sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_20220106.xlsx")

1 Introduction

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

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

In all cases the processing performed was:

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

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

2 Annotations

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

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

tt <- sm(library(EuPathDB))
tt <- sm(library(org.Lpanamensis.MHOMCOL81L13.v46.eg.db))
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")

3 Load a genome

meta <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
## Unable to find species names for 2 species.
## Leishmania sp. Ghana 2012 LV757 MHOMGH2012GH5, Leishmania sp. Namibia MPRO/NA/1975/252/LV425
## Appending to an existing file: EuPathDB/metadata/biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/GRanges_biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/OrgDb_biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/TxDb_biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/OrganismDbi_biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/BSgenome_biocv3.14_tritrypdbv55_metadata.csv
## Appending to an existing file: EuPathDB/metadata/biocv3.14_tritrypdbv55_invalid_metadata.csv
## Appending to an existing file: EuPathDB/metadata/GRanges_biocv3.14_tritrypdbv55_invalid_metadata.csv
## Appending to an existing file: EuPathDB/metadata/OrgDb_biocv3.14_tritrypdbv55_invalid_metadata.csv
## Appending to an existing file: EuPathDB/metadata/TxDb_biocv3.14_tritrypdbv55_invalid_metadata.csv
## Appending to an existing file: EuPathDB/metadata/OrganismDbi_biocv3.14_tritrypdbv55_invalid_metadata.csv
## Appending to an existing file: EuPathDB/metadata/BSgenome_biocv3.14_tritrypdbv55_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))

4 TODO:

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

5 Generate Expressionsets and Sample Estimation

The process of sample estimation takes two primary inputs:

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

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

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

5.1 Notes

The following samples are much lower coverage:

  • TMRC20002
  • TMRC20006
  • TMRC20007
  • TMRC20008

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

5.2 TODO:

  1. Do the multi-gene family removal right here instead of way down at the bottom
  2. Add zymodeme snps to the annotation later.
  3. Start phylogenetic analysis of variant table.
sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "zymodemecategorical")
lp_expt <- sm(create_expt(sample_sheet,
                          gene_info = hisat_annot,
                          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)

## 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: ggrepel: 53 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)

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

5.3 Distribution Visualization

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

5.4 By Susceptilibity

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

  • 0 <= x <= 35 is resistant
  • 36 <= x <= 48 is ambiguous
  • 49 <= x is sensitive
starting <- as.numeric(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])
## Warning: NAs introduced by coercion
sus_categorical <- starting
na_idx <- is.na(starting)
sus_categorical[na_idx] <- "unknown"

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

pData(lp_expt$expressionset)[["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)

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.png", 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)

clinical_nb_tsne <- plot_tsne(clinical_nb, plot_title = "TSNE of parasite expression values")
clinical_nb_tsne$plot
## Warning: ggrepel: 68 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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

plot_sm(clinical_norm)$plot
## Performing correlation.

5.5 By Cure/Fail status

cf_colors <- list(
    "cure" = "#006f00",
    "fail" = "#9dffa0",
    "unknown" = "#cbcbcb",
    "notapplicable" = "#000000")
cf_expt <- set_expt_conditions(lp_expt, fact = "clinicalcategorical") %>%
  set_expt_batches(fact = sus_categorical) %>%
  set_expt_colors(cf_colors)
## Warning in set_expt_colors(., cf_colors): Colors for the following categories
## are not being used: notapplicable.
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)

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)

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"]])

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"]])

6 Zymodeme analyses

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

6.1 Differential expression

6.1.1 With respect to zymodeme attribution

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

zy_expt <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
## subset_expt(): There were 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 <- sm(combine_de_tables(zy_de, excel = glue::glue("excel/zy_tables-v{ver}.xlsx")))
zy_sig <- sm(extract_significant_genes(zy_table, excel = glue::glue("excel/zy_sig-v{ver}.xlsx")))

6.1.2 Images of zymodeme DE

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

6.2 With respect to cure/failure

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

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

6.3 With respect to susceptibility

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

sus_de <- 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")))
sus_sig <- sm(extract_significant_genes(sus_table, excel = glue::glue("excel/sus_sig-v{ver}.xlsx")))
knitr::kable(head(sus_sig$deseq$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_000035800 LPAL13_000035800 hypothetical protein protein coding LPAL13_SCAF000500 737 1006 - reverse Not Assigned 270.0 269 15.350 0 14.480 0e+00 10.660 0.2306 4.9910 -3.9520 17.904 0.3803 8.943 10.920 0e+00 0.0000 2911.00 1.1280 13.600 0 30205.04 14.883 0.1400 4530.73 3171.56 1.650e+07 1223.564 0.00 0.00 0.00 6.7310 83.11 0 2.1490 1.635 -4.2650 0.1070 2.807e-01 2.877e-38 6.208e-16 0.000e+00 6.641e-09 13.500 0.000e+00 0.000e+00 3.567e-02 3.816e-03
LPAL13_320026300 LPAL13_320026300 hypothetical protein, conserved protein coding LpaL13_32 754268 755485 - reverse 32 1218.0 1217 12.980 0 13.470 0e+00 9.953 0.2542 4.6270 -4.0140 18.533 0.6270 8.642 10.230 0e+00 0.0000 1527.00 1.0900 11.910 0 17204.67 14.070 0.1308 2423.22 1696.30 2.403e+06 629.468 0.00 0.00 0.00 5.8010 73.69 0 1.9250 1.562 -4.3400 0.1233 3.157e-01 6.162e-29 3.920e-14 0.000e+00 9.705e-09 11.980 1.188e-01 9.915e-03 4.110e-02 5.068e-03
LPAL13_000044900 LPAL13_000044900 actin-related protein 2, putative protein coding LPAL13_SCAF000645 507 1685 - reverse Not Assigned 1179.0 1178 12.190 0 13.610 0e+00 9.092 0.1953 3.7750 -4.1700 17.373 0.1233 7.944 10.000 0e+00 0.0000 849.30 1.1340 10.750 0 128040.28 16.966 0.0000 1280.39 896.27 6.350e+05 343.849 1.00 0.00 1.00 4.9560 69.01 0 1.2680 1.757 -4.1320 0.0837 2.367e-01 2.157e-23 3.415e-13 0.000e+00 3.138e-08 11.780 1.188e-01 1.008e-02 2.790e-02 2.335e-03
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 13478.05 13.718 0.0000 134.77 94.34 9.270e+03 36.806 1.00 0.00 1.00 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_000051300 LPAL13_000051300 hypothetical protein, conserved protein coding LPAL13_SCAF000772 11 2344 + forward Not Assigned 2334.0 2333 8.786 0 9.749 0e+00 4.677 0.1557 0.4463 -3.9870 11.397 0.5398 4.433 6.594 0e+00 0.0000 146.30 1.2060 7.288 0 2017.81 10.979 0.0962 214.30 150.04 8.669e+04 62.049 0.00 0.00 0.00 2.4650 36.51 0 -1.0200 1.909 -3.9620 0.0608 1.903e-01 1.304e-10 1.564e-07 0.000e+00 7.692e-06 7.477 1.518e+00 2.031e-01 2.025e-02 1.230e-03
LPAL13_000040700 LPAL13_000040700 hypothetical protein, conserved protein coding LPAL13_SCAF000598 54 1067 + forward Not Assigned 1014.0 1013 6.648 0 7.096 0e+00 3.375 0.0531 -1.0270 -3.9700 6.795 0.5482 2.943 5.481 0e+00 0.0001 21.87 0.9994 6.652 0 227.13 7.827 0.1428 34.70 24.33 9.986e+02 9.762 0.00 0.00 0.00 -0.0905 32.62 0 -2.2010 2.554 -3.1400 0.0130 6.566e-02 5.267e-09 7.944e-07 0.000e+00 8.347e-05 5.792 7.091e-01 1.224e-01 4.337e-03 5.642e-05
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 72.70 6.184 13.2097 961.12 676.75 4.516e+05 58.224 0.00 1.00 0.00 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_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 59.75 5.901 2.1189 127.20 89.67 1.056e+04 22.395 0.00 0.00 0.00 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_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 2690.50 11.394 0.0000 26.89 18.83 5.831e+02 7.965 1.00 0.00 1.00 -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_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 2013.65 10.976 0.0000 20.13 14.09 5.608e+02 6.299 1.00 0.00 1.00 -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_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 43.77 5.452 0.8450 37.41 26.44 2.057e+03 8.868 0.00 0.00 0.00 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_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 24.99 4.643 8.3178 208.07 148.14 2.549e+04 17.788 0.00 1.00 0.00 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_080010800 LPAL13_080010800 hypothetical protein protein coding LpaL13_08 199409 199792 - reverse 8 384.0 383 5.448 0 6.865 0e+00 2.069 0.1208 -2.1170 -4.1700 4.788 0.1233 2.053 4.821 0e+00 0.0006 11.10 1.0070 5.407 0 1600.96 10.645 0.0000 16.00 11.20 4.009e+02 5.222 1.00 0.00 1.00 -0.8462 30.73 0 -3.0170 2.073 -3.7940 0.0422 1.489e-01 3.581e-06 2.225e-06 0.000e+00 4.671e-04 4.368 3.348e+00 7.664e-01 1.405e-02 5.922e-04
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 31.86 4.994 9.5389 304.18 215.79 6.840e+04 23.769 0.00 1.00 0.00 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_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 42.99 5.426 1.0338 44.87 31.72 1.873e+03 10.109 0.00 0.00 0.00 -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_000011800 LPAL13_000011800 hypothetical protein, conserved protein coding LPAL13_SCAF000076 446 640 - reverse Not Assigned 195.0 194 5.215 0 5.876 0e+00 1.508 0.2266 -2.2090 -3.9770 4.642 0.5524 1.768 3.842 5e-04 0.0039 11.97 0.9815 5.313 0 100.45 6.650 0.1540 16.46 11.57 6.253e+02 5.470 0.42 0.58 0.42 -0.8171 26.37 0 -2.9410 1.650 -4.2470 0.1039 2.807e-01 4.541e-06 1.267e-05 6.120e-01 3.178e-03 3.750 3.144e+00 8.385e-01 3.463e-02 3.598e-03
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 18.87 4.238 51.0721 963.80 689.98 4.309e+05 18.173 0.00 1.00 0.00 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_000026500 LPAL13_000026500 hypothetical protein protein coding LPAL13_SCAF000301 144 494 - reverse Not Assigned 351.0 350 4.424 0 4.383 0e+00 3.024 0.0953 0.2999 -2.3730 5.646 2.0132 2.673 4.398 1e-04 0.0012 46.88 0.7218 6.129 0 25.13 4.651 3.0463 76.80 54.67 5.564e+03 12.336 0.00 1.00 0.00 0.9356 28.87 0 -0.7437 2.214 -3.5240 0.0304 1.179e-01 1.053e-07 4.659e-06 9.682e-01 1.058e-03 3.944 1.908e-03 4.838e-04 1.013e-02 3.081e-04
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 16.74 4.065 12.1331 203.23 145.90 1.498e+04 13.093 0.00 1.00 0.00 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 13.63 3.768 32.7940 446.95 322.70 8.664e+04 12.589 0.00 1.00 0.00 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 
knitr::kable(head(sus_sig$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 0e+00 -3.3430 3.4550 12.7819 0.0608 -6.797 -10.000 0 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 12.97 0.0003 -0.9429 -8.222 13.700 0 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_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 0e+00 4.6300 8.2100 3.5430 0.0361 -3.581 -9.948 0 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 31.12 0.0000 5.7700 -6.294 8.671 0 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_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 0e+00 -2.1430 1.1750 2.1229 0.2166 -3.318 -10.830 0 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 34.37 0.0000 -1.4530 -8.082 12.190 0 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_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 0e+00 4.7250 7.1000 0.6657 1.0271 -2.375 -7.180 0 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 64.15 0.0000 5.4100 -7.759 14.430 0 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_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 0e+00 -4.1400 -0.4281 4.1920 0.4519 -3.712 -8.576 0 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 14.90 0.0001 -3.1990 -7.723 8.857 0 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_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 0e+00 0.1042 3.9370 8.7982 0.1681 -3.832 -6.689 0 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 18.17 0.0000 1.3280 -5.852 6.927 0 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_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 0e+00 1.4340 3.7450 1.6362 0.2106 -2.311 -8.385 0 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 43.82 0.0000 2.0540 -6.751 10.360 0 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_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 0e+00 1.1870 4.2690 4.1252 0.1033 -3.081 -7.803 0 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 26.01 0.0000 1.9780 -5.646 6.302 0 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_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 0e+00 -0.9701 2.1590 4.1051 0.7645 -3.129 -6.824 0 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 16.24 0.0001 -0.2054 -6.412 8.198 0 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_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 0e+00 -1.7270 0.9896 3.2962 0.5154 -2.717 -6.778 0 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 26.16 0.0000 -1.1310 -7.852 11.790 0 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_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 0e+00 -1.9850 1.3650 4.9749 0.6608 -3.350 -6.945 0 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 12.84 0.0003 -1.2750 -6.274 7.073 0 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_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 0e+00 3.9610 6.0230 0.5051 0.5074 -2.062 -8.394 0 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 60.97 0.0000 4.5830 -8.750 18.360 0 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_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 0e+00 -2.2070 1.1620 6.5920 0.6332 -3.370 -6.277 0 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 10.72 0.0011 -1.3790 -5.597 4.952 0 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_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 0e+00 -0.0244 2.6770 3.6342 0.1568 -2.701 -7.147 0 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 14.27 0.0002 0.4891 -5.487 5.509 0 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_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 0e+00 4.6280 6.7120 0.7919 0.0938 -2.084 -10.970 0 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 47.03 0.0000 5.2740 -7.285 12.540 0 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_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 0e+00 1.3560 3.2620 1.0759 0.1243 -1.906 -8.631 0 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 35.86 0.0000 1.9690 -6.586 9.689 0 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_330021800 LPAL13_330021800 lipase precursor-like protein protein coding LpaL13_33 571895 572905 + forward 33 1011.0 1010 -1.576 0.0000 -1.576 0.0000 -1.595 0e+00 6.4530 8.1050 0.6133 0.5172 -1.652 -6.480 0 0e+00 4358.000 0.2767 -5.695 0.0000 0.3083 -1.6977 9733.17 3000.563 5020.346 1.650e+07 0.3126 0.0000 1.0000 0.0000 7.3110 39.18 0.0000 6.9130 -5.664 6.219 0 3.616e-05 6.451e-07 4.515e-08 1.000e+00 4.736e-05 -1.620 6.154e-02 -3.799e-02 1.274e-07 4.399e-14
LPAL13_140019200 LPAL13_140019200 inositol-3-phosphate synthase protein coding LpaL13_14 527711 529291 + INO1 forward 14 1581.0 1580 -1.465 0.0000 -1.463 0.0000 -1.540 0e+00 8.8290 10.4100 0.2203 0.4390 -1.579 -7.488 0 0e+00 19840.000 0.2244 -6.528 0.0000 0.3520 -1.5065 40764.45 14347.428 22272.534 2.188e+08 0.3546 0.0000 1.0000 0.0000 9.4970 50.86 0.0000 9.2710 -6.797 10.650 0 1.585e-06 9.542e-09 3.150e-10 1.000e+00 4.504e-05 -1.544 5.157e-02 -3.340e-02 1.385e-09 5.474e-18
LPAL13_330021900 LPAL13_330021900 hypothetical protein, conserved protein coding LpaL13_33 574061 574276 + forward 33 216.0 215 -1.454 0.0000 -1.452 0.0000 -1.764 0e+00 2.2980 4.0580 0.6084 0.8373 -1.759 -5.816 0 3e-04 260.300 0.2674 -5.439 0.0000 0.2851 -1.8106 620.52 176.883 309.975 1.118e+05 0.2952 0.0007 0.9993 0.0007 3.2420 30.71 0.0000 2.8150 -6.296 8.738 0 5.888e-06 3.031e-06 1.890e-06 9.791e-01 2.490e-04 -1.614 6.533e-03 -4.047e-03 3.803e-08 1.813e-16
LPAL13_210015500 LPAL13_210015500 core histone h2a/h2b/h3/h4, putative protein coding LpaL13_21 326108 326506 + forward 21 399.0 398 -1.410 0.0012 -1.410 0.0003 -1.831 3e-04 4.9850 6.6460 2.6364 0.2834 -1.661 -4.840 0 4e-04 2218.000 0.3640 -3.875 0.0001 0.5020 -0.9943 3301.46 1657.261 2150.521 3.170e+06 0.5213 0.9841 0.0159 0.9841 6.3340 18.83 0.0000 5.6570 -4.790 3.040 0 2.888e-04 1.217e-03 2.228e-04 4.526e-02 4.717e-04 -1.600 2.080e-03 -1.300e-03 4.373e-05 2.979e-09 
sus_ma <- sus_table[["plots"]][["sensitive_vs_resistant"]][["deseq_ma_plots"]][["plot"]]
pp(file = "images/sus_ma.png", image = sus_ma)

## test <- ggplt(sus_ma)

6.4 Ontology searches

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

## Gene categories more represented in the 2.3 group.
zy_go_up <- sm(simple_goseq(sig_genes = zy_sig[["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_sig[["deseq"]][["downs"]][[1]],
                              go_db = lp_go, length_db = lp_lengths))

6.4.1 A couple plots from the differential expression

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

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

zy_table[["venns"]][[1]][["p_lfc1"]][["up_noweight"]]

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

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

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

zy_go_up$pvalue_plots$bpp_plot_over

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

zy_go_down$pvalue_plots$bpp_plot_over

6.5 Look for agreement between sensitivity and zymodemes

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

zy_df <- zy_table[["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)

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

6.6 Zymodeme enzyme gene IDs

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

They are:

  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.

6.6.1 Expression levels of zymodeme genes

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

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

zymo_expt <- exclude_genes_expt(zy_norm, ids = my_genes, method = "keep")
## Before removal, there were 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

6.7 Empirically observed Zymodeme genes from differential expression analysis

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

shared_zymo <- intersect_significant(zy_table)
## Deleting the file excel/intersect_significant.xlsx before writing the tables.
up_shared <- shared_zymo[["ups"]][[1]][["data"]][["all"]]
rownames(up_shared)
##  [1] "LPAL13_000033300" "LPAL13_000012000" "LPAL13_000038500" "LPAL13_000012100"
##  [5] "LPAL13_310031300" "LPAL13_000038400" "LPAL13_340039600" "LPAL13_050005000"
##  [9] "LPAL13_310031000" "LPAL13_310039200" "LPAL13_350063000" "LPAL13_180013900"
## [13] "LPAL13_140019300" "LPAL13_210015500" "LPAL13_340039700" "LPAL13_350013200"
## [17] "LPAL13_170015400" "LPAL13_250006300" "LPAL13_330024000" "LPAL13_350073400"
## [21] "LPAL13_140019100" "LPAL13_320038700" "LPAL13_210005000" "LPAL13_140019200"
## [25] "LPAL13_240009700" "LPAL13_000052700" "LPAL13_330021800" "LPAL13_160014500"
## [29] "LPAL13_230011200" "LPAL13_350073200" "LPAL13_330021900" "LPAL13_050009600"
## [33] "LPAL13_250025700" "LPAL13_230011500" "LPAL13_160014100" "LPAL13_040007800"
## [37] "LPAL13_230011400" "LPAL13_020006700" "LPAL13_310028500" "LPAL13_230011300"
## [41] "LPAL13_310032500" "LPAL13_140015200"
upshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(up_shared), method = "keep")
## Before removal, there were 8551 genes, now there are 42.
## There are 59 samples which kept less than 90 percent counts.
## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.3732    0.4452    0.1227    0.4003    0.1662    0.4253    0.5511    0.3428 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.4034    0.1735    0.4406    0.1521    0.4268    0.3287    0.1662    0.1494 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.3753    0.1677    0.1777    0.3503    0.1409    0.4173    0.1316    0.3789 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.1415    0.1522    0.1971    0.1728    0.1518    0.1365    0.1279    0.1494 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20042 TMRC20058 TMRC20072 
##    0.4929    0.1837    0.5541    0.5077    0.1928    0.1522    0.6068    0.1794 
## TMRC20059 TMRC20048 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 
##    0.2983    0.3285    0.1431    0.1394    0.1635    0.1558    0.1790    0.4487 
## TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 TMRC20080 TMRC20043 
##    0.4130    0.3533    0.6096    0.1523    0.1707    0.6237    0.4556    0.4232 
## TMRC20054 TMRC20046 TMRC20044 
##    0.5415    0.1726    0.1745

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

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

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

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

down_shared <- shared_zymo[["downs"]][[1]][["data"]][["all"]]
downshared_expt <- exclude_genes_expt(zy_norm, ids = rownames(down_shared), method = "keep")
## Before removal, there were 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 
##    0.2203    0.1876    0.6711    0.2263    0.6573    0.2104    0.2013    0.2387 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.2022    0.6894    0.1876    0.6365    0.1681    0.2075    0.5677    0.5508 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1654    0.6549    0.6494    0.1595    0.6528    0.1856    0.6853    0.1536 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20026 TMRC20031 TMRC20076 
##    0.6763    0.7153    0.6761    0.6973    0.7206    0.6958    0.6111    0.6079 
## TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20042 TMRC20058 TMRC20072 
##    0.1910    0.7106    0.1867    0.1696    0.5314    0.5479    0.2195    0.5354 
## TMRC20059 TMRC20048 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 
##    0.1378    0.1541    0.7602    0.5707    0.6624    0.6333    0.5717    0.1783 
## TMRC20064 TMRC20075 TMRC20051 TMRC20050 TMRC20049 TMRC20062 TMRC20080 TMRC20043 
##    0.1945    0.1802    0.1902    0.6151    0.6973    0.1885    0.1545    0.1727 
## TMRC20054 TMRC20046 TMRC20044 
##    0.2025    0.6338    0.6221
high_22_heatmap <- plot_sample_heatmap(downshared_expt, row_label = rownames(down_shared))
high_22_heatmap

7 SNP profiles

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

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

old_expt <- sm(create_expt("sample_sheets/tmrc2_samples_20191203.xlsx",
                           file_column = "tophat2file"))

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

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

7.1 Create the SNP expressionset

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

## The next line drops the samples which are missing the SNP pipeline.
lp_snp <- subset_expt(lp_expt, subset="!is.na(pData(lp_expt)[['bcftable']])")
## subset_expt(): There were 71, now there are 66 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))

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.

7.2 Plot of SNP profiles for zymodemes

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

old_new_variant_heatmap <- plot_disheat(both_norm)
pp(file = "images/raw_snp_disheat.png", image = old_new_variant_heatmap,
   height = 12, width = 12)

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 26 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)

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

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

7.3 SNPS associated with clinical response in the TMRC samples

clinical_sets <- get_snp_sets(new_snps, factor = "clinicalresponse")
## The factor cure has 26 rows.
## The factor failure has 21 rows.
## The factor laboratory line has only 1 row.
## The factor laboratory line miltefosine resistant has only 1 row.
## The factor nd has 13 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)

7.3.1 Cross reference these variants by gene

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

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

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

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

head(fail_ref_snps)
##                                       seqnames  start    end width strand
## chr_LpaL13-02_pos_83184_ref_A_alt_G  LpaL13-02  83184  83185     2      +
## chr_LpaL13-05_pos_368787_ref_C_alt_T LpaL13-05 368787 368788     2      +
## chr_LpaL13-06_pos_2975_ref_T_alt_C   LpaL13-06   2975   2976     2      +
## chr_LpaL13-06_pos_66067_ref_T_alt_C  LpaL13-06  66067  66068     2      +
## chr_LpaL13-07_pos_461925_ref_A_alt_G LpaL13-07 461925 461926     2      +
## chr_LpaL13-10_pos_327353_ref_T_alt_C LpaL13-10 327353 327354     2      +
head(cure_snps)
##                                           seqnames  start    end width strand
## chr_LpaL13-03_pos_5444_ref_T_alt_C       LpaL13-03   5444   5445     2      +
## chr_LpaL13-08_pos_184791_ref_T_alt_A     LpaL13-08 184791 184792     2      +
## chr_LpaL13-12_pos_290500_ref_A_alt_G     LpaL13-12 290500 290501     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      +
annot <- fData(lp_expt)
clinical_interest <- as.data.frame(clinical_snps[["gene_summaries"]][["cure"]])
clinical_interest <- merge(clinical_interest,
                           as.data.frame(clinical_snps[["gene_summaries"]][["failure, reference strain"]]),
                           by = "row.names")
rownames(clinical_interest) <- clinical_interest[["Row.names"]]
clinical_interest[["Row.names"]] <- NULL
colnames(clinical_interest) <- c("cure_snps","fail_snps")
annot <- merge(annot, clinical_interest, by = "row.names")
rownames(annot) <- annot[["Row.names"]]
annot[["Row.names"]] <- NULL
fData(lp_expt$expressionset) <- annot

8 Zymodeme for new samples

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

8.1 Hunt for snp clusters

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

## Drop the 2.1, 2.4, unknown, and null
pruned_snps <- subset_expt(new_snps, subset="condition=='z2.2'|condition=='z2.3'")
## subset_expt(): There were 66, now there are 54 samples.
new_sets <- get_snp_sets(pruned_snps, factor = "zymodemecategorical")
## The factor z22 has 28 rows.
## The factor z23 has 26 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 
##       888 character character
summary(new_sets[["intersections"]][["01"]])
##    Length     Class      Mode 
##     76419 character character

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

8.1.1 A small function for searching for potential PCR primers

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

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

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

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

  ## Working interactively here.

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

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

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

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

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

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

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

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

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

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

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

8.1.2 Extract a promising region from the genome

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

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

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

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


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


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


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

8.2 Go hunting for Sanger sequencing regions

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

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

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

8.3 Combine this table with 2.2/2.3 genes

Here is my note from our meeting:

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

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

logfc <- zy_table[["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.
## Error: <text>:13:29: unexpected INCOMPLETE_STRING
## 12: written <- write_xlsx(data=new_table,
## 13:                       excel="excel/favorite_primers_xref_zy_sus.
##                                 ^

8.4 Make a heatmap describing the clustering of variants

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

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

##new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE)
##new_zymo_norm <- set_expt_conditions(new_zymo_norm, fact = "zymodemecategorical")
clinical_colors_v2 <- list(
    "z22" = "#0000cc",
    "z23" = "#cc0000")
new_zymo_norm <- normalize_expt(pruned_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE) %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  set_expt_colors(clinical_colors_v2)
## Removing 0 low-count genes (568413 remaining).
## transform_counts: Found 28454638 values equal to 0, adding 1 to the matrix.
zymo_heat <- plot_disheat(new_zymo_norm)
pp(file = "images/onlyz22_z23_snp_heatmap.png", image=zymo_heat[["plot"]])

zymo_heat[["plot"]]

8.4.1 Annotated heatmap of variants

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

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

##hmcols <- colorRampPalette(c("yellow","black","darkblue"))(256)
correlations <- hpgl_cor(exprs(both_norm))

zymo_missing_idx <- is.na(des[["zymodemecategorical"]])
des[["zymodemecategorical"]] <- as.character(des[["cymodemecategorical"]])
## Error in `[[<-.data.frame`(`*tmp*`, "zymodemecategorical", value = character(0)): replacement has 0 rows, data has 99
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)
## annotated Heatmap
## 
## Rows: 'dendrogram' with 2 branches and 99 members total, at height 6.095 
##   11  annotation variable(s)
## Cols: 'dendrogram' with 2 branches and 99 members total, at height 6.095 
##   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"]])
## Error in eval(quote(list(...)), env): object 'pheno_snps' not found
pheno_snps$conditions
## Error in eval(expr, envir, enclos): object 'pheno_snps' not found
idx_tbl <- exprs(pheno_snps) > 5
## Error in h(simpleError(msg, call)): error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'pheno_snps' not found
new_tbl <- data.frame(row.names = rownames(exprs(pheno_snps)))
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': error in evaluating the argument 'object' in selecting a method for function 'exprs': object 'pheno_snps' not found
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
}
## Error in eval(expr, envir, enclos): object 'xref_prop' not found
keepers <- grepl(x = rownames(new_tbl), pattern = "LpaL13")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'grepl': error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'new_tbl' not found
new_tbl <- new_tbl[keepers, ]
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
new_tbl[["strong22"]] <- 1.001 - new_tbl[["z2.2"]]
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
new_tbl[["strong23"]] <- 1.001 - new_tbl[["z2.3"]]
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
s22_na <- new_tbl[["strong22"]] > 1
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
new_tbl[s22_na, "strong22"] <- 1
## Error in new_tbl[s22_na, "strong22"] <- 1: object 'new_tbl' not found
s23_na <- new_tbl[["strong23"]] > 1
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
new_tbl[s23_na, "strong23"] <- 1
## Error in new_tbl[s23_na, "strong23"] <- 1: object 'new_tbl' not found
new_tbl[["SNP"]] <- rownames(new_tbl)
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'rownames': object 'new_tbl' not found
new_tbl[["Chromosome"]] <- gsub(x = new_tbl[["SNP"]], pattern = "chr_(.*)_pos_.*", replacement = "\\1")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'gsub': object 'new_tbl' not found
new_tbl[["Position"]] <- gsub(x = new_tbl[["SNP"]], pattern = ".*_pos_(\\d+)_.*", replacement = "\\1")
## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'gsub': object 'new_tbl' not found
new_tbl <- new_tbl[, c("SNP", "Chromosome", "Position", "strong22", "strong23")]
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
library(CMplot)
## Much appreciate for using CMplot.
## Full description, Bug report, Suggestion and the latest codes:
## https://github.com/YinLiLin/CMplot
simplify <- new_tbl
## Error in eval(expr, envir, enclos): object 'new_tbl' not found
simplify[["strong22"]] <- NULL
## Error in simplify[["strong22"]] <- NULL: object 'simplify' not found
CMplot(simplify, bin.size = 100000)
## Error in is.data.frame(x): object 'simplify' not found
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)
## Error in is.data.frame(x): object 'new_tbl' not found

SNP Density Circular Manhattan Rectangular Manhattan QQ

8.5 Try out MatrixEQTL

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

  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 28ece304dcf5e4b5adbfb86dbece61884ad2d3b9
## This is hpgltools commit: Mon Jan 10 14:19:38 2022 -0500: 28ece304dcf5e4b5adbfb86dbece61884ad2d3b9
## Saving to tmrc2_02sample_estimation_v202201.rda.xz
tmp <- loadme(filename = savefile)
---
title: "TMRC2 Comprehensive Data Analysis: 202201"
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
 html_document:
  code_download: true
  code_folding: show
  fig_caption: true
  fig_height: 7
  fig_width: 7
  highlight: default
  keep_md: false
  mode: selfcontained
  number_sections: true
  self_contained: true
  theme: readable
  toc: true
  toc_float:
   collapsed: false
   smooth_scroll: false
---

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

```{r options, include = FALSE}
library(hpgltools)
tt <- sm(devtools::load_all("~/hpgltools"))
knitr::opts_knit$set(progress = TRUE,
                     verbose = TRUE,
                     width = 90,
                     echo = TRUE)
knitr::opts_chunk$set(error = TRUE,
                      fig.width = 8,
                      fig.height = 8,
                      dpi = 96)
old_options <- options(digits = 4,
                       stringsAsFactors = FALSE,
                       knitr.duplicate.label = "allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size = 12))
ver <- "202201"
rundate <- format(Sys.Date(), format = "%Y%m%d")

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

library(Heatplus)
```

```{r current_samplesheet}
sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_20220106.xlsx")
```

# Introduction

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

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

In all cases the processing performed was:

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

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

# Annotations

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

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

```{r annot}
tt <- sm(library(EuPathDB))
tt <- sm(library(org.Lpanamensis.MHOMCOL81L13.v46.eg.db))
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")
```

# Load a genome

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

# TODO:

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

# Generate Expressionsets and Sample Estimation

The process of sample estimation takes two primary inputs:

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

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

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

## Notes

The following samples are much lower coverage:

* TMRC20002
* TMRC20006
* TMRC20007
* TMRC20008

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

## TODO:

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

```{r new_samples_hisat}
sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "zymodemecategorical")
lp_expt <- sm(create_expt(sample_sheet,
                          gene_info = hisat_annot,
                          id_column = "hpglidentifier",
                          file_column = "lpanamensisv36hisatfile")) %>%
  set_expt_conditions(fact = "zymodemecategorical") %>%
  subset_expt(nonzero = 8550) %>%
  subset_expt(coverage = 5000000) %>%
  semantic_expt_filter(semantic = c("amastin", "gp63", "leishmanolysin"),
                       semantic_column = "annot_gene_product") %>%
  sanitize_expt_metadata(columns = sanitize_columns) %>%
  set_expt_factors(columns = sanitize_columns, class = "factor")

libsizes <- plot_libsize(lp_expt)
pp(file = "images/lp_expt_libsizes.png", image = libsizes$plot, width = 14, height = 9)
## I think samples 7,10 should be removed at minimum, probably also 9,11
nonzero <- plot_nonzero(lp_expt)
pp(file = "images/lp_nonzero.png", image = nonzero$plot, width = 9, height = 9)

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

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

## Distribution Visualization

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

## By Susceptilibity

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

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

```{r susceptibility}
starting <- as.numeric(pData(lp_expt)[["susceptibilityinfectionreduction32ugmlsbvhistoricaldata"]])
sus_categorical <- starting
na_idx <- is.na(starting)
sus_categorical[na_idx] <- "unknown"

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

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

```{r pre_questions}
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)

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

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

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

plot_sm(clinical_norm)$plot
```

## By Cure/Fail status

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

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

cf_nb <- normalize_expt(cf_expt, convert = "cpm", transform = "log2",
                        norm = "quant", filter = TRUE, batch = "svaseq")
cf_nb_pca <- plot_pca(cf_nb, plot_title = "PCA of parasite expression values",
                      plot_labels = FALSE)
pp(file = "images/cf_sus_share_nb.png", image = cf_nb_pca$plot)

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

test <- pca_information(cf_norm,
                        expt_factors = c("clinicalcategorical", "zymodemecategorical",
                                         "pathogenstrain", "passagenumber"),
                        num_components = 6, plot_pcas = TRUE)
test$anova_p
test$cor_heatmap
```

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

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

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

# Zymodeme analyses

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

## Differential expression

### With respect to zymodeme attribution

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

```{r zymo_de, fig.show = "hide"}
zy_expt <- subset_expt(lp_expt, subset = "condition=='z2.2'|condition=='z2.3'")
zy_norm <- normalize_expt(zy_expt, filter = TRUE, convert = "cpm", norm = "quant")
zy_de_nobatch <- sm(all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq"))
zy_de <- sm(all_pairwise(zy_expt, filter = TRUE, model_batch = "svaseq"))
zy_table <- sm(combine_de_tables(zy_de, excel = glue::glue("excel/zy_tables-v{ver}.xlsx")))
zy_sig <- sm(extract_significant_genes(zy_table, excel = glue::glue("excel/zy_sig-v{ver}.xlsx")))
```

### Images of zymodeme DE

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

## With respect to cure/failure

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

```{r curefail_de, fig.show = "hide"}
cf_de <- 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")))
cf_sig <- sm(extract_significant_genes(cf_table, excel = glue::glue("excel/cf_sig-v{ver}.xlsx")))
```

## With respect to susceptibility

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

```{r curefail_de02, fig.show = "hide"}
sus_de <- 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")))
sus_sig <- sm(extract_significant_genes(sus_table, excel = glue::glue("excel/sus_sig-v{ver}.xlsx")))
```

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

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

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

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

## Ontology searches

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

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

### A couple plots from the differential expression

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

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

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

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

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

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

```{r goseq_up}
zy_go_up$pvalue_plots$bpp_plot_over
```

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

```{r goseq_down}
zy_go_down$pvalue_plots$bpp_plot_over
```

## Look for agreement between sensitivity and zymodemes

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

```{r sensitive_vs_zymo}
zy_df <- zy_table[["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)
pp(file = "images/compare_sus_zy.png", image = compare$scatter)
compare$cor
```

## Zymodeme enzyme gene IDs

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

They are:

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

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

### Expression levels of zymodeme genes

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

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

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

## Empirically observed Zymodeme genes from differential expression analysis

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

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

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

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

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

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

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

# SNP profiles

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

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

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

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

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

## Create the SNP expressionset

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

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

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.

## Plot of SNP profiles for zymodemes

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

```{r plotting_variants}
old_new_variant_heatmap <- plot_disheat(both_norm)
pp(file = "images/raw_snp_disheat.png", image = old_new_variant_heatmap,
   height = 12, width = 12)
```

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

```{r get_snp_sets1}
snp_sets <- get_snp_sets(both_snps, factor = "condition")
both_expt <- combine_expts(lp_expt, old_expt)

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

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

## SNPS associated with clinical response in the TMRC samples

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

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

## clinical_written <- write_variants(new_snps)
```

### Cross reference these variants by gene

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

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

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


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

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

head(fail_ref_snps)
head(cure_snps)

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

# Zymodeme for new samples

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

## Hunt for snp clusters

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

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

summary(new_sets[["intersections"]][["10"]])
summary(new_sets[["intersections"]][["01"]])
```

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

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

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

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

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

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

  ## Working interactively here.

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

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

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

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

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

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

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

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

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

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

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

### Extract a promising region from the genome

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

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

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

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

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


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


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


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

## Go hunting for Sanger sequencing regions

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

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

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

## Combine this table with 2.2/2.3 genes

Here is my note from our meeting:

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

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

```{r xref_primers_deg}
logfc <- zy_table[["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.
```


## Make a heatmap describing the clustering of variants

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

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

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

zymo_heat <- plot_disheat(new_zymo_norm)
pp(file = "images/onlyz22_z23_snp_heatmap.png", image=zymo_heat[["plot"]])
zymo_heat[["plot"]]
```

### Annotated heatmap of variants

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

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

##hmcols <- colorRampPalette(c("yellow","black","darkblue"))(256)
correlations <- hpgl_cor(exprs(both_norm))

zymo_missing_idx <- is.na(des[["zymodemecategorical"]])
des[["zymodemecategorical"]] <- as.character(des[["cymodemecategorical"]])
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)
pp(file = "images/dendro_heatmap.png", image = map1, height = 20, width = 20)
```

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

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


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

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


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


CMplot(simplify, bin.size = 100000)

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

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

## Try out MatrixEQTL

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

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

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

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

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

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

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

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

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

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

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



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

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