver}.xlsx"),
    sig_excel = glue::glue("excel/zy_sig_nobatch-v{ver}.xlsx"),
    gmt = glue::glue("gmt/zymodeme_nobatch-v{ver}.gmt")))
zy_table <- sm(combine_de_tables(
    zy_de, excel = glue::glue("excel/zy_tables-v{ver}.xlsx"),
    sig_excel = glue::glue("excel/zy_sig-v{ver}.xlsx"),
    gmt = glue::glue("gmt/zymodeme-v{ver}.gmt")))

7.1.2 Create an initial implementation of a gmt writer from this result.

I want to add a function to extract gmt files from our DE results. In order for this to work best, I will likely need to change my expressionset creator in order to include in it the lab/experiment/etc, as I already did for the annotation source.

In addition, some work will need to be done in order to ensure we get the gene IDs in the expected format – this is work which has already mostly been done for the main ensembl reference species (humans/mouse), but may prove more challenging for things like Leishmania. So, let us test that for this data.

With the above in mind, I made a couple of changes to expt.r to add:

1. researcher (defaulting to elsayed)
2. study_name (defaulting to basename(getwd())

summary(zy_table_nobatch[["significant"]])

##               Length Class      Mode
## limma          7     -none-     list
## edger          7     -none-     list
## deseq          7     -none-     list
## ebseq          7     -none-     list
## basic          7     -none-     list
## sig_bar_plots 17     -none-     list
## summary_df    10     data.frame list

## For the moment, fill in the parameter with the dataset of interest
gmts_from_signicant <- function(zy_table_nobatch, according_to = "deseq") {
  researcher_name <- "elsayed"
  study_name <- "lpamanensis"
  annotation_name <- annotation(zy_table_nobatch[["input"]][["input"]][["expressionset"]])
  annotation_data <- get0(annotation_name)
  annot_meta <- metadata(annotation_data)
  organism_name_idx <- annot_meta[["name"]] == "ORGANISM"
  organism <- annot_meta[organism_name_idx, "value"]
  sig_tables <- zy_table_nobatch[["significant"]][[according_to]]
  categories <- names(sig_tables[["ups"]])
  for (cat in categories) {
    ups <- rownames(sig_tables[["ups"]][[cat]])
    downs <- rownames(sig_tables[["downs"]][[cat]])
    gsc <- make_gsc_from_ids(first_ids = ups, second_ids = downs,
                             annotation_name = annotation_name,
                             researcher_name = researcher_name,
                             study_name = study_name, organism = organism,
                             category_name = cat, pair_names = "up",
                             current_id = "ID", required_id = "ENTREZID")

  }
}

7.1.3 Images of zymodeme DE

pp(file = "images/zymo_ma.png", image = zy_table[["plots"]][["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]])

## Warning in pp(file = "images/zymo_ma.png", image = zy_table[["plots"]]
## [["z23_vs_z22"]][["deseq_ma_plots"]][["plot"]]): There is no device to shut
## down.

7.2 With respect to cure/failure

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

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

cf_table <- sm(combine_de_tables(
    cf_de,
    excel = glue::glue("excel/cf_tables-v{ver}.xlsx"),
    sig_excel = glue::glue("excel/cf_sig-v{ver}.xlsx")))

7.3 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 <- sm(all_pairwise(sus_expt, filter = TRUE, model_batch = "svaseq"))

sus_table <- sm(combine_de_tables(
    sus_de,
    excel = glue::glue("excel/sus_tables-v{ver}.xlsx"),
    sig_excel = glue::glue("excel/sus_sig-v{ver}.xlsx")))

knitr::kable(head(sus_table[["significant"]][["deseq"]][["ups"]][["sensitive_vs_resistant"]], n = 20))

	gid	annotgeneproduct	annotgenetype	chromosome	start	end	strand	annotgeneentrezid	annotgenename	annotstrand	annotchromosome	annotcdslength	length	deseq_logfc	deseq_adjp	edger_logfc	edger_adjp	limma_logfc	limma_adjp	basic_nummed	basic_denmed	basic_numvar	basic_denvar	basic_logfc	basic_t	basic_p	basic_adjp	deseq_basemean	deseq_lfcse	deseq_stat	deseq_p	ebseq_fc	ebseq_logfc	ebseq_c1mean	ebseq_c2mean	ebseq_mean	ebseq_var	ebseq_postfc	ebseq_ppee	ebseq_ppde	ebseq_adjp	edger_logcpm	edger_lr	edger_p	limma_ave	limma_t	limma_b	limma_p	limma_adjp_ihw	deseq_adjp_ihw	edger_adjp_ihw	ebseq_adjp_ihw	basic_adjp_ihw	lfc_meta	lfc_var	lfc_varbymed	p_meta	p_var
LPAL13_000017600	LPAL13_000017600	hypothetical protein, conserved	protein coding	LPAL13_SCAF000146	359	586	+			forward	Not Assigned	228.0	227	6.564	0	6.548	0e+00	6.287	0.0429	4.1180	-1.1470	5.268	2.5720	5.266	8.300	0e+00	0.0000	613.10	0.6613	9.927	0	72.705	6.184	13.210	961.12	676.75	4.516e+05	58.224	0.0000	1.0000	0.0000	4.4880	59.91	0	2.2600	2.665	-2.6780	0.0097	5.346e-02	6.951e-20	9.352e-12	8.934e-01	3.762e-07	6.754	2.432e+00	3.600e-01	3.238e-03	3.146e-05
LPAL13_000053200	LPAL13_000053200	hypothetical protein	protein coding	LPAL13_SCAF000804	5037	5249	-			reverse	Not Assigned	213.0	212	8.925	0	10.330	0e+00	6.031	0.0185	0.8570	-4.1700	9.600	0.1233	5.027	8.459	0e+00	0.0000	77.23	1.0440	8.550	0	13478.050	13.718	0.000	134.77	94.34	9.270e+03	36.806	1.0000	0.0000	1.0000	1.5470	58.05	0	-0.8949	3.084	-2.2650	0.0030	2.308e-02	1.372e-14	1.926e-11	0.000e+00	3.411e-07	8.406	3.528e-02	4.197e-03	1.003e-03	3.018e-06
LPAL13_200050100	LPAL13_200050100	hypothetical protein	protein coding	LpaL13_20.1	1627529	1627717	+			forward	20.1	189.0	188	5.525	0	5.473	0e+00	4.659	0.0009	2.3090	-1.8430	1.742	2.3634	4.152	8.155	0e+00	0.0000	121.50	0.5792	9.539	0	24.986	4.643	8.318	208.07	148.14	2.549e+04	17.788	0.0000	1.0000	0.0000	2.1720	62.54	0	0.7885	4.367	0.9945	0.0000	1.122e-03	2.478e-18	4.481e-12	9.791e-01	8.497e-06	5.208	3.918e-01	7.523e-02	1.551e-05	7.217e-10
LPAL13_300029400	LPAL13_300029400	hypothetical protein, conserved	protein coding	LpaL13_30	853953	854150	-			reverse	30	198.0	197	6.390	0	6.323	0e+00	5.128	0.0011	1.5080	-2.5360	1.854	1.9106	4.045	8.519	0e+00	0.0000	84.63	0.7095	9.007	0	59.752	5.901	2.119	127.20	89.67	1.056e+04	22.395	0.0000	0.0000	0.0000	1.6260	58.02	0	-0.0869	4.290	0.4427	0.0001	1.350e-03	2.648e-16	1.926e-11	0.000e+00	2.140e-06	5.953	3.312e-01	5.564e-02	2.036e-05	1.244e-09
LPAL13_350011800	LPAL13_350011800	hypothetical protein, conserved	protein coding	LpaL13_35	171009	171242	+			forward	35	234.0	233	5.289	0	5.278	0e+00	4.720	0.0033	2.6120	-1.1520	2.957	1.1319	3.764	8.418	0e+00	0.0000	178.20	0.5669	9.329	0	31.856	4.994	9.539	304.18	215.79	6.840e+04	23.769	0.0000	1.0000	0.0000	2.6990	59.94	0	1.0920	3.836	-0.0887	0.0003	4.173e-03	1.649e-17	9.352e-12	9.791e-01	1.578e-07	5.163	8.144e-01	1.577e-01	9.547e-05	2.734e-08
LPAL13_000035500	LPAL13_000035500	hypothetical protein, conserved	protein coding	LPAL13_SCAF000492	7045	7410	+			forward	Not Assigned	366.0	365	4.506	0	4.504	0e+00	4.085	0.0261	4.2390	0.8008	4.573	0.5655	3.439	7.496	0e+00	0.0000	529.10	0.5721	7.875	0	18.868	4.238	51.072	963.80	689.98	4.309e+05	18.173	0.0000	1.0000	0.0000	4.2760	43.65	0	2.7960	2.914	-2.1340	0.0049	3.282e-02	1.711e-12	7.000e-09	8.934e-01	5.314e-07	4.559	7.298e-01	1.601e-01	1.633e-03	7.997e-06
LPAL13_000014000	LPAL13_000014000	hypothetical protein	protein coding	LPAL13_SCAF000119	655	942	+			forward	Not Assigned	288.0	287	4.336	0	4.322	0e+00	4.000	0.0071	2.3130	-1.0970	1.833	1.9220	3.410	7.178	0e+00	0.0000	129.60	0.5067	8.557	0	16.737	4.065	12.133	203.23	145.90	1.498e+04	13.093	0.0000	1.0000	0.0000	2.2670	54.00	0	1.0460	3.522	-0.8407	0.0008	8.896e-03	1.192e-14	1.052e-10	9.791e-01	1.809e-05	4.374	6.912e-01	1.580e-01	2.644e-04	2.098e-07
LPAL13_220019500	LPAL13_220019500	hypothetical protein	protein coding	LpaL13_22	578260	578538	+			forward	22	279.0	278	3.869	0	3.866	0e+00	3.186	0.0322	3.3350	0.3472	2.695	0.7936	2.988	7.415	0e+00	0.0000	287.50	0.4908	7.882	0	13.625	3.768	32.794	446.95	322.70	8.664e+04	12.589	0.0000	1.0000	0.0000	3.4060	45.98	0	2.3050	2.810	-2.3530	0.0065	3.956e-02	1.711e-12	2.772e-09	9.791e-01	9.724e-07	3.704	9.768e-02	2.637e-02	2.181e-03	1.427e-05
LPAL13_040019400	LPAL13_040019400	hypothetical protein	protein coding	LpaL13_04	440768	441127	-			reverse	4	360.0	359	5.679	0	5.727	0e+00	3.639	0.0059	-0.4572	-3.3380	1.891	1.1727	2.881	7.087	0e+00	0.0000	28.83	0.8258	6.877	0	43.769	5.452	0.845	37.41	26.44	2.057e+03	8.868	0.0000	0.0000	0.0000	0.1553	36.77	0	-1.8060	3.603	-1.3140	0.0006	7.378e-03	1.373e-09	1.607e-07	0.000e+00	6.646e-06	5.066	6.063e-02	1.197e-02	2.041e-04	1.250e-07
LPAL13_080009800	LPAL13_080009800	tuzin, putative	protein coding	LpaL13_08	184254	185207	+			forward	8	954.0	953	3.312	0	3.314	0e+00	3.191	0.0000	7.1310	4.2880	1.763	0.4548	2.843	8.952	0e+00	0.0000	3419.00	0.3957	8.370	0	8.908	3.155	647.196	5765.41	4229.95	1.806e+07	9.131	0.0001	0.9999	0.0001	6.9600	56.60	0	6.1730	5.792	6.8160	0.0000	2.024e-05	5.422e-14	2.857e-11	1.000e+00	3.138e-08	3.587	6.434e-01	1.794e-01	7.470e-08	1.674e-14
LPAL13_170012200	LPAL13_170012200	hypothetical protein	protein coding	LpaL13_17	289959	290222	-			reverse	17	264.0	263	3.862	0	3.861	0e+00	3.345	0.0084	3.2290	0.4204	3.867	0.2272	2.809	7.088	0e+00	0.0000	285.70	0.5427	7.116	0	14.361	3.844	32.795	471.10	339.61	1.078e+05	13.286	0.0000	1.0000	0.0000	3.3940	37.77	0	2.0090	3.452	-0.7961	0.0010	1.050e-02	3.311e-10	1.088e-07	9.791e-01	2.258e-06	3.897	4.859e-01	1.247e-01	3.286e-04	3.239e-07
LPAL13_000011700	LPAL13_000011700	hypothetical protein	protein coding	LPAL13_SCAF000076	101	364	-			reverse	Not Assigned	264.0	263	6.200	0	7.639	0e+00	3.206	0.0185	-1.4720	-4.1700	6.800	0.1233	2.698	5.362	0e+00	0.0002	14.87	1.1620	5.337	0	2690.500	11.394	0.000	26.89	18.83	5.831e+02	7.965	1.0000	0.0000	1.0000	-0.5642	26.82	0	-2.8600	3.083	-2.3420	0.0030	2.262e-02	5.009e-06	9.045e-06	0.000e+00	1.999e-04	5.654	9.394e-01	1.661e-01	1.003e-03	3.018e-06
LPAL13_130006100	LPAL13_130006100	hypothetical protein	protein coding	LpaL13_13	26114	26335	-			reverse	13	222.0	221	3.094	0	3.095	0e+00	2.608	0.0059	3.4420	0.9663	1.020	0.2916	2.476	10.050	0e+00	0.0000	267.70	0.3652	8.472	0	7.926	2.986	50.143	397.49	293.28	5.813e+04	7.591	0.0000	1.0000	0.0000	3.3090	59.37	0	2.6680	3.601	-0.3859	0.0006	7.393e-03	1.719e-14	9.352e-12	9.791e-01	5.005e-09	2.932	0.000e+00	0.000e+00	2.055e-04	1.266e-07
LPAL13_170014500	LPAL13_170014500	hypothetical protein, conserved	protein coding	LpaL13_17	361708	362040	+			forward	17	333.0	332	5.267	0	5.133	1e-04	3.102	0.0171	-0.8103	-3.2750	6.452	1.5010	2.465	4.134	2e-04	0.0020	22.51	1.0160	5.184	0	42.994	5.426	1.034	44.87	31.72	1.873e+03	10.109	0.0000	0.0000	0.0000	-0.2222	20.80	0	-2.3740	3.121	-2.1820	0.0027	2.110e-02	8.269e-06	1.362e-04	0.000e+00	1.681e-03	4.355	1.129e-01	2.591e-02	9.014e-04	2.423e-06
LPAL13_190012600	LPAL13_190012600	hypothetical protein, conserved	protein coding	LpaL13_19	254293	254577	-			reverse	19	285.0	284	3.451	0	3.446	0e+00	2.745	0.0056	1.9270	-0.3928	1.639	0.3257	2.319	7.924	0e+00	0.0000	94.05	0.4065	8.491	0	9.125	3.190	16.947	154.72	113.39	8.550e+03	7.723	0.0000	1.0000	0.0000	1.8280	53.36	0	1.0340	3.622	-0.4766	0.0006	6.875e-03	1.594e-14	1.132e-10	9.791e-01	2.568e-07	3.199	6.800e-04	2.126e-04	1.917e-04	1.103e-07
LPAL13_240030500	LPAL13_240030500	hypothetical protein	protein coding	LpaL13_24	828719	828913	+			forward	24	195.0	194	2.310	0	2.312	0e+00	2.762	0.0066	3.2430	0.9338	1.225	3.9317	2.309	3.789	2e-03	0.0112	249.20	0.4205	5.493	0	4.360	2.124	81.147	353.80	272.00	4.014e+04	4.263	0.0022	0.9978	0.0022	3.1870	28.15	0	2.4700	3.552	-0.5192	0.0007	8.277e-03	1.991e-06	5.296e-06	8.933e-01	1.131e-02	2.621	5.723e-01	2.183e-01	2.399e-04	1.725e-07
LPAL13_000025200	LPAL13_000025200	hypothetical protein	protein coding	LPAL13_SCAF000287	104	373	+			forward	Not Assigned	270.0	269	3.105	0	3.104	0e+00	2.745	0.0174	3.6680	1.3670	2.562	0.4839	2.301	6.339	0e+00	0.0000	305.20	0.5342	5.812	0	7.158	2.840	76.274	546.02	405.10	1.255e+05	7.032	0.0000	1.0000	0.0000	3.4860	27.26	0	2.3770	3.111	-1.6810	0.0028	2.184e-02	5.316e-07	8.896e-06	9.682e-01	8.102e-06	3.016	1.756e-02	5.822e-03	9.247e-04	2.565e-06
LPAL13_210015300	LPAL13_210015300	core histone h2a/h2b/h3/h4, putative	protein coding	LpaL13_21	324301	324699	+			forward	21	399.0	398	2.588	0	2.592	0e+00	2.476	0.0000	8.6590	6.4030	1.842	0.5195	2.256	6.831	0e+00	0.0000	11650.00	0.3960	6.536	0	5.802	2.537	2899.069	16821.37	12644.68	1.212e+08	5.942	0.0056	0.9944	0.0056	8.7310	37.01	0	7.9430	6.000	7.5030	0.0000	1.406e-05	7.878e-09	1.176e-07	1.000e+00	3.408e-06	2.546	4.177e-02	1.641e-02	3.333e-08	3.210e-15
LPAL13_080010600	LPAL13_080010600	hypothetical protein, conserved	protein coding	LpaL13_08	195555	195749	-			reverse	8	195.0	194	6.002	0	7.324	0e+00	2.837	0.0176	-1.9210	-4.1700	4.935	0.1233	2.249	5.207	0e+00	0.0003	11.70	1.0980	5.466	0	2013.648	10.976	0.000	20.13	14.09	5.608e+02	6.299	1.0000	0.0000	1.0000	-0.9047	27.91	0	-3.0720	3.108	-2.3430	0.0028	2.206e-02	2.256e-06	5.812e-06	0.000e+00	2.196e-04	5.091	1.949e+00	3.828e-01	9.341e-04	2.617e-06
LPAL13_000018000	LPAL13_000018000	hypothetical protein	protein coding	LPAL13_SCAF000166	2	412	+			forward	Not Assigned	411.0	410	2.730	0	2.731	0e+00	2.705	0.0004	6.6890	4.4530	2.050	0.1292	2.237	7.718	0e+00	0.0000	2496.00	0.4776	5.716	0	6.003	2.586	687.316	4125.65	3094.15	6.072e+06	6.106	0.0000	1.0000	0.0000	6.5080	28.55	0	5.5810	4.693	2.8090	0.0000	3.392e-04	6.009e-07	4.114e-06	1.000e+00	6.248e-07	2.696	7.249e-02	2.688e-02	4.857e-06	6.930e-11

knitr::kable(head(sus_table[["significant"]][["deseq"]][["downs"]][["sensitive_vs_resistant"]], n = 20))

	gid	annotgeneproduct	annotgenetype	chromosome	start	end	strand	annotgeneentrezid	annotgenename	annotstrand	annotchromosome	annotcdslength	length	deseq_logfc	deseq_adjp	edger_logfc	edger_adjp	limma_logfc	limma_adjp	basic_nummed	basic_denmed	basic_numvar	basic_denvar	basic_logfc	basic_t	basic_p	basic_adjp	deseq_basemean	deseq_lfcse	deseq_stat	deseq_p	ebseq_fc	ebseq_logfc	ebseq_c1mean	ebseq_c2mean	ebseq_mean	ebseq_var	ebseq_postfc	ebseq_ppee	ebseq_ppde	ebseq_adjp	edger_logcpm	edger_lr	edger_p	limma_ave	limma_t	limma_b	limma_p	limma_adjp_ihw	deseq_adjp_ihw	edger_adjp_ihw	ebseq_adjp_ihw	basic_adjp_ihw	lfc_meta	lfc_var	lfc_varbymed	p_meta	p_var
LPAL13_000033300	LPAL13_000033300	hypothetical protein, conserved	protein coding	LPAL13_SCAF000463	551	811	+			forward	Not Assigned	261.0	260	-4.464	0.0026	-4.408	0.0030	-7.262	0.0000	-3.3430	3.4550	12.7819	0.0608	-6.797	-10.000	0e+00	0e+00	134.700	1.2270	-3.637	0.0003	0.1605	-2.6393	339.45	54.476	139.969	2.623e+04	0.1688	0.0000	0.0000	0.0000	2.2950	12.970	0.0003	-0.9429	-8.222	13.700	0e+00	4.930e-08	3.156e-03	3.526e-03	0.000e+00	3.138e-08	-5.378	0.000e+00	0.000e+00	1.971e-04	2.955e-08
LPAL13_000012000	LPAL13_000012000	hypothetical protein	protein coding	LPAL13_SCAF000080	710	1159	-			reverse	Not Assigned	450.0	449	-2.495	0.0010	-2.491	0.0003	-4.411	0.0000	0.1042	3.9370	8.7982	0.1681	-3.832	-6.689	0e+00	0e+00	212.100	0.6323	-3.946	0.0001	0.2657	-1.9123	490.41	130.281	238.320	6.077e+04	0.2767	0.0243	0.9757	0.0243	2.9530	18.170	0.0000	1.3280	-5.852	6.927	0e+00	2.056e-05	1.155e-03	4.073e-04	9.680e-01	8.497e-06	-3.046	6.800e-02	-2.232e-02	3.334e-05	1.707e-09
LPAL13_310035500	LPAL13_310035500	hypothetical protein	protein coding	LpaL13_31	1198439	1198957	-			reverse	31	519.0	518	-2.525	0.0033	-2.426	0.0013	-3.477	0.0000	-4.1400	-0.4281	4.1920	0.4519	-3.712	-8.576	0e+00	0e+00	7.035	0.7117	-3.548	0.0004	0.2972	-1.7503	19.35	5.744	9.826	3.556e+02	0.3460	0.0000	0.0000	0.0000	-1.9660	14.900	0.0001	-3.1990	-7.723	8.857	0e+00	1.220e-07	3.383e-03	1.342e-03	0.000e+00	5.704e-08	-2.836	1.131e-02	-3.987e-03	1.671e-04	3.986e-08
LPAL13_000038400	LPAL13_000038400	expression-site associated gene (esag3), putative	protein coding	LPAL13_SCAF000573	101	1360	+			forward	Not Assigned	1260.0	1259	-2.815	0.0000	-2.813	0.0000	-3.784	0.0000	4.6300	8.2100	3.5430	0.0361	-3.581	-9.948	0e+00	0e+00	3720.000	0.5328	-5.283	0.0000	0.1947	-2.3605	9563.26	1862.157	4172.487	1.879e+07	0.1996	0.0000	0.0000	0.0000	7.0800	31.120	0.0000	5.7700	-6.294	8.671	0e+00	4.873e-06	6.859e-06	1.468e-06	0.000e+00	4.396e-08	-3.241	3.350e-02	-1.034e-02	6.075e-08	3.322e-15
LPAL13_000012100	LPAL13_000012100	hypothetical protein	protein coding	LPAL13_SCAF000080	1637	1894	-			reverse	Not Assigned	258.0	257	-2.077	0.0107	-2.069	0.0076	-3.736	0.0000	-2.2070	1.1620	6.5920	0.6332	-3.370	-6.277	0e+00	0e+00	31.370	0.6642	-3.128	0.0018	0.3004	-1.7351	69.77	20.952	35.598	2.066e+03	0.3283	0.0073	0.9927	0.0073	0.2324	10.720	0.0011	-1.3790	-5.597	4.952	0e+00	4.490e-05	1.086e-02	9.088e-03	9.780e-01	1.036e-05	-2.487	1.387e-01	-5.575e-02	9.408e-04	7.864e-07
LPAL13_000038500	LPAL13_000038500	hypothetical protein	protein coding	LPAL13_SCAF000575	39	251	+			forward	Not Assigned	213.0	212	-2.142	0.0030	-2.130	0.0032	-3.662	0.0000	-1.9850	1.3650	4.9749	0.6608	-3.350	-6.945	0e+00	0e+00	32.660	0.5980	-3.582	0.0003	0.2950	-1.7611	81.03	23.898	41.037	2.717e+03	0.3194	0.0219	0.9781	0.0219	0.2414	12.840	0.0003	-1.2750	-6.274	7.073	0e+00	6.329e-06	3.021e-03	3.685e-03	9.680e-01	2.258e-06	-2.540	4.506e-02	-1.774e-02	2.268e-04	3.858e-08
LPAL13_350063000	LPAL13_350063000	hypothetical protein	protein coding	LpaL13_35	1964328	1964543	-			reverse	35	216.0	215	-2.757	0.0000	-2.737	0.0000	-3.704	0.0000	-2.1430	1.1750	2.1229	0.2166	-3.318	-10.830	0e+00	0e+00	21.340	0.4698	-5.868	0.0000	0.1753	-2.5125	58.91	10.316	24.895	7.197e+02	0.1934	0.0000	1.0000	0.0000	-0.3625	34.370	0.0000	-1.4530	-8.082	12.190	0e+00	4.930e-08	3.292e-07	3.841e-07	9.791e-01	1.500e-09	-3.037	1.963e-02	-6.464e-03	2.994e-09	6.631e-18
LPAL13_310031300	LPAL13_310031300	hypothetical protein, conserved	protein coding	LpaL13_31	1084772	1085059	-			reverse	31	288.0	287	-2.255	0.0014	-2.248	0.0008	-3.859	0.0000	-0.9701	2.1590	4.1051	0.7645	-3.129	-6.824	0e+00	0e+00	65.480	0.5902	-3.821	0.0001	0.2715	-1.8808	150.60	40.886	73.800	7.795e+03	0.2884	0.1534	0.8466	0.1534	1.2550	16.240	0.0001	-0.2054	-6.412	8.198	0e+00	4.634e-06	1.439e-03	9.162e-04	8.673e-01	2.258e-06	-2.799	7.637e-02	-2.729e-02	6.295e-05	4.451e-09
LPAL13_340039600	LPAL13_340039600	hypothetical protein	protein coding	LpaL13_34	1247554	1247757	-			reverse	34	204.0	203	-2.336	0.0001	-2.334	0.0000	-3.414	0.0000	1.1870	4.2690	4.1252	0.1033	-3.081	-7.803	0e+00	0e+00	232.400	0.4936	-4.733	0.0000	0.2185	-2.1941	605.18	132.244	274.125	6.529e+04	0.2234	0.0000	1.0000	0.0000	3.0740	26.010	0.0000	1.9780	-5.646	6.302	0e+00	3.888e-05	5.505e-05	1.474e-05	8.934e-01	7.620e-07	-2.690	1.959e-03	-7.283e-04	9.841e-07	1.141e-12
LPAL13_310031000	LPAL13_310031000	hypothetical protein, conserved	protein coding	LpaL13_31	1075172	1075459	-			reverse	31	288.0	287	-2.217	0.0000	-2.201	0.0000	-3.375	0.0000	-1.7270	0.9896	3.2962	0.5154	-2.717	-6.778	0e+00	0e+00	27.090	0.4444	-4.989	0.0000	0.3096	-1.6916	59.78	18.499	30.883	1.138e+03	0.3299	0.3062	0.6938	0.3062	0.0538	26.160	0.0000	-1.1310	-7.852	11.790	0e+00	8.114e-08	2.381e-05	1.185e-05	7.316e-01	2.436e-06	-2.598	0.000e+00	0.000e+00	3.075e-07	9.255e-14
LPAL13_050005000	LPAL13_050005000	hypothetical protein	protein coding	LpaL13_05	3394	3612	-			reverse	5	219.0	218	-2.022	0.0038	-2.019	0.0018	-2.980	0.0000	-0.0244	2.6770	3.6342	0.1568	-2.701	-7.147	0e+00	0e+00	92.660	0.5776	-3.500	0.0005	0.2979	-1.7473	195.27	58.154	99.289	7.853e+03	0.3064	0.0000	1.0000	0.0000	1.7490	14.270	0.0002	0.4891	-5.487	5.509	0e+00	6.053e-05	3.865e-03	2.037e-03	8.934e-01	2.298e-06	-2.259	1.872e-02	-8.287e-03	2.080e-04	5.564e-08
LPAL13_140019300	LPAL13_140019300	bt1 family, putative	protein coding	LpaL13_14	530784	531350	+			forward	14	567.0	566	-2.653	0.0000	-2.651	0.0000	-2.671	0.0000	4.7250	7.1000	0.6657	1.0271	-2.375	-7.180	0e+00	0e+00	1940.000	0.3605	-7.359	0.0000	0.1910	-2.3882	5126.37	979.260	2223.392	6.182e+06	0.1961	0.0000	1.0000	0.0000	6.1420	64.150	0.0000	5.4100	-7.759	14.430	0e+00	8.114e-08	4.900e-11	2.142e-12	9.560e-01	4.518e-05	-2.777	1.910e-01	-6.879e-02	2.735e-11	2.228e-21
LPAL13_310039200	LPAL13_310039200	hypothetical protein	protein coding	LpaL13_31	1301745	1301972	-			reverse	31	228.0	227	-2.348	0.0000	-2.345	0.0000	-2.711	0.0000	1.4340	3.7450	1.6362	0.2106	-2.311	-8.385	0e+00	0e+00	198.300	0.3819	-6.150	0.0000	0.3053	-1.7118	426.88	130.311	219.282	4.004e+04	0.3156	0.1979	0.8021	0.1979	2.8580	43.820	0.0000	2.0540	-6.751	10.360	0e+00	1.849e-06	8.256e-08	7.702e-09	7.442e-01	8.324e-08	-2.621	8.058e-02	-3.074e-02	1.905e-09	6.879e-18
LPAL13_050016500	LPAL13_050016500	unspecified product	snoRNA encoding	LpaL13_05	undefined	undefined	+			forward	5	0.0	undefined	-1.102	0.2008	-1.091	0.2390	-2.500	0.0021	-1.4520	0.6502	3.8690	0.9718	-2.103	-4.491	1e-04	9e-04	30.300	0.6526	-1.689	0.0913	0.7102	-0.4937	47.76	33.919	38.073	4.052e+03	0.7470	0.7265	0.2735	0.7265	0.1482	2.528	0.1118	-1.3560	-4.029	0.496	2e-04	2.601e-03	2.133e-01	2.737e-01	2.860e-01	7.600e-04	-1.524	3.228e-01	-2.118e-01	6.775e-02	3.532e-03
LPAL13_340039700	LPAL13_340039700	snare domain containing protein, putative	protein coding	LpaL13_34	1248192	1248947	-			reverse	34	756.0	755	-1.890	0.0000	-1.889	0.0000	-2.149	0.0000	4.6280	6.7120	0.7919	0.0938	-2.084	-10.970	0e+00	0e+00	1426.000	0.3010	-6.278	0.0000	0.2803	-1.8347	3280.43	919.659	1627.891	1.661e+06	0.2841	0.0000	1.0000	0.0000	5.6970	47.030	0.0000	5.2740	-7.285	12.540	0e+00	2.533e-07	5.661e-08	2.434e-09	9.698e-01	1.500e-09	-1.969	6.690e-03	-3.397e-03	3.045e-10	7.844e-20
LPAL13_140019100	LPAL13_140019100	bt1 family, putative	protein coding	LpaL13_14	525164	525514	+			forward	14	351.0	350	-2.085	0.0000	-2.084	0.0000	-2.270	0.0000	3.9610	6.0230	0.5051	0.5074	-2.062	-8.394	0e+00	0e+00	900.900	0.2929	-7.119	0.0000	0.2498	-2.0009	2157.85	539.110	1024.734	8.522e+05	0.2536	0.0000	1.0000	0.0000	5.0350	60.970	0.0000	4.5830	-8.750	18.360	0e+00	1.675e-08	2.969e-10	8.398e-12	8.934e-01	2.258e-06	-2.201	2.558e-02	-1.162e-02	8.466e-13	5.626e-25
LPAL13_350073400	LPAL13_350073400	hypothetical protein	protein coding	LpaL13_35	2342701	2342883	+			forward	35	183.0	182	-1.403	0.0040	-1.396	0.0061	-2.137	0.0002	-0.2233	1.7810	1.2535	0.8080	-2.004	-5.986	0e+00	1e-04	50.190	0.4025	-3.486	0.0005	0.3119	-1.6806	120.30	37.519	62.352	7.255e+03	0.3353	0.0002	0.9998	0.0002	0.8256	11.230	0.0008	0.0804	-5.000	3.719	0e+00	2.185e-04	3.963e-03	6.171e-03	9.791e-01	7.644e-05	-1.602	1.952e-02	-1.219e-02	4.335e-04	1.629e-07
LPAL13_170015400	LPAL13_170015400	hypothetical protein, conserved	protein coding	LpaL13_17	395975	396307	+			forward	17	333.0	332	-1.739	0.0000	-1.736	0.0000	-2.077	0.0000	1.3560	3.2620	1.0759	0.1243	-1.906	-8.631	0e+00	0e+00	150.600	0.2986	-5.825	0.0000	0.3481	-1.5226	296.39	103.157	161.127	1.504e+04	0.3526	0.0000	1.0000	0.0000	2.4540	35.860	0.0000	1.9690	-6.586	9.689	0e+00	2.759e-06	4.103e-07	2.448e-07	9.791e-01	4.953e-08	-1.803	1.522e-02	-8.438e-03	5.796e-09	1.373e-17
LPAL13_180013900	LPAL13_180013900	hypothetical protein	protein coding	LpaL13_18	351792	352085	+			forward	18	294.0	293	-1.347	0.0038	-1.342	0.0035	-2.097	0.0001	-0.2658	1.5920	1.0315	0.0777	-1.858	-8.927	0e+00	0e+00	46.230	0.3853	-3.497	0.0005	0.4249	-1.2348	78.10	33.181	46.657	8.576e+02	0.4295	0.0028	0.9972	0.0028	0.7518	12.620	0.0004	0.1474	-5.270	4.540	0e+00	1.103e-04	3.900e-03	4.025e-03	9.791e-01	4.837e-08	-1.537	1.419e-02	-9.230e-03	2.844e-04	6.196e-08
LPAL13_350013200	LPAL13_350013200	hypothetical protein, conserved	protein coding	LpaL13_35	223837	224070	+			forward	35	234.0	233	-1.320	0.0092	-1.308	0.0146	-2.058	0.0002	-2.0020	-0.1622	1.2185	0.6795	-1.840	-5.814	0e+00	1e-04	11.430	0.4142	-3.186	0.0014	0.3789	-1.4002	27.01	10.228	15.263	3.236e+02	0.4038	0.7229	0.2771	0.7229	-1.1140	9.184	0.0024	-1.9950	-4.983	2.676	0e+00	2.315e-04	9.806e-03	1.698e-02	3.226e-01	8.757e-05	-1.488	1.433e-02	-9.631e-03	1.296e-03	1.501e-06

sus_ma <- sus_table[["plots"]][["sensitive_vs_resistant"]][["deseq_ma_plots"]][["plot"]]
pp(file = "images/sus_ma.png", image = sus_ma)

## Warning in pp(file = "images/sus_ma.png", image = sus_ma): There is no device to
## shut down.

## test <- ggplt(sus_ma)

7.4 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 <- sm(simple_goseq(sig_genes = zy_table[["significant"]][["deseq"]][["ups"]][[1]],
                            go_db = lp_go, length_db = lp_lengths))

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

7.4.1 A couple plots from the differential expression

7.4.1.1 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[["venns"]][[1]][["p_lfc1"]][["up_noweight"]]

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

zy_table[["venns"]][[1]][["p_lfc1"]][["down_noweight"]]

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

zy_go_up$pvalue_plots$bpp_plot_over

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

zy_go_down$pvalue_plots$bpp_plot_over

7.5 Look for agreement between sensitivity and zymodemes

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

zy_df <- zy_table[["data"]][["z23_vs_z22"]]
sus_df <- sus_table[["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

pp(file = "images/compare_sus_zy.png", image = compare$scatter)

## Warning in pp(file = "images/compare_sus_zy.png", image = compare$scatter):
## There is no device to shut down.

compare$cor

## 
##  Pearson's product-moment correlation
## 
## data:  df[, 1] and df[, 2]
## t = -244, df = 8549, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9378 -0.9325
## sample estimates:
##     cor 
## -0.9352

7.6 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:

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

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

7.6.1 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 8551 genes, now there are 6.

## There are 59 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.1313    0.1250    0.1321    0.1061    0.1301    0.1102    0.1131    0.1165 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.1157    0.1182    0.1150    0.1137    0.1101    0.1062    0.1104    0.1209 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1208    0.1066    0.1092    0.1147    0.1225    0.1128    0.1103    0.1063 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.1307    0.1126    0.1203    0.1164    0.1128    0.1386    0.1005    0.1203 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20042 TMRC20058 TMRC20072 
##    0.1228    0.1347    0.1269    0.1234    0.1341    0.1315    0.1182    0.1431 
## TMRC20059 TMRC20048 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 
##    0.1104    0.1033    0.1087    0.1221    0.1209    0.1168    0.1183    0.1106 
## TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 TMRC20080 TMRC20043 
##    0.1140    0.1113    0.1283    0.1154    0.1394    0.1286    0.1155    0.1138 
## TMRC20054 TMRC20046 TMRC20044 
##    0.1278    0.1368    0.1338

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

7.7 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)

## 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_000038400"
##  [5] "LPAL13_000038500" "LPAL13_310031300" "LPAL13_340039600" "LPAL13_050005000"
##  [9] "LPAL13_310035500" "LPAL13_310031000" "LPAL13_310039200" "LPAL13_350063000"
## [13] "LPAL13_140019300" "LPAL13_210015500" "LPAL13_340039700" "LPAL13_180013900"
## [17] "LPAL13_170015400" "LPAL13_250006300" "LPAL13_350073400" "LPAL13_290026200"
## [21] "LPAL13_350013200" "LPAL13_140019100" "LPAL13_000025600" "LPAL13_330024000"
## [25] "LPAL13_050016500" "LPAL13_230016400" "LPAL13_190021800" "LPAL13_320038700"
## [29] "LPAL13_240009700" "LPAL13_350012400" "LPAL13_330021800" "LPAL13_140019200"
## [33] "LPAL13_270034100" "LPAL13_210005000" "LPAL13_160014500" "LPAL13_000029000"
## [37] "LPAL13_350073200" "LPAL13_250025700" "LPAL13_230011200" "LPAL13_330021900"
## [41] "LPAL13_000052700" "LPAL13_230021300" "LPAL13_050009600" "LPAL13_160014100"
## [45] "LPAL13_230011500" "LPAL13_310028500" "LPAL13_310032500" "LPAL13_040007800"
## [49] "LPAL13_160014200" "LPAL13_230011400" "LPAL13_020006700" "LPAL13_000045100"
## [53] "LPAL13_230011300"

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

## Before removal, there were 8551 genes, now there are 53.

## There are 59 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.3932    0.4641    0.1453    0.4758    0.2091    0.5464    0.6423    0.3691 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.4202    0.1913    0.4875    0.1747    0.4566    0.3736    0.1888    0.1534 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.4250    0.2302    0.1952    0.4045    0.1674    0.4577    0.1705    0.4480 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.1558    0.1969    0.2370    0.2122    0.2186    0.1409    0.1309    0.2037 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20042 TMRC20058 TMRC20072 
##    0.5922    0.2213    0.5851    0.5850    0.2494    0.1975    0.7807    0.2338 
## TMRC20059 TMRC20048 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 
##    0.3495    0.4065    0.1451    0.1461    0.2658    0.1908    0.2287    0.5417 
## TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 TMRC20080 TMRC20043 
##    0.5423    0.3757    0.7146    0.2298    0.2052    0.7305    0.5693    0.5051 
## TMRC20054 TMRC20046 TMRC20044 
##    0.6412    0.1828    0.2029

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

7.7.1 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

7.7.2 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 8551 genes, now there are 68.

## There are 59 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.2208    0.1912    0.6726    0.2278    0.6597    0.2115    0.2020    0.2399 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.2035    0.6927    0.1899    0.6407    0.1712    0.2111    0.5737    0.5573 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1683    0.6603    0.6543    0.1620    0.6572    0.1870    0.6903    0.1571 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.6812    0.7222    0.6814    0.6993    0.7260    0.7025    0.6176    0.6103 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20042 TMRC20058 TMRC20072 
##    0.1959    0.7163    0.1906    0.1730    0.5351    0.5511    0.2224    0.5397 
## TMRC20059 TMRC20048 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 
##    0.1432    0.1563    0.7657    0.5766    0.6658    0.6365    0.5752    0.1801 
## TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 TMRC20080 TMRC20043 
##    0.1963    0.1821    0.1913    0.6169    0.6990    0.1894    0.1555    0.1740 
## TMRC20054 TMRC20046 TMRC20044 
##    0.2037    0.6355    0.6241

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

8 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)

8.1 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 71, 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

both_snps <- combine_expts(new_snps, old_snps)
both_norm <- sm(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.

8.2 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)
pp(file = "images/raw_snp_disheat.png", image = old_new_variant_heatmap,
   height = 12, width = 12)

## Warning in pp(file = "images/raw_snp_disheat.png", image =
## old_new_variant_heatmap, : There is no device to shut down.

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 7 rows.

## 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.

## Iterating over 727 elements.

both_expt <- combine_expts(lp_expt, old_expt)

## Error in combine(exp1, exp2): objects have different annotations: org.Lpanamensis.MHOMCOL81L13.v46.eg.db, org.Hs.eg.db

snp_genes <- sm(snps_vs_genes(both_expt, snp_sets, expt_name_col = "chromosome"))

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'both_expt' not found

## 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")))

## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'fData': object 'both_expt' not found

zymo_heat <- plot_sample_heatmap(snp_subset, row_label = rownames(exprs(snp_subset)))

## Error in plot_sample_heatmap(snp_subset, row_label = rownames(exprs(snp_subset))): object 'snp_subset' not found

zymo_heat

## Error in eval(expr, envir, enclos): object 'zymo_heat' not found

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

8.3 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.

## Iterating over 695 elements.

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)

8.3.1 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

9 Zymodeme for new samples

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

9.1 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.

## Iterating over 695 elements.

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.

9.1.1 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!

9.1.2 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

9.2 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 71, now there are 59 samples.

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

## subset_expt(): There were 59, 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")

## Started with 154019, after cutoff there are 76569 rows left.

## Starting chromosome: LpaL13_01.

## Starting chromosome: LpaL13_02.

## Starting chromosome: LpaL13_03.

## Starting chromosome: LpaL13_04.

## Starting chromosome: LpaL13_05.

## Starting chromosome: LpaL13_06.

## Starting chromosome: LpaL13_07.

## Starting chromosome: LpaL13_08.

## Starting chromosome: LpaL13_09.

## Starting chromosome: LpaL13_10.

## Starting chromosome: LpaL13_11.

## Starting chromosome: LpaL13_12.

## Starting chromosome: LpaL13_13.

## Starting chromosome: LpaL13_14.

## Starting chromosome: LpaL13_15.

## Starting chromosome: LpaL13_16.

## Starting chromosome: LpaL13_17.

## Starting chromosome: LpaL13_18.

## Starting chromosome: LpaL13_19.

## Starting chromosome: LpaL13_20.1.

## Starting chromosome: LpaL13_20.2.

## Starting chromosome: LpaL13_21.

## Starting chromosome: LpaL13_22.

## Starting chromosome: LpaL13_23.

## Starting chromosome: LpaL13_24.

## Starting chromosome: LpaL13_25.

## Starting chromosome: LpaL13_26.

## Starting chromosome: LpaL13_27.

## Starting chromosome: LpaL13_28.

## Starting chromosome: LpaL13_29.

## Starting chromosome: LpaL13_30.

## Starting chromosome: LpaL13_31.

## Starting chromosome: LpaL13_32.

## Starting chromosome: LpaL13_33.

## Starting chromosome: LpaL13_34.

## Starting chromosome: LpaL13_35.

## Starting chromosome: LPAL13_SCAF000001.

## Starting chromosome: LPAL13_SCAF000002.

## Starting chromosome: LPAL13_SCAF000003.

## Starting chromosome: LPAL13_SCAF000005.

## Starting chromosome: LPAL13_SCAF000009.

## Starting chromosome: LPAL13_SCAF000010.

## Starting chromosome: LPAL13_SCAF000011.

## Starting chromosome: LPAL13_SCAF000014.

## Starting chromosome: LPAL13_SCAF000015.

## Starting chromosome: LPAL13_SCAF000017.

## Starting chromosome: LPAL13_SCAF000018.

## Starting chromosome: LPAL13_SCAF000019.

## Starting chromosome: LPAL13_SCAF000021.

## Starting chromosome: LPAL13_SCAF000022.

## Starting chromosome: LPAL13_SCAF000026.

## Starting chromosome: LPAL13_SCAF000027.

## Starting chromosome: LPAL13_SCAF000029.

## Starting chromosome: LPAL13_SCAF000032.

## Starting chromosome: LPAL13_SCAF000033.

## Starting chromosome: LPAL13_SCAF000035.

## Starting chromosome: LPAL13_SCAF000036.

## Starting chromosome: LPAL13_SCAF000037.

## Starting chromosome: LPAL13_SCAF000039.

## Starting chromosome: LPAL13_SCAF000041.

## Starting chromosome: LPAL13_SCAF000042.

## Starting chromosome: LPAL13_SCAF000043.

## Starting chromosome: LPAL13_SCAF000044.

## Starting chromosome: LPAL13_SCAF000047.

## Starting chromosome: LPAL13_SCAF000048.

## Starting chromosome: LPAL13_SCAF000049.

## Starting chromosome: LPAL13_SCAF000050.

## Starting chromosome: LPAL13_SCAF000051.

## Starting chromosome: LPAL13_SCAF000052.

## Starting chromosome: LPAL13_SCAF000053.

## Starting chromosome: LPAL13_SCAF000054.

## Starting chromosome: LPAL13_SCAF000056.

## Starting chromosome: LPAL13_SCAF000058.

## Starting chromosome: LPAL13_SCAF000062.

## Starting chromosome: LPAL13_SCAF000063.

## Starting chromosome: LPAL13_SCAF000064.

## Starting chromosome: LPAL13_SCAF000065.

## Starting chromosome: LPAL13_SCAF000066.

## Starting chromosome: LPAL13_SCAF000067.

## Starting chromosome: LPAL13_SCAF000070.

## Starting chromosome: LPAL13_SCAF000071.

## Starting chromosome: LPAL13_SCAF000073.

## Starting chromosome: LPAL13_SCAF000075.

## Starting chromosome: LPAL13_SCAF000076.

## Starting chromosome: LPAL13_SCAF000078.

## Starting chromosome: LPAL13_SCAF000080.

## Starting chromosome: LPAL13_SCAF000084.

## Starting chromosome: LPAL13_SCAF000085.

## Starting chromosome: LPAL13_SCAF000086.

## Starting chromosome: LPAL13_SCAF000088.

## Starting chromosome: LPAL13_SCAF000089.

## Starting chromosome: LPAL13_SCAF000090.

## Starting chromosome: LPAL13_SCAF000092.

## Starting chromosome: LPAL13_SCAF000095.

## Starting chromosome: LPAL13_SCAF000097.

## Starting chromosome: LPAL13_SCAF000101.

## Starting chromosome: LPAL13_SCAF000103.

## Starting chromosome: LPAL13_SCAF000109.

## Starting chromosome: LPAL13_SCAF000110.

## Starting chromosome: LPAL13_SCAF000111.

## Starting chromosome: LPAL13_SCAF000112.

## Starting chromosome: LPAL13_SCAF000113.

## Starting chromosome: LPAL13_SCAF000114.

## Starting chromosome: LPAL13_SCAF000115.

## Starting chromosome: LPAL13_SCAF000117.

## Starting chromosome: LPAL13_SCAF000118.

## Starting chromosome: LPAL13_SCAF000119.

## Starting chromosome: LPAL13_SCAF000122.

## Starting chromosome: LPAL13_SCAF000124.

## Starting chromosome: LPAL13_SCAF000125.

## Starting chromosome: LPAL13_SCAF000137.

## Starting chromosome: LPAL13_SCAF000138.

## Starting chromosome: LPAL13_SCAF000139.

## Starting chromosome: LPAL13_SCAF000140.

## Starting chromosome: LPAL13_SCAF000142.

## Starting chromosome: LPAL13_SCAF000144.

## Starting chromosome: LPAL13_SCAF000145.

## Starting chromosome: LPAL13_SCAF000146.

## Starting chromosome: LPAL13_SCAF000147.

## Starting chromosome: LPAL13_SCAF000148.

## Starting chromosome: LPAL13_SCAF000150.

## Starting chromosome: LPAL13_SCAF000151.

## Starting chromosome: LPAL13_SCAF000152.

## Starting chromosome: LPAL13_SCAF000154.

## Starting chromosome: LPAL13_SCAF000155.

## Starting chromosome: LPAL13_SCAF000156.

## Starting chromosome: LPAL13_SCAF000158.

## Starting chromosome: LPAL13_SCAF000159.

## Starting chromosome: LPAL13_SCAF000160.

## Starting chromosome: LPAL13_SCAF000163.

## Starting chromosome: LPAL13_SCAF000164.

## Starting chromosome: LPAL13_SCAF000165.

## Starting chromosome: LPAL13_SCAF000166.

## Starting chromosome: LPAL13_SCAF000168.

## Starting chromosome: LPAL13_SCAF000169.

## Starting chromosome: LPAL13_SCAF000171.

## Starting chromosome: LPAL13_SCAF000172.

## Starting chromosome: LPAL13_SCAF000174.

## Starting chromosome: LPAL13_SCAF000175.

## Starting chromosome: LPAL13_SCAF000176.

## Starting chromosome: LPAL13_SCAF000177.

## Starting chromosome: LPAL13_SCAF000180.

## Starting chromosome: LPAL13_SCAF000184.

## Starting chromosome: LPAL13_SCAF000185.

## Starting chromosome: LPAL13_SCAF000188.

## Starting chromosome: LPAL13_SCAF000190.

## Starting chromosome: LPAL13_SCAF000191.

## Starting chromosome: LPAL13_SCAF000192.

## Starting chromosome: LPAL13_SCAF000194.

## Starting chromosome: LPAL13_SCAF000195.

## Starting chromosome: LPAL13_SCAF000196.

## Starting chromosome: LPAL13_SCAF000197.

## Starting chromosome: LPAL13_SCAF000198.

## Starting chromosome: LPAL13_SCAF000199.

## Starting chromosome: LPAL13_SCAF000200.

## Starting chromosome: LPAL13_SCAF000201.

## Starting chromosome: LPAL13_SCAF000202.

## Starting chromosome: LPAL13_SCAF000203.

## Starting chromosome: LPAL13_SCAF000204.

## Starting chromosome: LPAL13_SCAF000206.

## Starting chromosome: LPAL13_SCAF000209.

## Starting chromosome: LPAL13_SCAF000211.

## Starting chromosome: LPAL13_SCAF000212.

## Starting chromosome: LPAL13_SCAF000219.

## Starting chromosome: LPAL13_SCAF000221.

## Starting chromosome: LPAL13_SCAF000223.

## Starting chromosome: LPAL13_SCAF000226.

## Starting chromosome: LPAL13_SCAF000227.

## Starting chromosome: LPAL13_SCAF000229.

## Starting chromosome: LPAL13_SCAF000233.

## Starting chromosome: LPAL13_SCAF000235.

## Starting chromosome: LPAL13_SCAF000237.

## Starting chromosome: LPAL13_SCAF000240.

## Starting chromosome: LPAL13_SCAF000241.

## Starting chromosome: LPAL13_SCAF000242.

## Starting chromosome: LPAL13_SCAF000244.

## Starting chromosome: LPAL13_SCAF000245.

## Starting chromosome: LPAL13_SCAF000246.

## Starting chromosome: LPAL13_SCAF000247.

## Starting chromosome: LPAL13_SCAF000248.

## Starting chromosome: LPAL13_SCAF000249.

## Starting chromosome: LPAL13_SCAF000250.

## Starting chromosome: LPAL13_SCAF000251.

## Starting chromosome: LPAL13_SCAF000252.

## Starting chromosome: LPAL13_SCAF000253.

## Starting chromosome: LPAL13_SCAF000254.

## Starting chromosome: LPAL13_SCAF000255.

## Starting chromosome: LPAL13_SCAF000257.

## Starting chromosome: LPAL13_SCAF000258.

## Starting chromosome: LPAL13_SCAF000260.

## Starting chromosome: LPAL13_SCAF000261.

## Starting chromosome: LPAL13_SCAF000262.

## Starting chromosome: LPAL13_SCAF000263.

## Starting chromosome: LPAL13_SCAF000264.

## Starting chromosome: LPAL13_SCAF000265.

## Starting chromosome: LPAL13_SCAF000266.

## Starting chromosome: LPAL13_SCAF000267.

## Starting chromosome: LPAL13_SCAF000269.

## Starting chromosome: LPAL13_SCAF000270.

## Starting chromosome: LPAL13_SCAF000271.

## Starting chromosome: LPAL13_SCAF000274.

## Starting chromosome: LPAL13_SCAF000275.

## Starting chromosome: LPAL13_SCAF000276.

## Starting chromosome: LPAL13_SCAF000277.

## Starting chromosome: LPAL13_SCAF000279.

## Starting chromosome: LPAL13_SCAF000280.

## Starting chromosome: LPAL13_SCAF000282.

## Starting chromosome: LPAL13_SCAF000283.

## Starting chromosome: LPAL13_SCAF000284.

## Starting chromosome: LPAL13_SCAF000287.

## Starting chromosome: LPAL13_SCAF000288.

## Starting chromosome: LPAL13_SCAF000289.

## Starting chromosome: LPAL13_SCAF000290.

## Starting chromosome: LPAL13_SCAF000292.

## Starting chromosome: LPAL13_SCAF000293.

## Starting chromosome: LPAL13_SCAF000294.

## Starting chromosome: LPAL13_SCAF000295.

## Starting chromosome: LPAL13_SCAF000298.

## Starting chromosome: LPAL13_SCAF000299.

## Starting chromosome: LPAL13_SCAF000302.

## Starting chromosome: LPAL13_SCAF000303.

## Starting chromosome: LPAL13_SCAF000306.

## Starting chromosome: LPAL13_SCAF000307.

## Starting chromosome: LPAL13_SCAF000308.

## Starting chromosome: LPAL13_SCAF000310.

## Starting chromosome: LPAL13_SCAF000312.

## Starting chromosome: LPAL13_SCAF000315.

## Starting chromosome: LPAL13_SCAF000316.

## Starting chromosome: LPAL13_SCAF000319.

## Starting chromosome: LPAL13_SCAF000325.

## Starting chromosome: LPAL13_SCAF000327.

## Starting chromosome: LPAL13_SCAF000331.

## Starting chromosome: LPAL13_SCAF000332.

## Starting chromosome: LPAL13_SCAF000334.

## Starting chromosome: LPAL13_SCAF000343.

## Starting chromosome: LPAL13_SCAF000344.

## Starting chromosome: LPAL13_SCAF000347.

## Starting chromosome: LPAL13_SCAF000348.

## Starting chromosome: LPAL13_SCAF000349.

## Starting chromosome: LPAL13_SCAF000350.

## Starting chromosome: LPAL13_SCAF000351.

## Starting chromosome: LPAL13_SCAF000352.

## Starting chromosome: LPAL13_SCAF000355.

## Starting chromosome: LPAL13_SCAF000357.

## Starting chromosome: LPAL13_SCAF000358.

## Starting chromosome: LPAL13_SCAF000360.

## Starting chromosome: LPAL13_SCAF000361.

## Starting chromosome: LPAL13_SCAF000362.

## Starting chromosome: LPAL13_SCAF000364.

## Starting chromosome: LPAL13_SCAF000368.

## Starting chromosome: LPAL13_SCAF000373.

## Starting chromosome: LPAL13_SCAF000375.

## Starting chromosome: LPAL13_SCAF000377.

## Starting chromosome: LPAL13_SCAF000379.

## Starting chromosome: LPAL13_SCAF000382.

## Starting chromosome: LPAL13_SCAF000383.

## Starting chromosome: LPAL13_SCAF000385.

## Starting chromosome: LPAL13_SCAF000387.

## Starting chromosome: LPAL13_SCAF000388.

## Starting chromosome: LPAL13_SCAF000389.

## Starting chromosome: LPAL13_SCAF000392.

## Starting chromosome: LPAL13_SCAF000393.

## Starting chromosome: LPAL13_SCAF000394.

## Starting chromosome: LPAL13_SCAF000395.

## Starting chromosome: LPAL13_SCAF000396.

## Starting chromosome: LPAL13_SCAF000397.

## Starting chromosome: LPAL13_SCAF000398.

## Starting chromosome: LPAL13_SCAF000399.

## Starting chromosome: LPAL13_SCAF000400.

## Starting chromosome: LPAL13_SCAF000403.

## Starting chromosome: LPAL13_SCAF000407.

## Starting chromosome: LPAL13_SCAF000408.

## Starting chromosome: LPAL13_SCAF000409.

## Starting chromosome: LPAL13_SCAF000412.

## Starting chromosome: LPAL13_SCAF000413.

## Starting chromosome: LPAL13_SCAF000417.

## Starting chromosome: LPAL13_SCAF000418.

## Starting chromosome: LPAL13_SCAF000420.

## Starting chromosome: LPAL13_SCAF000421.

## Starting chromosome: LPAL13_SCAF000422.

## Starting chromosome: LPAL13_SCAF000423.

## Starting chromosome: LPAL13_SCAF000424.

## Starting chromosome: LPAL13_SCAF000426.

## Starting chromosome: LPAL13_SCAF000431.

## Starting chromosome: LPAL13_SCAF000434.

## Starting chromosome: LPAL13_SCAF000441.

## Starting chromosome: LPAL13_SCAF000442.

## Starting chromosome: LPAL13_SCAF000444.

## Starting chromosome: LPAL13_SCAF000445.

## Starting chromosome: LPAL13_SCAF000447.

## Starting chromosome: LPAL13_SCAF000449.

## Starting chromosome: LPAL13_SCAF000450.

## Starting chromosome: LPAL13_SCAF000454.

## Starting chromosome: LPAL13_SCAF000455.

## Starting chromosome: LPAL13_SCAF000457.

## Starting chromosome: LPAL13_SCAF000458.

## Starting chromosome: LPAL13_SCAF000459.

## Starting chromosome: LPAL13_SCAF000461.

## Starting chromosome: LPAL13_SCAF000462.

## Starting chromosome: LPAL13_SCAF000463.

## Starting chromosome: LPAL13_SCAF000464.

## Starting chromosome: LPAL13_SCAF000465.

## Starting chromosome: LPAL13_SCAF000466.

## Starting chromosome: LPAL13_SCAF000467.

## Starting chromosome: LPAL13_SCAF000470.

## Starting chromosome: LPAL13_SCAF000472.

## Starting chromosome: LPAL13_SCAF000473.

## Starting chromosome: LPAL13_SCAF000474.

## Starting chromosome: LPAL13_SCAF000475.

## Starting chromosome: LPAL13_SCAF000476.

## Starting chromosome: LPAL13_SCAF000477.

## Starting chromosome: LPAL13_SCAF000482.

## Starting chromosome: LPAL13_SCAF000483.

## Starting chromosome: LPAL13_SCAF000485.

## Starting chromosome: LPAL13_SCAF000489.

## Starting chromosome: LPAL13_SCAF000492.

## Starting chromosome: LPAL13_SCAF000495.

## Starting chromosome: LPAL13_SCAF000496.

## Starting chromosome: LPAL13_SCAF000497.

## Starting chromosome: LPAL13_SCAF000498.

## Starting chromosome: LPAL13_SCAF000499.

## Starting chromosome: LPAL13_SCAF000503.

## Starting chromosome: LPAL13_SCAF000504.

## Starting chromosome: LPAL13_SCAF000506.

## Starting chromosome: LPAL13_SCAF000510.

## Starting chromosome: LPAL13_SCAF000511.

## Starting chromosome: LPAL13_SCAF000512.

## Starting chromosome: LPAL13_SCAF000513.

## Starting chromosome: LPAL13_SCAF000514.

## Starting chromosome: LPAL13_SCAF000515.

## Starting chromosome: LPAL13_SCAF000516.

## Starting chromosome: LPAL13_SCAF000520.

## Starting chromosome: LPAL13_SCAF000521.

## Starting chromosome: LPAL13_SCAF000522.

## Starting chromosome: LPAL13_SCAF000525.

## Starting chromosome: LPAL13_SCAF000532.

## Starting chromosome: LPAL13_SCAF000541.

## Starting chromosome: LPAL13_SCAF000544.

## Starting chromosome: LPAL13_SCAF000550.

## Starting chromosome: LPAL13_SCAF000554.

## Starting chromosome: LPAL13_SCAF000555.

## Starting chromosome: LPAL13_SCAF000557.

## Starting chromosome: LPAL13_SCAF000565.

## Starting chromosome: LPAL13_SCAF000569.

## Starting chromosome: LPAL13_SCAF000573.

## Starting chromosome: LPAL13_SCAF000575.

## Starting chromosome: LPAL13_SCAF000576.

## Starting chromosome: LPAL13_SCAF000577.

## Starting chromosome: LPAL13_SCAF000581.

## Starting chromosome: LPAL13_SCAF000582.

## Starting chromosome: LPAL13_SCAF000584.

## Starting chromosome: LPAL13_SCAF000585.

## Starting chromosome: LPAL13_SCAF000588.

## Starting chromosome: LPAL13_SCAF000589.

## Starting chromosome: LPAL13_SCAF000590.

## Starting chromosome: LPAL13_SCAF000591.

## Starting chromosome: LPAL13_SCAF000593.

## Starting chromosome: LPAL13_SCAF000595.

## Starting chromosome: LPAL13_SCAF000596.

## Starting chromosome: LPAL13_SCAF000599.

## Starting chromosome: LPAL13_SCAF000600.

## Starting chromosome: LPAL13_SCAF000601.

## Starting chromosome: LPAL13_SCAF000603.

## Starting chromosome: LPAL13_SCAF000604.

## Starting chromosome: LPAL13_SCAF000605.

## Starting chromosome: LPAL13_SCAF000606.

## Starting chromosome: LPAL13_SCAF000608.

## Starting chromosome: LPAL13_SCAF000609.

## Starting chromosome: LPAL13_SCAF000610.

## Starting chromosome: LPAL13_SCAF000611.

## Starting chromosome: LPAL13_SCAF000612.

## Starting chromosome: LPAL13_SCAF000613.

## Starting chromosome: LPAL13_SCAF000615.

## Starting chromosome: LPAL13_SCAF000618.

## Starting chromosome: LPAL13_SCAF000619.

## Starting chromosome: LPAL13_SCAF000620.

## Starting chromosome: LPAL13_SCAF000621.

## Starting chromosome: LPAL13_SCAF000627.

## Starting chromosome: LPAL13_SCAF000631.

## Starting chromosome: LPAL13_SCAF000633.

## Starting chromosome: LPAL13_SCAF000635.

## Starting chromosome: LPAL13_SCAF000637.

## Starting chromosome: LPAL13_SCAF000642.

## Starting chromosome: LPAL13_SCAF000643.

## Starting chromosome: LPAL13_SCAF000644.

## Starting chromosome: LPAL13_SCAF000647.

## Starting chromosome: LPAL13_SCAF000648.

## Starting chromosome: LPAL13_SCAF000649.

## Starting chromosome: LPAL13_SCAF000650.

## Starting chromosome: LPAL13_SCAF000652.

## Starting chromosome: LPAL13_SCAF000654.

## Starting chromosome: LPAL13_SCAF000657.

## Starting chromosome: LPAL13_SCAF000660.

## Starting chromosome: LPAL13_SCAF000663.

## Starting chromosome: LPAL13_SCAF000664.

## Starting chromosome: LPAL13_SCAF000665.

## Starting chromosome: LPAL13_SCAF000666.

## Starting chromosome: LPAL13_SCAF000667.

## Starting chromosome: LPAL13_SCAF000669.

## Starting chromosome: LPAL13_SCAF000670.

## Starting chromosome: LPAL13_SCAF000673.

## Starting chromosome: LPAL13_SCAF000675.

## Starting chromosome: LPAL13_SCAF000676.

## Starting chromosome: LPAL13_SCAF000677.

## Starting chromosome: LPAL13_SCAF000678.

## Starting chromosome: LPAL13_SCAF000680.

## Starting chromosome: LPAL13_SCAF000682.

## Starting chromosome: LPAL13_SCAF000683.

## Starting chromosome: LPAL13_SCAF000684.

## Starting chromosome: LPAL13_SCAF000686.

## Starting chromosome: LPAL13_SCAF000687.

## Starting chromosome: LPAL13_SCAF000689.

## Starting chromosome: LPAL13_SCAF000691.

## Starting chromosome: LPAL13_SCAF000693.

## Starting chromosome: LPAL13_SCAF000694.

## Starting chromosome: LPAL13_SCAF000697.

## Starting chromosome: LPAL13_SCAF000699.

## Starting chromosome: LPAL13_SCAF000700.

## Starting chromosome: LPAL13_SCAF000701.

## Starting chromosome: LPAL13_SCAF000702.

## Starting chromosome: LPAL13_SCAF000703.

## Starting chromosome: LPAL13_SCAF000705.

## Starting chromosome: LPAL13_SCAF000706.

## Starting chromosome: LPAL13_SCAF000707.

## Starting chromosome: LPAL13_SCAF000708.

## Starting chromosome: LPAL13_SCAF000710.

## Starting chromosome: LPAL13_SCAF000713.

## Starting chromosome: LPAL13_SCAF000715.

## Starting chromosome: LPAL13_SCAF000717.

## Starting chromosome: LPAL13_SCAF000718.

## Starting chromosome: LPAL13_SCAF000720.

## Starting chromosome: LPAL13_SCAF000721.

## Starting chromosome: LPAL13_SCAF000722.

## Starting chromosome: LPAL13_SCAF000723.

## Starting chromosome: LPAL13_SCAF000724.

## Starting chromosome: LPAL13_SCAF000727.

## Starting chromosome: LPAL13_SCAF000729.

## Starting chromosome: LPAL13_SCAF000733.

## Starting chromosome: LPAL13_SCAF000734.

## Starting chromosome: LPAL13_SCAF000735.

## Starting chromosome: LPAL13_SCAF000736.

## Starting chromosome: LPAL13_SCAF000737.

## Starting chromosome: LPAL13_SCAF000738.

## Starting chromosome: LPAL13_SCAF000741.

## Starting chromosome: LPAL13_SCAF000742.

## Starting chromosome: LPAL13_SCAF000743.

## Starting chromosome: LPAL13_SCAF000745.

## Starting chromosome: LPAL13_SCAF000746.

## Starting chromosome: LPAL13_SCAF000747.

## Starting chromosome: LPAL13_SCAF000751.

## Starting chromosome: LPAL13_SCAF000754.

## Starting chromosome: LPAL13_SCAF000755.

## Starting chromosome: LPAL13_SCAF000757.

## Starting chromosome: LPAL13_SCAF000758.

## Starting chromosome: LPAL13_SCAF000759.

## Starting chromosome: LPAL13_SCAF000760.

## Starting chromosome: LPAL13_SCAF000761.

## Starting chromosome: LPAL13_SCAF000762.

## Starting chromosome: LPAL13_SCAF000764.

## Starting chromosome: LPAL13_SCAF000765.

## Starting chromosome: LPAL13_SCAF000766.

## Starting chromosome: LPAL13_SCAF000767.

## Starting chromosome: LPAL13_SCAF000768.

## Starting chromosome: LPAL13_SCAF000769.

## Starting chromosome: LPAL13_SCAF000770.

## Starting chromosome: LPAL13_SCAF000771.

## Starting chromosome: LPAL13_SCAF000773.

## Starting chromosome: LPAL13_SCAF000774.

## Starting chromosome: LPAL13_SCAF000776.

## Starting chromosome: LPAL13_SCAF000777.

## Starting chromosome: LPAL13_SCAF000778.

## Starting chromosome: LPAL13_SCAF000779.

## Starting chromosome: LPAL13_SCAF000780.

## Starting chromosome: LPAL13_SCAF000781.

## Starting chromosome: LPAL13_SCAF000782.

## Starting chromosome: LPAL13_SCAF000783.

## Starting chromosome: LPAL13_SCAF000784.

## Starting chromosome: LPAL13_SCAF000785.

## Starting chromosome: LPAL13_SCAF000786.

## Starting chromosome: LPAL13_SCAF000787.

## Starting chromosome: LPAL13_SCAF000789.

## Starting chromosome: LPAL13_SCAF000791.

## Starting chromosome: LPAL13_SCAF000794.

## Starting chromosome: LPAL13_SCAF000795.

## Starting chromosome: LPAL13_SCAF000796.

## Starting chromosome: LPAL13_SCAF000797.

## Starting chromosome: LPAL13_SCAF000798.

## Starting chromosome: LPAL13_SCAF000799.

## Starting chromosome: LPAL13_SCAF000803.

## Starting chromosome: LPAL13_SCAF000804.

## Starting chromosome: LPAL13_SCAF000805.

## Starting chromosome: LPAL13_SCAF000806.

## Starting chromosome: LPAL13_SCAF000807.

## Starting chromosome: LPAL13_SCAF000809.

## Starting chromosome: LPAL13_SCAF000810.

## Starting chromosome: LPAL13_SCAF000811.

## Starting chromosome: LPAL13_SCAF000812.

## Starting chromosome: LPAL13_SCAF000813.

## Starting chromosome: LPAL13_SCAF000815.

## Starting chromosome: LPAL13_SCAF000816.

## Starting chromosome: LPAL13_SCAF000818.

## Starting chromosome: LPAL13_SCAF000819.

## Extracting primer regions.

## Searching for overlapping/closest genes.

## 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.

## Now the annotation has 8665 rows.

## Dropped 11 regions with Ns in the 5' region.

## Dropped 9 regions with Ns in the 3' region.

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)

9.3 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[["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[["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")

9.4 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)
pp(file = "images/onlyz22_z23_snp_heatmap.pdf", image=zymo_heat[["plot"]])

zymo_heat[["plot"]]

9.4.1 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)
map1 <- 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

pp(file = "images/dendro_heatmap.png", image = map1, height = 20, width = 20)

## Warning in pp(file = "images/dendro_heatmap.png", image = map1, height = 20, :
## There is no device to shut down.

## annotated Heatmap
## 
## Rows: 'dendrogram' with 2 branches and 100 members total, at height 6.173 
##   11  annotation variable(s)
## Cols: 'dendrogram' with 2 branches and 100 members total, at height 6.173 
##   10  annotation variable(s)

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_git

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%)
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##  Plots are stored in: /mnt/cbcb/fs01_abelew/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_git

SNP Density

9.5 Try out MatrixEQTL

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

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

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

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

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

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

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

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

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

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

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

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

## If you wish to reproduce this exact build of hpgltools, invoke the following:

## > git clone http://github.com/abelew/hpgltools.git

## > git reset cc2fec1cb27347df846cacd2578dfa1a9312f14e

## This is hpgltools commit: Wed Feb 23 13:53:56 2022 -0500: cc2fec1cb27347df846cacd2578dfa1a9312f14e

## Saving to tmrc2_reorganized_2022.rda.xz

tmp <- loadme(filename = savefile)