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:

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

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

2 Annotations

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

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

tt <- sm(library(EuPathDB))
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 MHOM/GH/2012/GH5, 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:

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

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

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

5.1 Notes

The following samples are much lower coverage:

TMRC20002
TMRC20006
TMRC20007
TMRC20008

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

5.2 TODO:

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

sanitize_columns <- c("passagenumber", "clinicalresponse", "clinicalcategorical",
                      "zymodemecategorical", "phenotypiccharacteristics")
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"]])
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",
    "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)

zymo_3dpca <- plot_3d_pca(zymo_pca)
zymo_3dpca$plot

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

## Warning: ggrepel: 13 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

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

## Removing 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 324 low elements to zero.

## transform_counts: Found 324 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: 52 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 0.331134 0.47800 1.728e-03 0.69347 0.42085
## zymodemecategorical 6.720e-06 0.005922 0.70766 3.336e-02 0.01269 0.09026
## pathogenstrain      7.092e-01 0.776303 0.84356 4.512e-06 0.01811 0.56192
## passagenumber       8.896e-01 0.237729 0.04096 2.795e-02 0.22294 0.41299

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)

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

## Removing 142 low-count genes (8568 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 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 229 low elements to zero.

## transform_counts: Found 229 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 55 samples.

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

## Removing 160 low-count genes (8550 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	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	ebseq_fc	ebseq_logfc	ebseq_c1mean	ebseq_c2mean	ebseq_mean	ebseq_var	ebseq_postfc	ebseq_ppee	ebseq_ppde	ebseq_adjp	edger_logcpm	edger_lr	limma_ave	limma_t	limma_b	limma_p	limma_adjp_ihw	deseq_adjp_ihw	edger_adjp_ihw	ebseq_adjp_ihw	basic_adjp_ihw	lfc_meta	lfc_var	lfc_varbymed	p_meta	p_var
LPAL13_000044900	LPAL13_000044900	actin-related protein 2, putative	protein coding	LPAL13_SCAF000645	507	1685	-	reverse	Not Assigned	1179.0	1178	29.300	0e+00	13.430	0e+00	9.0070	0.2369	3.9690	-4.1880	15.756	0.1172	8.157	10.200	0.0000	0.0000	841.70	1.1550	25.370	115812.91	16.821	0.0000	1158.119	782.513	4.955e+05	356.464	1.0000	0.0000	1.0000	4.9800	60.02	1.3620	1.619	-4.2760	0.1099	2.370e-01	4.861e-138	1.647e-11	0.000e+00	5.941e-08	14.280	1.013e+01	7.092e-01	3.663e-02	4.026e-03
LPAL13_000035800	LPAL13_000035800	hypothetical protein	protein coding	LPAL13_SCAF000500	737	1006	-	reverse	Not Assigned	270.0	269	14.910	0e+00	14.080	0e+00	10.4500	0.2580	5.3190	-3.9740	15.252	0.3680	9.293	11.610	0.0000	0.0000	2880.00	1.1430	13.040	31670.08	14.951	0.1254	4287.362	2896.907	1.341e+07	1311.656	0.0000	0.0000	0.0000	6.7500	86.47	2.2850	1.554	-4.3360	0.1248	2.577e-01	2.908e-35	1.154e-16	0.000e+00	7.479e-09	16.200	9.099e+00	5.616e-01	4.160e-02	5.192e-03
LPAL13_320026300	LPAL13_320026300	hypothetical protein, conserved	protein coding	LpaL13_32	754268	755485	-	reverse	32	1218.0	1217	14.210	0e+00	13.330	0e+00	9.8160	0.2961	4.7860	-4.0330	18.222	0.6163	8.819	9.984	0.0000	0.0000	1521.00	1.1160	12.740	17750.55	14.116	0.1173	2258.771	1526.235	2.020e+06	672.288	0.0000	0.0000	0.0000	5.8310	77.84	2.0190	1.445	-4.4300	0.1531	2.965e-01	1.034e-33	5.258e-15	0.000e+00	4.003e-08	12.360	9.588e-02	7.757e-03	5.103e-02	7.813e-03
LPAL13_000053200	LPAL13_000053200	hypothetical protein	protein coding	LPAL13_SCAF000804	5037	5249	-	reverse	Not Assigned	213.0	212	8.774	0e+00	10.180	0e+00	5.8300	0.0288	1.0040	-4.1880	9.040	0.1172	5.193	8.521	0.0000	0.0000	75.03	1.0880	8.064	12533.34	13.614	0.0000	125.323	84.678	7.766e+03	39.301	1.0000	0.0000	1.0000	1.5420	52.91	-0.9226	2.861	-2.8140	0.0056	3.481e-02	4.408e-13	2.647e-10	0.000e+00	7.765e-07	8.168	4.425e-02	5.417e-03	1.866e-03	1.045e-05
LPAL13_000051300	LPAL13_000051300	hypothetical protein, conserved	protein coding	LPAL13_SCAF000772	11	2344	+	forward	Not Assigned	2334.0	2333	8.477	0e+00	9.468	0e+00	3.7190	0.2694	0.2373	-4.0070	10.238	0.5216	4.244	6.306	0.0000	0.0000	135.60	1.2650	6.701	1753.26	10.776	0.0861	168.514	113.889	6.534e+04	56.718	0.0000	0.0000	0.0000	2.4020	32.16	-1.1240	1.520	-4.3650	0.1330	2.690e-01	4.676e-09	1.618e-06	0.000e+00	2.766e-05	6.999	2.573e+00	3.676e-01	4.433e-02	5.896e-03
LPAL13_000040700	LPAL13_000040700	hypothetical protein, conserved	protein coding	LPAL13_SCAF000598	54	1067	+	forward	Not Assigned	1014.0	1013	7.671	0e+00	6.800	0e+00	2.8180	0.1002	-1.2510	-3.9900	6.479	0.5345	2.740	4.971	0.0000	0.0004	20.16	1.1500	6.669	186.50	7.543	0.1278	25.699	17.405	3.919e+02	8.201	0.0000	0.0000	0.0000	-0.1417	25.17	-2.3660	2.188	-3.7130	0.0321	1.004e-01	5.117e-09	2.694e-05	0.000e+00	3.968e-04	5.586	1.673e+00	2.996e-01	1.069e-02	3.430e-04
LPAL13_000017600	LPAL13_000017600	hypothetical protein, conserved	protein coding	LPAL13_SCAF000146	359	586	+	forward	Not Assigned	228.0	227	6.599	0e+00	6.582	0e+00	5.9140	0.0594	4.4470	-1.1540	4.361	2.6176	5.601	8.940	0.0000	0.0000	615.90	0.6798	9.707	80.79	6.336	11.8247	956.082	649.836	3.876e+05	64.957	0.0000	1.0000	0.0000	4.5290	58.28	2.3410	2.483	-3.0470	0.0155	5.934e-02	5.443e-19	3.255e-11	9.676e-01	2.057e-07	6.658	2.129e+00	3.198e-01	5.160e-03	7.988e-05
LPAL13_300029400	LPAL13_300029400	hypothetical protein, conserved	protein coding	LpaL13_30	853953	854150	-	reverse	30	198.0	197	6.334	0e+00	6.254	0e+00	4.9430	0.0024	1.7270	-2.5570	1.492	1.9180	4.284	9.143	0.0000	0.0000	89.08	0.7409	8.549	64.90	6.020	1.8983	123.831	84.285	8.962e+03	24.716	0.0000	1.0000	0.0000	1.7340	54.93	0.0165	3.995	-0.5115	0.0002	2.361e-03	1.330e-14	1.268e-10	9.650e-01	1.550e-06	5.915	5.901e-01	9.977e-02	5.337e-05	8.544e-09
LPAL13_080010600	LPAL13_080010600	hypothetical protein, conserved	protein coding	LpaL13_08	195555	195749	-	reverse	8	195.0	194	5.937	0e+00	7.435	0e+00	2.3330	0.0513	-1.8840	-4.1880	4.892	0.1172	2.304	5.084	0.0000	0.0004	10.97	1.1490	5.165	1825.43	10.834	0.0000	18.244	12.327	4.601e+02	6.571	1.0000	0.0000	1.0000	-0.9372	26.93	-3.1350	2.562	-3.3070	0.0126	6.156e-02	9.050e-06	1.393e-05	0.000e+00	4.302e-04	4.938	4.273e+00	8.653e-01	4.200e-03	5.292e-05
LPAL13_000011700	LPAL13_000011700	hypothetical protein	protein coding	LPAL13_SCAF000076	101	364	-	reverse	Not Assigned	264.0	263	5.837	0e+00	7.381	0e+00	2.6660	0.0452	-1.3950	-4.1880	6.725	0.1172	2.793	5.291	0.0000	0.0003	13.83	1.1890	4.910	2415.59	11.238	0.0000	24.146	16.315	4.529e+02	8.171	1.0000	0.0000	1.0000	-0.6026	24.80	-2.9340	2.632	-3.1710	0.0105	4.521e-02	2.898e-05	3.232e-05	0.000e+00	3.013e-04	5.108	1.611e+00	3.154e-01	3.494e-03	3.660e-05
LPAL13_040019400	LPAL13_040019400	hypothetical protein	protein coding	LpaL13_04	440768	441127	-	reverse	4	360.0	359	5.467	0e+00	5.338	0e+00	3.4590	0.0204	-0.4395	-3.3620	1.762	1.1875	2.922	7.098	0.0000	0.0000	33.36	0.8746	6.251	43.94	5.457	0.7564	33.666	22.992	1.721e+03	9.142	0.0000	0.0000	0.0000	0.3743	31.59	-1.6880	3.026	-2.5930	0.0035	2.038e-02	5.815e-08	1.928e-06	0.000e+00	8.066e-06	4.777	1.077e-01	2.254e-02	1.161e-03	4.046e-06
LPAL13_170014500	LPAL13_170014500	hypothetical protein, conserved	protein coding	LpaL13_17	361708	362040	+	forward	17	333.0	332	5.167	0e+00	4.916	3e-04	2.7620	0.0290	-0.6550	-3.2950	6.983	1.4800	2.640	4.161	0.0002	0.0020	21.04	0.9833	5.255	46.88	5.551	0.9255	43.850	29.929	1.585e+03	11.208	0.0000	0.0000	0.0000	-0.2718	18.70	-2.4250	2.857	-2.7770	0.0057	2.911e-02	6.836e-06	3.877e-04	0.000e+00	1.994e-03	4.197	2.010e-01	4.790e-02	1.892e-03	1.065e-05
LPAL13_350011800	LPAL13_350011800	hypothetical protein, conserved	protein coding	LpaL13_35	171009	171242	+	forward	35	234.0	233	5.163	0e+00	5.148	0e+00	4.6090	0.0026	2.8750	-1.1550	2.429	1.1467	4.030	9.181	0.0000	0.0000	174.70	0.5695	9.066	34.83	5.122	8.5427	297.854	204.023	5.757e+04	26.123	0.0000	1.0000	0.0000	2.7050	58.33	1.1580	3.938	-0.0230	0.0002	2.642e-03	2.111e-16	3.255e-11	9.650e-01	6.496e-08	4.958	6.883e-01	1.388e-01	6.487e-05	1.262e-08
LPAL13_200050100	LPAL13_200050100	hypothetical protein	protein coding	LpaL13_20.1	1627529	1627717	+	forward	20.1	189.0	188	5.114	0e+00	5.083	0e+00	4.6430	0.0017	2.4570	-1.8560	1.010	2.3968	4.313	8.802	0.0000	0.0000	116.30	0.5978	8.555	25.75	4.686	7.4499	192.056	132.184	2.099e+04	18.447	0.0000	1.0000	0.0000	2.1490	51.00	0.7904	4.129	0.0911	0.0001	2.041e-03	1.305e-14	5.942e-10	9.676e-01	9.799e-06	5.002	1.092e+00	2.183e-01	3.370e-05	3.407e-09
LPAL13_080010800	LPAL13_080010800	hypothetical protein	protein coding	LpaL13_08	199409	199792	-	reverse	8	384.0	383	5.065	1e-04	6.542	0e+00	1.6980	0.2085	-2.3590	-4.1880	4.126	0.1172	1.829	4.375	0.0002	0.0018	10.25	1.0820	4.681	1036.53	10.018	0.0000	10.355	6.997	1.037e+02	3.870	1.0000	0.0000	1.0000	-0.8838	24.72	-3.1090	1.714	-4.2060	0.0910	2.089e-01	7.164e-05	3.308e-05	0.000e+00	1.773e-03	3.916	3.049e+00	7.785e-01	3.034e-02	2.762e-03
LPAL13_000011800	LPAL13_000011800	hypothetical protein, conserved	protein coding	LPAL13_SCAF000076	446	640	-	reverse	Not Assigned	195.0	194	4.744	2e-04	5.365	3e-04	0.9541	0.4747	-2.5010	-3.9920	3.638	0.5576	1.491	3.404	0.0017	0.0102	11.03	1.0720	4.424	61.09	5.933	0.1379	9.026	6.143	7.072e+01	3.201	0.9990	0.0010	0.9990	-0.8584	18.80	-3.0600	1.029	-4.6930	0.3073	4.744e-01	1.774e-04	3.603e-04	1.773e-02	1.023e-02	3.320	3.640e+00	1.096e+00	1.024e-01	3.148e-02
LPAL13_000014000	LPAL13_000014000	hypothetical protein	protein coding	LPAL13_SCAF000119	655	942	+	forward	Not Assigned	288.0	287	4.278	0e+00	4.268	0e+00	3.9120	0.0114	2.4330	-1.1070	1.521	1.9542	3.540	7.486	0.0000	0.0000	128.20	0.5257	8.137	17.50	4.129	10.8620	190.207	132.041	1.264e+04	13.784	0.0000	1.0000	0.0000	2.2850	49.29	1.0980	3.311	-1.4520	0.0015	1.136e-02	3.161e-13	1.293e-09	9.609e-01	1.616e-05	4.339	9.720e-01	2.240e-01	4.953e-04	7.361e-07
LPAL13_000035500	LPAL13_000035500	hypothetical protein, conserved	protein coding	LPAL13_SCAF000492	7045	7410	+	forward	Not Assigned	366.0	365	4.244	0e+00	4.247	0e+00	3.8130	0.0290	4.5240	0.8016	2.358	0.5645	3.722	9.900	0.0000	0.0000	509.00	0.5882	7.214	19.25	4.267	45.7435	880.950	610.072	3.355e+05	18.528	0.0000	1.0000	0.0000	4.2590	38.49	2.8070	2.857	-2.2370	0.0057	3.509e-02	2.215e-10	1.077e-07	9.650e-01	8.739e-09	4.278	9.389e-01	2.195e-01	1.886e-03	1.067e-05
LPAL13_000026500	LPAL13_000026500	hypothetical protein	protein coding	LPAL13_SCAF000301	144	494	-	reverse	Not Assigned	351.0	350	4.099	0e+00	4.058	1e-04	2.4050	0.1735	0.1342	-2.3950	5.419	1.9803	2.529	4.092	0.0003	0.0025	43.82	0.7856	5.218	20.01	4.323	2.7286	54.802	37.913	1.498e+03	9.648	0.0002	0.9998	0.0002	0.8896	22.05	-0.8872	1.840	-4.0380	0.0701	1.737e-01	7.827e-06	8.662e-05	9.676e-01	2.483e-03	3.298	2.748e-01	8.333e-02	2.337e-02	1.638e-03
LPAL13_220019500	LPAL13_220019500	hypothetical protein	protein coding	LpaL13_22	578260	578538	+	forward	22	279.0	278	3.766	0e+00	3.767	0e+00	3.0520	0.0282	3.5420	0.3451	2.128	0.8014	3.196	8.201	0.0000	0.0000	284.60	0.4888	7.703	14.41	3.849	29.3640	423.356	295.575	6.945e+04	13.320	0.0000	1.0000	0.0000	3.4280	45.31	2.3620	2.872	-2.2080	0.0054	2.827e-02	6.678e-12	6.457e-09	9.650e-01	2.914e-07	3.738	3.673e-01	9.825e-02	1.809e-03	9.817e-06

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

	gid	annotgeneproduct	annotgenetype	chromosome	start	end	strand	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_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	-5.470	0.0003	-5.372	0.0007	-5.948	0.0000	-3.7270	3.4570	10.5693	0.0607	-7.184	-10.980	0e+00	128.700	1.2590	-4.344	0.0000	0.1257	-2.992	303.97	38.190	124.390	2.181e+04	0.1338	0.0000	0.0000	0.0000	2.2660	16.67	0.0000	-1.0450	-5.584	4.7710	0e+00	3.750e-05	2.310e-04	8.774e-04	0.000e+00	2.320e-08	-5.597	0.000e+00	0.000e+00	1.964e-05	5.091e-10
LPAL13_000038400	LPAL13_000038400	expression-site associated gene (esag3), putative	protein coding	LPAL13_SCAF000573	101	1360	+		forward	Not Assigned	1260.0	1259	-2.890	0.0000	-2.883	0.0000	-3.228	0.0001	4.6320	8.2100	3.1208	0.0361	-3.578	-10.010	0e+00	3613.000	0.5417	-5.335	0.0000	0.1769	-2.499	8563.68	1514.852	3800.959	1.502e+07	0.1810	0.0000	0.0000	0.0000	7.0710	32.07	0.0000	5.8030	-5.353	5.2830	0e+00	7.083e-05	4.901e-06	1.316e-06	0.000e+00	6.552e-08	-2.980	2.311e-02	-7.757e-03	3.978e-07	3.538e-13
LPAL13_350063000	LPAL13_350063000	hypothetical protein	protein coding	LpaL13_35	1964328	1964543	-		reverse	35	216.0	215	-2.787	0.0000	-2.760	0.0000	-3.430	0.0000	-2.3360	1.1760	2.1326	0.2175	-3.511	-10.920	0e+00	20.820	0.4834	-5.767	0.0000	0.1430	-2.806	52.76	7.535	22.203	6.022e+02	0.1650	0.0000	1.0000	0.0000	-0.3551	32.28	0.0000	-1.4490	-6.980	8.1370	0e+00	9.781e-07	6.585e-07	1.463e-06	9.609e-01	5.752e-09	-3.009	2.562e-03	-8.513e-04	7.635e-09	3.534e-17
LPAL13_140019300	LPAL13_140019300	bt1 family, putative	protein coding	LpaL13_14	530784	531350	+		forward	14	567.0	566	-2.647	0.0000	-2.642	0.0000	-2.524	0.0000	4.6450	7.1000	0.4797	1.0258	-2.455	-7.588	0e+00	1850.000	0.3812	-6.943	0.0000	0.1690	-2.565	4590.02	775.713	2012.785	5.149e+06	0.1732	0.0000	1.0000	0.0000	6.1060	54.17	0.0000	5.3900	-6.983	11.6400	0e+00	9.781e-07	1.215e-09	1.268e-10	9.956e-01	3.706e-05	-2.637	1.599e-01	-6.065e-02	4.897e-10	7.135e-19
LPAL13_000012000	LPAL13_000012000	hypothetical protein	protein coding	LPAL13_SCAF000080	710	1159	-		reverse	Not Assigned	450.0	449	-2.597	0.0006	-2.588	0.0003	-3.018	0.0033	0.0920	3.9390	7.6668	0.1677	-3.847	-6.794	0e+00	204.300	0.6316	-4.113	0.0000	0.2273	-2.137	439.13	99.824	209.870	4.713e+04	0.2373	0.1104	0.8896	0.1104	2.9350	18.91	0.0000	1.3490	-3.853	0.3639	3e-04	3.273e-03	5.777e-04	3.565e-04	8.804e-01	1.427e-05	-2.699	3.322e-02	-1.231e-02	1.041e-04	1.825e-08
LPAL13_310039200	LPAL13_310039200	hypothetical protein	protein coding	LpaL13_31	1301745	1301972	-		reverse	31	228.0	227	-2.424	0.0000	-2.419	0.0000	-2.442	0.0000	1.2180	3.7480	1.3520	0.2102	-2.530	-9.456	0e+00	189.000	0.4028	-6.018	0.0000	0.2518	-1.990	382.33	96.248	189.031	3.283e+04	0.2618	0.3602	0.6398	0.3602	2.8250	39.27	0.0000	1.9810	-5.653	6.3710	0e+00	3.272e-05	1.888e-07	8.181e-08	6.661e-01	1.829e-08	-2.505	9.974e-02	-3.981e-02	1.114e-07	3.655e-14
LPAL13_310035500	LPAL13_310035500	hypothetical protein	protein coding	LpaL13_31	1198439	1198957	-		reverse	31	519.0	518	-2.381	0.0059	-2.284	0.0045	-3.219	0.0000	-4.1820	-0.4332	4.0735	0.4619	-3.749	-8.353	0e+00	6.986	0.7064	-3.370	0.0008	0.3087	-1.696	17.33	5.343	9.229	3.055e+02	0.3627	0.0000	0.0000	0.0000	-1.9240	12.08	0.0005	-3.1420	-7.026	5.9880	0e+00	9.518e-07	5.431e-03	6.168e-03	0.000e+00	2.057e-07	-2.637	1.925e-03	-7.302e-04	4.202e-04	1.469e-07
LPAL13_340039600	LPAL13_340039600	hypothetical protein	protein coding	LpaL13_34	1247554	1247757	-		reverse	34	204.0	203	-2.226	0.0002	-2.222	0.0001	-2.731	0.0005	1.2470	4.2710	3.6166	0.1032	-3.024	-7.724	0e+00	225.900	0.5086	-4.377	0.0000	0.2131	-2.230	541.90	115.473	253.773	5.388e+04	0.2179	0.0000	1.0000	0.0000	3.0670	20.97	0.0000	1.9970	-4.581	2.6640	0e+00	6.390e-04	2.224e-04	1.488e-04	9.609e-01	2.350e-06	-2.435	8.498e-03	-3.489e-03	1.227e-05	5.938e-11
LPAL13_310031000	LPAL13_310031000	hypothetical protein, conserved	protein coding	LpaL13_31	1075172	1075459	-		reverse	31	288.0	287	-2.225	0.0000	-2.203	0.0000	-2.878	0.0000	-2.0110	0.9902	3.4994	0.5164	-3.001	-7.015	0e+00	26.070	0.4422	-5.032	0.0000	0.2783	-1.845	53.53	14.891	27.423	9.837e+02	0.3026	0.1041	0.8959	0.1041	0.0442	25.63	0.0000	-1.1600	-6.196	6.2140	0e+00	7.298e-06	1.793e-05	2.417e-05	8.856e-01	2.953e-06	-2.435	0.000e+00	0.000e+00	3.120e-07	5.784e-14
LPAL13_000012100	LPAL13_000012100	hypothetical protein	protein coding	LPAL13_SCAF000080	1637	1894	-		reverse	Not Assigned	258.0	257	-2.194	0.0093	-2.179	0.0075	-3.393	0.0003	-2.2170	1.1630	6.2496	0.6344	-3.380	-6.141	0e+00	30.040	0.6857	-3.200	0.0014	0.2868	-1.802	62.47	17.913	32.365	1.713e+03	0.3159	0.0526	0.9474	0.0526	0.2151	10.88	0.0010	-1.4100	-4.876	2.4920	0e+00	3.045e-04	9.270e-03	8.836e-03	9.356e-01	2.741e-05	-2.573	1.242e-01	-4.826e-02	7.845e-04	4.935e-07
LPAL13_310031300	LPAL13_310031300	hypothetical protein, conserved	protein coding	LpaL13_31	1084772	1085059	-		reverse	31	288.0	287	-2.134	0.0024	-2.122	0.0016	-3.169	0.0002	-1.0760	2.1610	3.9624	0.7670	-3.237	-6.863	0e+00	62.920	0.5806	-3.676	0.0002	0.2357	-2.085	134.87	31.779	65.216	6.123e+03	0.2525	0.0099	0.9901	0.0099	1.2360	14.58	0.0001	-0.2025	-4.969	3.2940	0e+00	2.022e-04	2.379e-03	2.121e-03	9.584e-01	3.680e-06	-2.423	2.657e-02	-1.097e-02	1.253e-04	1.350e-08
LPAL13_140019100	LPAL13_140019100	bt1 family, putative	protein coding	LpaL13_14	525164	525514	+		forward	14	351.0	350	-2.003	0.0000	-1.998	0.0000	-2.040	0.0000	3.9170	6.0240	0.3510	0.5070	-2.107	-8.882	0e+00	869.900	0.2988	-6.703	0.0000	0.2312	-2.113	1932.12	446.698	928.457	7.009e+05	0.2344	0.0000	1.0000	0.0000	5.0180	52.46	0.0000	4.5850	-7.335	13.0600	0e+00	5.447e-07	4.676e-09	2.970e-10	9.609e-01	2.897e-06	-2.057	4.177e-02	-2.031e-02	1.192e-10	3.558e-20
LPAL13_050005000	LPAL13_050005000	hypothetical protein	protein coding	LpaL13_05	3394	3612	-		reverse	5	219.0	218	-1.993	0.0056	-1.986	0.0035	-2.736	0.0002	0.1994	2.6790	2.0375	0.1572	-2.480	-8.063	0e+00	88.780	0.5888	-3.384	0.0007	0.2837	-1.817	174.87	49.604	90.231	6.026e+03	0.2922	0.0002	0.9998	0.0002	1.7250	12.71	0.0004	0.4855	-4.996	3.8090	0e+00	2.276e-04	5.662e-03	4.140e-03	9.609e-01	5.395e-07	-2.217	1.753e-02	-7.908e-03	3.606e-04	1.258e-07
LPAL13_000038500	LPAL13_000038500	hypothetical protein	protein coding	LPAL13_SCAF000575	39	251	+		forward	Not Assigned	213.0	212	-1.978	0.0067	-1.963	0.0097	-3.314	0.0000	-1.9410	1.3660	4.6543	0.6628	-3.307	-6.731	0e+00	31.090	0.5964	-3.317	0.0009	0.2989	-1.742	72.58	21.686	38.191	2.293e+03	0.3243	0.1752	0.8248	0.1752	0.2162	10.24	0.0014	-1.2950	-5.590	4.5350	0e+00	4.344e-05	6.321e-03	1.256e-02	8.291e-01	5.673e-06	-2.298	2.433e-01	-1.059e-01	7.605e-04	4.864e-07
LPAL13_340039700	LPAL13_340039700	snare domain containing protein, putative	protein coding	LpaL13_34	1248192	1248947	-		reverse	34	756.0	755	-1.798	0.0000	-1.794	0.0000	-1.930	0.0000	4.6130	6.7130	0.6868	0.0937	-2.100	-11.180	0e+00	1390.000	0.3094	-5.810	0.0000	0.2704	-1.887	2937.42	794.360	1489.406	1.369e+06	0.2739	0.0000	1.0000	0.0000	5.6930	38.61	0.0000	5.2760	-6.469	9.5800	0e+00	3.701e-06	5.447e-07	8.727e-08	9.487e-01	2.029e-09	-1.790	3.196e-02	-1.786e-02	6.333e-09	3.442e-17
LPAL13_170015400	LPAL13_170015400	hypothetical protein, conserved	protein coding	LpaL13_17	395975	396307	+		forward	17	333.0	332	-1.715	0.0000	-1.708	0.0000	-1.699	0.0001	1.2420	3.2650	0.9481	0.1241	-2.023	-9.210	0e+00	148.900	0.2814	-6.095	0.0000	0.3164	-1.660	265.43	83.965	142.820	1.263e+04	0.3213	0.0000	1.0000	0.0000	2.4750	37.57	0.0000	1.9960	-5.333	5.1870	0e+00	7.409e-05	1.166e-07	1.322e-07	9.609e-01	3.537e-08	-1.702	4.225e-02	-2.482e-02	3.903e-07	4.548e-13
LPAL13_210015500	LPAL13_210015500	core histone h2a/h2b/h3/h4, putative	protein coding	LpaL13_21	326108	326506	+		forward	21	399.0	398	-1.415	0.0015	-1.404	0.0004	-1.438	0.0045	4.9010	6.6470	2.6083	0.2832	-1.746	-4.882	4e-04	2143.000	0.3701	-3.822	0.0001	0.4722	-1.082	2956.62	1396.232	1902.304	2.503e+06	0.4907	0.9914	0.0086	0.9914	6.3210	18.04	0.0000	5.6650	-3.716	-0.3655	4e-04	4.497e-03	1.522e-03	3.184e-04	4.523e-02	4.434e-04	-1.498	2.465e-02	-1.646e-02	1.874e-04	3.965e-08
LPAL13_140019200	LPAL13_140019200	inositol-3-phosphate synthase	protein coding	LpaL13_14	527711	529291	+	INO1	forward	14	1581.0	1580	-1.414	0.0000	-1.409	0.0000	-1.490	0.0000	8.8260	10.4100	0.1715	0.4377	-1.580	-7.588	0e+00	19060.000	0.2377	-5.946	0.0000	0.3502	-1.514	36500.63	12780.792	20473.713	1.790e+08	0.3525	0.0000	1.0000	0.0000	9.4720	41.49	0.0000	9.2510	-6.301	8.9390	0e+00	5.648e-06	2.763e-07	2.036e-08	1.000e+00	4.852e-05	-1.522	1.763e-02	-1.158e-02	9.103e-09	1.784e-16
LPAL13_320038700	LPAL13_320038700	hypothetical protein, conserved	protein coding	LpaL13_32	1175024	1175257	+		forward	32	234.0	233	-1.413	0.0000	-1.406	0.0000	-1.442	0.0000	2.5490	3.9280	0.4267	0.1122	-1.379	-8.484	0e+00	255.300	0.2271	-6.222	0.0000	0.4234	-1.240	421.88	178.602	257.503	2.064e+04	0.4270	0.0009	0.9991	0.0009	3.2540	40.65	0.0000	3.0110	-5.952	7.5730	0e+00	1.437e-05	6.640e-08	4.747e-08	9.609e-01	1.169e-07	-1.490	2.028e-02	-1.361e-02	3.376e-08	3.351e-15
LPAL13_040007800	LPAL13_040007800	hypothetical protein, conserved	protein coding	LpaL13_04	77524	78306	+		forward	4	783.0	782	-1.378	0.0000	-1.373	0.0000	-1.246	0.0000	6.5520	7.7350	0.1432	0.3718	-1.183	-6.174	3e-04	4021.000	0.2295	-6.007	0.0000	0.3941	-1.343	6683.93	2634.211	3947.634	7.264e+06	0.3986	0.0001	0.9999	0.0001	7.2270	44.53	0.0000	7.0050	-5.829	7.0500	0e+00	2.049e-05	2.015e-07	7.246e-09	9.679e-01	3.080e-04	-1.364	6.839e-02	-5.014e-02	5.564e-08	8.971e-15

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 = -193, df = 8548, p-value <2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  -0.9060 -0.8981
## sample estimates:
##     cor 
## -0.9021

6.6 Zymodeme enzyme gene IDs

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

They are:

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

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

6.6.1 Expression levels of zymodeme genes

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

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

zymo_expt <- exclude_genes_expt(zy_norm, ids = my_genes, method = "keep")

## Before removal, there were 8550 genes, now there are 6.

## There are 55 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.1312    0.1249    0.1320    0.1060    0.1300    0.1102    0.1130    0.1165 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.1157    0.1181    0.1149    0.1137    0.1101    0.1062    0.1104    0.1208 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1207    0.1066    0.1092    0.1147    0.1224    0.1127    0.1102    0.1063 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20031 TMRC20055 TMRC20079 
##    0.1307    0.1126    0.1202    0.1163    0.1128    0.1005    0.1346    0.1268 
## TMRC20071 TMRC20078 TMRC20058 TMRC20072 TMRC20059 TMRC20048 TMRC20060 TMRC20077 
##    0.1234    0.1340    0.1181    0.1430    0.1104    0.1033    0.1087    0.1220 
## TMRC20074 TMRC20063 TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 
##    0.1208    0.1167    0.1182    0.1105    0.1140    0.1113    0.1283    0.1154 
## TMRC20049 TMRC20062 TMRC20080 TMRC20043 TMRC20054 TMRC20046 TMRC20044 
##    0.1393    0.1285    0.1154    0.1137    0.1278    0.1367    0.1337

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_050005000" "LPAL13_340039600"
##  [9] "LPAL13_310031000" "LPAL13_310039200" "LPAL13_350063000" "LPAL13_180013900"
## [13] "LPAL13_140019300" "LPAL13_210015500" "LPAL13_350013200" "LPAL13_340039700"
## [17] "LPAL13_350073400" "LPAL13_250006300" "LPAL13_170015400" "LPAL13_330024000"
## [21] "LPAL13_140019100" "LPAL13_320038700" "LPAL13_000052700" "LPAL13_140019200"
## [25] "LPAL13_210005000" "LPAL13_230011200" "LPAL13_330021800" "LPAL13_240009700"
## [29] "LPAL13_160014500" "LPAL13_350073200" "LPAL13_050009600" "LPAL13_250025700"
## [33] "LPAL13_230011500" "LPAL13_160014100" "LPAL13_230011400" "LPAL13_040007800"
## [37] "LPAL13_230011300" "LPAL13_020006700" "LPAL13_310032500" "LPAL13_310028500"

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

## Before removal, there were 8550 genes, now there are 40.

## There are 55 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.3322    0.4156    0.1039    0.3671    0.1512    0.3873    0.5153    0.3105 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.3625    0.1575    0.4002    0.1335    0.3897    0.2896    0.1460    0.1261 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.3401    0.1453    0.1548    0.3148    0.1255    0.3827    0.1178    0.3447 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20031 TMRC20055 TMRC20079 
##    0.1226    0.1371    0.1805    0.1545    0.1339    0.1099    0.1670    0.5184 
## TMRC20071 TMRC20078 TMRC20058 TMRC20072 TMRC20059 TMRC20048 TMRC20060 TMRC20077 
##    0.4733    0.1748    0.5681    0.1654    0.2709    0.2979    0.1251    0.1243 
## TMRC20074 TMRC20063 TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 
##    0.1484    0.1369    0.1613    0.4133    0.3801    0.3249    0.5661    0.1338 
## TMRC20049 TMRC20062 TMRC20080 TMRC20043 TMRC20054 TMRC20046 TMRC20044 
##    0.1526    0.5845    0.4227    0.3880    0.5060    0.1546    0.1557

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 8550 genes, now there are 68.

## There are 55 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 
##    0.2203    0.1878    0.6713    0.2263    0.6575    0.2105    0.2015    0.2387 
## TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011 TMRC20012 
##    0.2024    0.6894    0.1877    0.6366    0.1683    0.2077    0.5678    0.5511 
## TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 
##    0.1656    0.6549    0.6493    0.1597    0.6529    0.1858    0.6854    0.1538 
## TMRC20022 TMRC20024 TMRC20036 TMRC20069 TMRC20033 TMRC20031 TMRC20055 TMRC20079 
##    0.6762    0.7158    0.6760    0.6975    0.7211    0.6116    0.7106    0.1868 
## TMRC20071 TMRC20078 TMRC20058 TMRC20072 TMRC20059 TMRC20048 TMRC20060 TMRC20077 
##    0.1697    0.5313    0.2196    0.5354    0.1380    0.1543    0.7607    0.5706 
## TMRC20074 TMRC20063 TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 
##    0.6620    0.6333    0.5715    0.1784    0.1946    0.1804    0.1904    0.6151 
## TMRC20049 TMRC20062 TMRC20080 TMRC20043 TMRC20054 TMRC20046 TMRC20044 
##    0.6976    0.1887    0.1547    0.1729    0.2026    0.6344    0.6226

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 67 samples.

new_snps <- sm(count_expt_snps(lp_snp, annot_column = "bcftable"))

## Error : 'preprocessing/TMRC20080/outputs/vcfutils_lpanamensis_v36/r1_trimmed_lpanamensis_v36_count.txt' does not exist in current working directory ('/mnt/cbcb/fs01_abelew/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019').
## Error : 'preprocessing/TMRC20080/outputs/vcfutils_lpanamensis_v36/r1_trimmed_lpanamensis_v36_count.txt' does not exist in current working directory ('/mnt/cbcb/fs01_abelew/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019').

## Error in Biobase::`sampleNames<-`(`*tmp*`, value = colnames(snp_exprs)): number of new names (66) should equal number of rows in AnnotatedDataFrame (67)

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

both_snps <- combine_expts(new_snps, old_snps)

## Error in combine_expts(new_snps, old_snps): object 'new_snps' not found

both_norm <- sm(normalize_expt(both_snps, transform = "log2", convert = "cpm", filter = TRUE))

## Error in normalize_expt(both_snps, transform = "log2", convert = "cpm", : object 'both_snps' not found

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

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

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)

## Error in plot_heatmap(expt_data, expt_colors = expt_colors, expt_design = expt_design, : object 'both_norm' not found

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

## Error in pp(file = "images/raw_snp_disheat.png", image = old_new_variant_heatmap, : object 'old_new_variant_heatmap' not found

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")

## Error in get_snp_sets(both_snps, factor = "condition"): object 'both_snps' not found

both_expt <- combine_expts(lp_expt, old_expt)

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

## Error in snps_vs_genes(both_expt, snp_sets, expt_name_col = "chromosome"): object 'snp_sets' not found

## I think we have some metrics here we can plot...
snp_subset <- sm(snp_subset_genes(
  both_expt, both_snps,
  genes = c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
            "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300")))

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

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

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

zymo_heat

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

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

7.3 SNPS associated with clinical response in the TMRC samples

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

## Error in get_snp_sets(new_snps, factor = "clinicalresponse"): object 'new_snps' not found

density_vec <- clinical_sets[["density"]]

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

chromosome_idx <- grep(pattern = "LpaL", x = names(density_vec))

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

density_df <- as.data.frame(density_vec[chromosome_idx])

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'density_vec' not found

density_df[["chr"]] <- rownames(density_df)

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

colnames(density_df) <- c("density_vec", "chr")

## Error in colnames(density_df) <- c("density_vec", "chr"): object 'density_df' not found

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))

## Error in ggplot(density_df, aes_string(x = "chr", y = "density_vec")): object 'density_df' not found

## 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"))

## Error in snps_vs_genes(lp_expt, clinical_sets, expt_name_col = "chromosome"): object 'clinical_sets' not found

snp_density <- merge(as.data.frame(clinical_genes[["summary_by_gene"]]),
                     as.data.frame(fData(lp_expt)),
                     by = "row.names")

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'merge': error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'clinical_genes' not found

snp_density <- snp_density[, c(1, 2, 4, 15)]

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

colnames(snp_density) <- c("name", "snps", "product", "length")

## Error in colnames(snp_density) <- c("name", "snps", "product", "length"): object 'snp_density' not found

snp_density[["product"]] <- tolower(snp_density[["product"]])

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

snp_density[["length"]] <- as.numeric(snp_density[["length"]])

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

snp_density[["density"]] <- snp_density[["snps"]] / snp_density[["length"]]

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

snp_idx <- order(snp_density[["density"]], decreasing = TRUE)

## Error in eval(quote(list(...)), env): object 'snp_density' not found

snp_density <- snp_density[snp_idx, ]

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

removers <- c("amastin", "gp63", "leishmanolysin")
for (r in removers) {
  drop_idx <- grepl(pattern = r, x = snp_density[["product"]])
  snp_density <- snp_density[!drop_idx, ]
}

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

## Filter these for [A|a]mastin gp63 Leishmanolysin

clinical_snps <- snps_intersections(lp_expt, clinical_sets, chr_column = "chromosome")

## Error in snps_intersections(lp_expt, clinical_sets, chr_column = "chromosome"): object 'clinical_sets' not found

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

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'clinical_snps' not found

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

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'clinical_snps' not found

head(fail_ref_snps)

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

head(cure_snps)

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

annot <- fData(lp_expt)
clinical_interest <- as.data.frame(clinical_snps[["gene_summaries"]][["cure"]])

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'clinical_snps' not found

clinical_interest <- merge(clinical_interest,
                           as.data.frame(clinical_snps[["gene_summaries"]][["failure, reference strain"]]),
                           by = "row.names")

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

rownames(clinical_interest) <- clinical_interest[["Row.names"]]

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

clinical_interest[["Row.names"]] <- NULL

## Error in clinical_interest[["Row.names"]] <- NULL: object 'clinical_interest' not found

colnames(clinical_interest) <- c("cure_snps","fail_snps")

## Error in colnames(clinical_interest) <- c("cure_snps", "fail_snps"): object 'clinical_interest' not found

annot <- merge(annot, clinical_interest, by = "row.names")

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

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.

new_sets <- get_snp_sets(new_snps, factor = "phenotypiccharacteristics")

## Error in get_snp_sets(new_snps, factor = "phenotypiccharacteristics"): object 'new_snps' not found

summary(new_sets)

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

## 1000000: 2.2
## 0100000: 2.3

summary(new_sets[["intersections"]][["10000"]])

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

summary(new_sets[["intersections"]][["01000"]])

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

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 = "22", 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 = "23",
                                          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.

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

## Error in snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"): object 'new_sets' not found

new_zymo_norm <- normalize_expt(new_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE)

## Error in normalize_expt(new_snps, filter = TRUE, convert = "cpm", norm = "quant", : object 'new_snps' not found

new_zymo_norm <- set_expt_conditions(new_zymo_norm, fact = "phenotypiccharacteristics")

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

zymo_heat <- plot_disheat(new_zymo_norm)

## Error in plot_heatmap(expt_data, expt_colors = expt_colors, expt_design = expt_design, : object 'new_zymo_norm' not found

zymo_heat[["plot"]]

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

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"]]

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

undef_idx <- is.na(des[["strain"]])

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

des[undef_idx, "strain"] <- "unknown"

## Error in des[undef_idx, "strain"] <- "unknown": object 'des' not found

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

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

zymo_missing_idx <- is.na(des[["phenotypiccharacteristics"]])

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

des[["phenotypiccharacteristics"]] <- as.character(des[["phenotypiccharacteristics"]])

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

des[["clinicalcategorical"]] <- as.character(des[["clinicalcategorical"]])

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

des[zymo_missing_idx, "phenotypiccharacteristics"] <- "unknown"

## Error in des[zymo_missing_idx, "phenotypiccharacteristics"] <- "unknown": object 'des' not found

mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c("phenotypiccharacteristics", "clinicalcategorical")])

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'des' not found

unknown_clinical <- is.na(col_data[["clinicalcategorical"]])

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

row_data <- as.data.frame(des[, c("strain")])

## Error in h(simpleError(msg, call)): error in evaluating the argument 'x' in selecting a method for function 'as.data.frame': object 'des' not found

colnames(col_data) <- c("zymodeme", "outcome")

## Error in colnames(col_data) <- c("zymodeme", "outcome"): object 'col_data' not found

col_data[unknown_clinical, "outcome"] <- "undefined"

## Error in col_data[unknown_clinical, "outcome"] <- "undefined": object 'col_data' not found

colnames(row_data) <- c("strain")

## Error in colnames(row_data) <- c("strain"): object 'row_data' not found

myannot <- list(
  "Col" = list("data" = col_data),
  "Row" = list("data" = row_data))

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

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)

## Error in annHeatmap2(correlations, dendrogram = mydendro, annotation = myannot, : object 'correlations' not found

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

## Error in pp(file = "images/dendro_heatmap.png", image = map1, height = 20, : object 'map1' not found

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

$Circular Manhattan$ Rectangular Manhattan

8.5 Try out MatrixEQTL

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

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

## For this, let us use the 'new_snps' data structure.
## Caveat here: these need to be coerced to numbers.
my_covariates <- pData(new_snps)[, c("phenotypiccharacteristics", "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 b006f25aaf505e4650ed89d020b1cb7e4b46f6a0

## This is hpgltools commit: Mon Dec 13 14:13:47 2021 -0500: b006f25aaf505e4650ed89d020b1cb7e4b46f6a0

## 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", "phenotypiccharacteristics")
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",
    "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)

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)

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}
new_sets <- get_snp_sets(new_snps, factor = "phenotypiccharacteristics")
summary(new_sets)
## 1000000: 2.2
## 0100000: 2.3

summary(new_sets[["intersections"]][["10000"]])
summary(new_sets[["intersections"]][["01000"]])
```

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 = "22", 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 = "23",
                                          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}
snp_genes <- sm(snps_vs_genes(lp_expt, new_sets, expt_name_col = "chromosome"))
new_zymo_norm <- normalize_expt(new_snps, filter = TRUE, convert = "cpm", norm = "quant", transform = TRUE)
new_zymo_norm <- set_expt_conditions(new_zymo_norm, fact = "phenotypiccharacteristics")

zymo_heat <- plot_disheat(new_zymo_norm)
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[["phenotypiccharacteristics"]])
des[["phenotypiccharacteristics"]] <- as.character(des[["phenotypiccharacteristics"]])
des[["clinicalcategorical"]] <- as.character(des[["clinicalcategorical"]])
des[zymo_missing_idx, "phenotypiccharacteristics"] <- "unknown"
mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c("phenotypiccharacteristics", "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("phenotypiccharacteristics", "clinicalcategorical")]
for (col in colnames(my_covariates)) {
  my_covariates[[col]] <- as.numeric(as.factor(my_covariates[[col]]))
}
my_covariates <- t(my_covariates)

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

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

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

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

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

useModel = modelLINEAR # modelANOVA, modelLINEAR, or modelLINEAR_CROSS

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

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



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

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


TMRC2 Comprehensive Data Analysis: 202201

atb abelew@gmail.com

2022-01-07