TMRC2 Comprehensive Data Analysis: 202103

1 Introduction

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

In all cases the processing performed was:

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

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

2 Annotations

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

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

tt <- sm(library(EuPathDB))
tt <- sm(library(org.Lpanamensis.MHOMCOL81L13.v46.eg.db))
pan_db <- org.Lpanamensis.MHOMCOL81L13.v46.eg.db
all_fields <- columns(pan_db)

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

lp_go <- sm(load_orgdb_go(pan_db))
lp_lengths <- all_lp_annot[, c("gid", "annot_cds_length")]
colnames(lp_lengths)  <- c("ID", "length")

orthos <- sm(EuPathDB::extract_eupath_orthologs(db=pan_db))

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

3 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 primary 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.

3.1 Generate expressionsets

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

sample_sheet <- glue::glue("sample_sheets/tmrc2_samples_{ver}.xlsx")
lp_expt <- sm(create_expt(sample_sheet,
                          gene_info=hisat_annot,
                          id_column="hpglidentifier",
                          file_column="lpanamensisv36hisatfile"))
lp_expt <- set_expt_conditions(lp_expt, fact="zymodemecategorical")

libsizes <- plot_libsize(lp_expt)

## The scale difference between the smallest and largest
## libraries is > 10. Assuming a log10 scale is better, set scale = FALSE if not.

libsizes$plot

## I think samples 7,10 should be removed at minimum, probably also 9,11
nonzero <- plot_nonzero(lp_expt)
nonzero$plot

## Warning: ggrepel: 11 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

plot_boxplot(lp_expt)

## This data will benefit from being displayed on the log scale.

## If this is not desired, set scale='raw'

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

## Changed 2659 zero count features.

4 TODO:

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

4.1 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’.

4.2 Exclude lowest coverage

lp_expt <- subset_expt(lp_expt, nonzero = 8550)

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

## TMRC20002 TMRC20006 
##  11681227   6670348

## There were 30, now there are 28 samples.

all_norm <- sm(normalize_expt(lp_expt, norm="quant", transform="log2", convert="cpm",
                              batch=FALSE, filter=TRUE))
zymo_pca <- plot_pca(all_norm, plot_title="PCA of parasite expression values")
zymo_pca$plot

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

zymo_3dpca <- plot_3d_pca(zymo_pca)
zymo_3dpca$plot

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

## Removing 153 low-count genes (8625 remaining).

## batch_counts: Before batch/surrogate estimation, 487 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 1187 entries are 0<x<1: 0%.

## Setting 103 low elements to zero.

## transform_counts: Found 103 values equal to 0, adding 1 to the matrix.

all_nb_pca <- plot_pca(all_nb)
all_nb_pca$plot

corheat <- plot_corheat(all_norm, title="Correlation heatmap of parasite expression values
(Same legend as above)")$plot

plot_sm(all_norm)$plot

## Performing correlation.

sm(plot_variance_coefficients(all_norm))$plot

sm(plot_sample_cvheatmap(all_norm))$plot

## NULL

4.3 Cure/Fail status

cf_expt <- set_expt_conditions(lp_expt, fact="clinicalcategorical")

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

## Removing 153 low-count genes (8625 remaining).

## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.

start_cf <- plot_pca(cf_norm)

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 153 low-count genes (8625 remaining).

## batch_counts: Before batch/surrogate estimation, 5 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 1511 entries are 0<x<1: 1%.

## Setting 24 low elements to zero.

## transform_counts: Found 24 values equal to 0, adding 1 to the matrix.

cf_nb_pca <- plot_pca(cf_nb)
cf_nb_pca$plot

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

## Warning in MASS::cov.trob(data[, vars]): Probable convergence failure

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

## Removing 153 low-count genes (8625 remaining).

## transform_counts: Found 5 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)

## More shallow curves in these plots suggest more genes in this principle component.

4.4 Susceptilibity

Column ‘Q’ in the sample sheet, make a categorical version of it: 0-40 is resistant, 40-60 is indeterminate, 60+ is susceptible

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

## Warning: NAs introduced by coercion

sus_categorical <- starting
na_idx <- is.na(starting)
sus_categorical[na_idx] <- "unknown"

resist_idx <- starting <= 0.4
sus_categorical[resist_idx] <- "resistant"
indeterminant_idx <- starting > 0.4 & starting <= 0.6
sus_categorical[indeterminant_idx] <- "indeterminant"
susceptible_idx <- starting > 0.6
sus_categorical[susceptible_idx] <- "susceptible"

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

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

## Removing 153 low-count genes (8625 remaining).

## transform_counts: Found 5 values equal to 0, adding 1 to the matrix.

sus_pca <- plot_pca(sus_norm)
sus_pca$plot

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

## Removing 153 low-count genes (8625 remaining).

## batch_counts: Before batch/surrogate estimation, 487 entries are x==0: 0%.

## batch_counts: Before batch/surrogate estimation, 1187 entries are 0<x<1: 0%.

## Setting 89 low elements to zero.

## transform_counts: Found 89 values equal to 0, adding 1 to the matrix.

sus_nb_pca <- plot_pca(sus_nb)
sus_nb_pca$plot

4.4.1 Notes

The following samples are much lower coverage:

• TMRC20002
• TMRC20006
• TMRC20007
• TMRC20008

At this time, we do not have very many samples, so the set of metrics/plots is fairly limited. There is really only one factor in the metadata which we can use for performing differential expression analyses, the ‘zymodeme’.

5 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.

5.1 Differential expression

5.1.1 With respect to zymodeme attribution

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

## Using a subset expression.

## There were 28, now there are 15 samples.

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")))

5.2 With respect to cure/failure

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")))

5.3 With respect to susceptibility

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")))

## Error: Sheet 'up_limma_susceptible_vs_indeterminant' does not exist.

5.4 Ontology searches

## 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))

5.4.1 A couple plots from the differential expression

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

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

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

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

5.4.1.3 MA plot of the differential expression between the zymodemes.

zy_table$plots[[1]][["deseq_ma_plots"]][["plot"]]

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

zy_go_up$pvalue_plots$bpp_plot_over

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

zy_go_down$pvalue_plots$bpp_plot_over

5.5 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.

5.5.1 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(all_norm, ids=my_genes, method="keep")

## Before removal, there were 8625 entries.

## Now there are 6 entries.

## Percent of the counts kept after filtering: 0.086, 0.084, 0.083, 0.085, 0.086, 0.083, 0.084, 0.087, 0.083, 0.084, 0.083, 0.084, 0.083, 0.083, 0.085, 0.085, 0.083, 0.083, 0.083, 0.083, 0.081, 0.081, 0.085, 0.085, 0.081, 0.082, 0.087, 0.081

## There are 28 samples which kept less than 90 percent counts.

##      TMRC20001 TMRC20004 TMRC20005 TMRC20029 TMRC20007 TMRC20008 TMRC20027
##      TMRC20028 TMRC20032 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011
##      TMRC20012 TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20020
##      TMRC20021 TMRC20022 TMRC20025 TMRC20024 TMRC20033 TMRC20026 TMRC20031

test <- plot_sample_heatmap(zymo_expt, row_label=my_names)

5.6 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_310031300" "LPAL13_000012000" "LPAL13_000038400"
##  [5] "LPAL13_000038500" "LPAL13_000012100" "LPAL13_340039600" "LPAL13_050005000"
##  [9] "LPAL13_210015500" "LPAL13_310039200" "LPAL13_270034100" "LPAL13_250006300"
## [13] "LPAL13_290018800" "LPAL13_180013900" "LPAL13_170015400" "LPAL13_350044000"
## [17] "LPAL13_340039700" "LPAL13_000041000" "LPAL13_200013000" "LPAL13_240009700"
## [21] "LPAL13_330021800" "LPAL13_140019300" "LPAL13_140019100" "LPAL13_330021900"
## [25] "LPAL13_260031400" "LPAL13_250009900" "LPAL13_210005000" "LPAL13_350073200"
## [29] "LPAL13_280037900" "LPAL13_320038700" "LPAL13_230011200" "LPAL13_250025700"
## [33] "LPAL13_140019200" "LPAL13_300031600" "LPAL13_310032500" "LPAL13_230011400"
## [37] "LPAL13_000010600" "LPAL13_230011500" "LPAL13_310028500"

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

## Before removal, there were 8625 entries.

## Now there are 39 entries.

## Percent of the counts kept after filtering: 0.351, 0.260, 0.244, 0.270, 0.254, 0.246, 0.280, 0.275, 0.289, 0.372, 0.247, 0.368, 0.366, 0.259, 0.239, 0.370, 0.254, 0.255, 0.370, 0.244, 0.246, 0.370, 0.239, 0.280, 0.269, 0.250, 0.249, 0.248

## There are 28 samples which kept less than 90 percent counts.

##      TMRC20001 TMRC20004 TMRC20005 TMRC20029 TMRC20007 TMRC20008 TMRC20027
##      TMRC20028 TMRC20032 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011
##      TMRC20012 TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20020
##      TMRC20021 TMRC20022 TMRC20025 TMRC20024 TMRC20033 TMRC20026 TMRC20031

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

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

5.6.2 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(all_norm, ids=rownames(down_shared), method="keep")

## Before removal, there were 8625 entries.

## Now there are 80 entries.

## Percent of the counts kept after filtering: 0.535, 0.834, 0.835, 0.813, 0.829, 0.844, 0.542, 0.504, 0.762, 0.491, 0.881, 0.474, 0.514, 0.878, 0.832, 0.502, 0.865, 0.890, 0.506, 0.868, 0.879, 0.509, 0.910, 0.629, 0.892, 0.890, 0.859, 0.839

## There are 28 samples which kept less than 90 percent counts.

##      TMRC20001 TMRC20004 TMRC20005 TMRC20029 TMRC20007 TMRC20008 TMRC20027
##      TMRC20028 TMRC20032 TMRC20015 TMRC20009 TMRC20010 TMRC20016 TMRC20011
##      TMRC20012 TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20020
##      TMRC20021 TMRC20022 TMRC20025 TMRC20024 TMRC20033 TMRC20026 TMRC20031

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

6 SNP profiles

In this block, I am combining our previous samples and our new samples in the hopes of finding variant positions which help elucidate aspects of either the new or old samples. 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. We may be able 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).

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

new_snps <- sm(count_expt_snps(lp_expt, 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_norm <- set_expt_conditions(both_norm, fact="strain")

6.1 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).

tt <- plot_disheat(both_norm)

snp_sets <- get_snp_sets(both_snps, factor="condition")

## The factor z2.3 has 7 rows.

## The factor z2.2 has 8 rows.

## The factor unknown has 13 rows.

## The factor sh has 13 rows.

## The factor chr has 14 rows.

## The factor inf has 6 rows.

## Iterating over 727 elements.

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

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)))

7 Clinical response for new samples

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

## The factor Cure has 9 rows.

## The factor Failure has 12 rows.

## The factor Laboratory line has 2 rows.

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

## The factor ND has only 1 row.

## The factor Reference strain has 3 rows.

## Iterating over 686 elements.

clinical_genes <- sm(snps_vs_genes(lp_expt, clinical_sets, expt_name_col="chromosome"))
clinical_snps <- snps_intersections(lp_expt, clinical_sets, chr_column="chromosome")
head(as.data.frame(clinical_snps$inters[["Failure"]]))

##                                       seqnames  start    end width strand
## chr_LpaL13-02_pos_205839_ref_C_alt_T LpaL13-02 205839 205840     2      +
## chr_LpaL13-03_pos_107522_ref_T_alt_C LpaL13-03 107522 107523     2      +
## chr_LpaL13-05_pos_161416_ref_T_alt_C LpaL13-05 161416 161417     2      +
## chr_LpaL13-06_pos_342394_ref_G_alt_C LpaL13-06 342394 342395     2      +
## chr_LpaL13-07_pos_280944_ref_A_alt_G LpaL13-07 280944 280945     2      +
## chr_LpaL13-07_pos_387049_ref_C_alt_T LpaL13-07 387049 387050     2      +

head(as.data.frame(clinical_snps$inters[["Cure"]]))

##                                       seqnames  start    end width strand
## chr_LpaL13-04_pos_37865_ref_G_alt_A  LpaL13-04  37865  37866     2      +
## chr_LpaL13-04_pos_37867_ref_A_alt_G  LpaL13-04  37867  37868     2      +
## chr_LpaL13-05_pos_340999_ref_G_alt_A LpaL13-05 340999 341000     2      +
## chr_LpaL13-06_pos_231508_ref_C_alt_T LpaL13-06 231508 231509     2      +
## chr_LpaL13-06_pos_288177_ref_C_alt_G LpaL13-06 288177 288178     2      +
## chr_LpaL13-09_pos_177788_ref_C_alt_T LpaL13-09 177788 177789     2      +

head(clinical_snps$gene_summaries$Failure)

## LPAL13_200008400 LPAL13_300019900 LPAL13_000017900 LPAL13_100008800 
##                3                3                2                2 
## LPAL13_200008500 LPAL13_200014300 
##                2                2

head(clinical_snps$gene_summaries$Cure)

## LPAL13_040006400 LPAL13_190014700 LPAL13_200013000 LPAL13_200014600 
##                2                2                2                2 
## LPAL13_200015100 LPAL13_200016900 
##                2                2

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

8 Zymodeme for new samples

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

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

## Error in if (sum(columns) < 1) {: missing value where TRUE/FALSE needed

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)

## Removing 0 low-count genes (516896 remaining).

## transform_counts: Found 3654576 values equal to 0, adding 1 to the matrix.

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

zymo_subset <- snp_subset_genes(lp_expt, new_snps,
                                genes=c("LPAL13_120010900", "LPAL13_340013000", "LPAL13_000054100",
                                        "LPAL13_140006100", "LPAL13_180018500", "LPAL13_320022300"))

## Warning in .Seqinfo.mergexy(x, y): Each of the 2 combined objects has sequence levels not in the other:
##   - in 'x': LPAL13-SCAF000002, LPAL13-SCAF000003, LPAL13-SCAF000004, LPAL13-SCAF000005, LPAL13-SCAF000009, LPAL13-SCAF000013, LPAL13-SCAF000014, LPAL13-SCAF000015, LPAL13-SCAF000018, LPAL13-SCAF000019, LPAL13-SCAF000020, LPAL13-SCAF000022, LPAL13-SCAF000023, LPAL13-SCAF000026, LPAL13-SCAF000029, LPAL13-SCAF000030, LPAL13-SCAF000031, LPAL13-SCAF000032, LPAL13-SCAF000035, LPAL13-SCAF000036, LPAL13-SCAF000037, LPAL13-SCAF000038, LPAL13-SCAF000042, LPAL13-SCAF000043, LPAL13-SCAF000045, LPAL13-SCAF000047, LPAL13-SCAF000049, LPAL13-SCAF000050, LPAL13-SCAF000052, LPAL13-SCAF000054, LPAL13-SCAF000056, LPAL13-SCAF000057, LPAL13-SCAF000058, LPAL13-SCAF000060, LPAL13-SCAF000066, LPAL13-SCAF000067, LPAL13-SCAF000069, LPAL13-SCAF000070, LPAL13-SCAF000073, LPAL13-SCAF000081, LPAL13-SCAF000082, LPAL13-SCAF000083, LPAL13-SCAF000085, LPAL13-SCAF000086, LPAL13-SCAF000088, LPAL13-SCAF000090, LPAL13-SCAF000091, LPAL13-SCAF000092, LPAL13-SCAF000095, LPAL13-SCAF000098, LPAL13-SCAF000101, LPAL13-SCAF000103, LPAL13-SCAF000106, LPAL13-SCAF000109, LPAL13-SCAF000111, LPAL13-SCAF000112, LPAL13-SCAF000113, LPAL13-SCAF000118, LPAL13-SCAF000125, LPAL13-SCAF000126, LPAL13-SCAF000138, LPAL13-SCAF000139, LPAL13-SCAF000140, LPAL13-SCAF000141, LPAL13-SCAF000144, LPAL13-SCAF000145, LPAL13-SCAF000147, LPAL13-SCAF000148, LPAL13-SCAF000150, LPAL13-SCAF000151, LPAL13-SCAF000152, LPAL13-SCAF000154, LPAL13-SCAF000155, LPAL13-SCAF000156, LPAL13-SCAF000157, LPAL13-SCAF000158, LPAL13-SCAF000159, LPAL13-SCAF000160, LPAL13-SCAF000161, LPAL13-SCAF000163, LPAL13-SCAF000164, LPAL13-SCAF000167, LPAL13-SCAF000168, LPAL13-SCAF000169, LPAL13-SCAF000170, LPAL13-SCAF000175, LPAL13-SCAF000177, LPAL13-SCAF000178, LPAL13-SCAF000179, LPAL13-SCAF000180, LPAL13-SCAF000183, LPAL13-SCAF000184, LPAL13-SCAF000185, LPAL13-SCAF000189, LPAL13-SCAF000190, LPAL13-SCAF000192, LPAL13-SCAF000195, LPAL13-SCAF000196, LPAL13-SCAF000198, LPAL13-SCAF000199, LPAL13-SCAF000204, LPAL13-SCAF000207, LPAL13-SCAF000208, LPAL13-SCAF000210, LPAL13-SCAF000212, LPAL13-SCAF000213, LPAL13-SCAF000214, LPAL13-SCAF000215, LPAL13-SCAF000216, LPAL13-SCAF000218, LPAL13-SCAF000219, LPAL13-SCAF000221, LPAL13-SCAF000222, LPAL13-SCAF000223, LPAL13-SCAF000224, LPAL13-SCAF000225, LPAL13-SCAF000226, LPAL13-SCAF000228, LPAL13-SCAF000234, LPAL13-SCAF000236, LPAL13-SCAF000238, LPAL13-SCAF000240, LPAL13-SCAF000241, LPAL13-SCAF000242, LPAL13-SCAF000243, LPAL13-SCAF000244, LPAL13-SCAF000246, LPAL13-SCAF000247, LPAL13-SCAF000251, LPAL13-SCAF000252, LPAL13-SCAF000254, LPAL13-SCAF000255, LPAL13-SCAF000257, LPAL13-SCAF000258, LPAL13-SCAF000260, LPAL13-SCAF000262, LPAL13-SCAF000263, LPAL13-SCAF000268, LPAL13-SCAF000269, LPAL13-SCAF000270, LPAL13-SCAF000272, LPAL13-SCAF000273, LPAL13-SCAF000274, LPAL13-SCAF000275, LPAL13-SCAF000276, LPAL13-SCAF000277, LPAL13-SCAF000278, LPAL13-SCAF000279, LPAL13-SCAF000280, LPAL13-SCAF000282, LPAL13-SCAF000283, LPAL13-SCAF000284, LPAL13-SCAF000289, LPAL13-SCAF000290, LPAL13-SCAF000293, LPAL13-SCAF000294, LPAL13-SCAF000297, LPAL13-SCAF000298, LPAL13-SCAF000299, LPAL13-SCAF000304, LPAL13-SCAF000305, LPAL13-SCAF000306, LPAL13-SCAF000307, LPAL13-SCAF000308, LPAL13-SCAF000311, LPAL13-SCAF000312, LPAL13-SCAF000315, LPAL13-SCAF000318, LPAL13-SCAF000323, LPAL13-SCAF000324, LPAL13-SCAF000325, LPAL13-SCAF000327, LPAL13-SCAF000329, LPAL13-SCAF000331, LPAL13-SCAF000332, LPAL13-SCAF000333, LPAL13-SCAF000334, LPAL13-SCAF000336, LPAL13-SCAF000341, LPAL13-SCAF000342, LPAL13-SCAF000343, LPAL13-SCAF000344, LPAL13-SCAF000345, LPAL13-SCAF000346, LPAL13-SCAF000348, LPAL13-SCAF000349, LPAL13-SCAF000350, LPAL13-SCAF000351, LPAL13-SCAF000352, LPAL13-SCAF000353, LPAL13-SCAF000354, LPAL13-SCAF000355, LPAL13-SCAF000356, LPAL13-SCAF000357, LPAL13-SCAF000359, LPAL13-SCAF000360, LPAL13-SCAF000361, LPAL13-SCAF000362, LPAL13-SCAF000365, LPAL13-SCAF000366, LPAL13-SCAF000369, LPAL13-SCAF000371, LPAL13-SCAF000372, LPAL13-SCAF000373, LPAL13-SCAF000375, LPAL13-SCAF000376, LPAL13-SCAF000377, LPAL13-SCAF000378, LPAL13-SCAF000379, LPAL13-SCAF000380, LPAL13-SCAF000381, LPAL13-SCAF000382, LPAL13-SCAF000383, LPAL13-SCAF000384, LPAL13-SCAF000385, LPAL13-SCAF000386, LPAL13-SCAF000387, LPAL13-SCAF000389, LPAL13-SCAF000390, LPAL13-SCAF000392, LPAL13-SCAF000393, LPAL13-SCAF000394, LPAL13-SCAF000395, LPAL13-SCAF000396, LPAL13-SCAF000398, LPAL13-SCAF000399, LPAL13-SCAF000402, LPAL13-SCAF000404, LPAL13-SCAF000406, LPAL13-SCAF000407, LPAL13-SCAF000408, LPAL13-SCAF000409, LPAL13-SCAF000410, LPAL13-SCAF000411, LPAL13-SCAF000412, LPAL13-SCAF000413, LPAL13-SCAF000414, LPAL13-SCAF000416, LPAL13-SCAF000418, LPAL13-SCAF000422, LPAL13-SCAF000423, LPAL13-SCAF000425, LPAL13-SCAF000427, LPAL13-SCAF000428, LPAL13-SCAF000429, LPAL13-SCAF000431, LPAL13-SCAF000433, LPAL13-SCAF000435, LPAL13-SCAF000437, LPAL13-SCAF000438, LPAL13-SCAF000439, LPAL13-SCAF000441, LPAL13-SCAF000442, LPAL13-SCAF000443, LPAL13-SCAF000444, LPAL13-SCAF000445, LPAL13-SCAF000449, LPAL13-SCAF000450, LPAL13-SCAF000451, LPAL13-SCAF000452, LPAL13-SCAF000454, LPAL13-SCAF000455, LPAL13-SCAF000457, LPAL13-SCAF000458, LPAL13-SCAF000462, LPAL13-SCAF000464, LPAL13-SCAF000466, LPAL13-SCAF000467, LPAL13-SCAF000472, LPAL13-SCAF000473, LPAL13-SCAF000474, LPAL13-SCAF000475, LPAL13-SCAF000476, LPAL13-SCAF000478, LPAL13-SCAF000479, LPAL13-SCAF000480, LPAL13-SCAF000481, LPAL13-SCAF000482, LPAL13-SCAF000485, LPAL13-SCAF000487, LPAL13-SCAF000489, LPAL13-SCAF000493, LPAL13-SCAF000494, LPAL13-SCAF000497, LPAL13-SCAF000498, LPAL13-SCAF000499, LPAL13-SCAF000501, LPAL13-SCAF000502, LPAL13-SCAF000504, LPAL13-SCAF000506, LPAL13-SCAF000509, LPAL13-SCAF000510, LPAL13-SCAF000513, LPAL13-SCAF000514, LPAL13-SCAF000516, LPAL13-SCAF000517, LPAL13-SCAF000518, LPAL13-SCAF000519, LPAL13-SCAF000520, LPAL13-SCAF000521, LPAL13-SCAF000523, LPAL13-SCAF000524, LPAL13-SCAF000525, LPAL13-SCAF000526, LPAL13-SCAF000530, LPAL13-SCAF000531, LPAL13-SCAF000534, LPAL13-SCAF000545, LPAL13-SCAF000546, LPAL13-SCAF000550, LPAL13-SCAF000551, LPAL13-SCAF000557, LPAL13-SCAF000561, LPAL13-SCAF000565, LPAL13-SCAF000571, LPAL13-SCAF000579, LPAL13-SCAF000581, LPAL13-SCAF000584, LPAL13-SCAF000589, LPAL13-SCAF000592, LPAL13-SCAF000594, LPAL13-SCAF000595, LPAL13-SCAF000596, LPAL13-SCAF000597, LPAL13-SCAF000602, LPAL13-SCAF000604, LPAL13-SCAF000606, LPAL13-SCAF000608, LPAL13-SCAF000609, LPAL13-SCAF000612, LPAL13-SCAF000613, LPAL13-SCAF000615, LPAL13-SCAF000620, LPAL13-SCAF000621, LPAL13-SCAF000623, LPAL13-SCAF000624, LPAL13-SCAF000629, LPAL13-SCAF000630, LPAL13-SCAF000631, LPAL13-SCAF000632, LPAL13-SCAF000633, LPAL13-SCAF000634, LPAL13-SCAF000635, LPAL13-SCAF000638, LPAL13-SCAF000640, LPAL13-SCAF000642, LPAL13-SCAF000647, LPAL13-SCAF000648, LPAL13-SCAF000657, LPAL13-SCAF000658, LPAL13-SCAF000660, LPAL13-SCAF000662, LPAL13-SCAF000663, LPAL13-SCAF000664, LPAL13-SCAF000665, LPAL13-SCAF000667, LPAL13-SCAF000669, LPAL13-SCAF000670, LPAL13-SCAF000671, LPAL13-SCAF000674, LPAL13-SCAF000675, LPAL13-SCAF000676, LPAL13-SCAF000677, LPAL13-SCAF000678, LPAL13-SCAF000683, LPAL13-SCAF000684, LPAL13-SCAF000685, LPAL13-SCAF000686, LPAL13-SCAF000687, LPAL13-SCAF000689, LPAL13-SCAF000690, LPAL13-SCAF000691, LPAL13-SCAF000692, LPAL13-SCAF000693, LPAL13-SCAF000694, LPAL13-SCAF000699, LPAL13-SCAF000701, LPAL13-SCAF000702, LPAL13-SCAF000703, LPAL13-SCAF000705, LPAL13-SCAF000706, LPAL13-SCAF000708, LPAL13-SCAF000709, LPAL13-SCAF000710, LPAL13-SCAF000712, LPAL13-SCAF000715, LPAL13-SCAF000718, LPAL13-SCAF000721, LPAL13-SCAF000725, LPAL13-SCAF000728, LPAL13-SCAF000729, LPAL13-SCAF000730, LPAL13-SCAF000731, LPAL13-SCAF000733, LPAL13-SCAF000736, LPAL13-SCAF000739, LPAL13-SCAF000740, LPAL13-SCAF000741, LPAL13-SCAF000742, LPAL13-SCAF000743, LPAL13-SCAF000745, LPAL13-SCAF000746, LPAL13-SCAF000747, LPAL13-SCAF000749, LPAL13-SCAF000750, LPAL13-SCAF000751, LPAL13-SCAF000752, LPAL13-SCAF000753, LPAL13-SCAF000754, LPAL13-SCAF000755, LPAL13-SCAF000756, LPAL13-SCAF000757, LPAL13-SCAF000758, LPAL13-SCAF000759, LPAL13-SCAF000763, LPAL13-SCAF000764, LPAL13-SCAF000765, LPAL13-SCAF000766, LPAL13-SCAF000767, LPAL13-SCAF000768, LPAL13-SCAF000769, LPAL13-SCAF000770, LPAL13-SCAF000771, LPAL13-SCAF000773, LPAL13-SCAF000774, LPAL13-SCAF000775, LPAL13-SCAF0007

## Before removal, there were 516896 entries.

## Now there are 82 entries.

## Percent of the counts kept after filtering: 0.037, 0.000, 0.042, 0.000, 0.053, 0.046, 0.060, 0.077, 0.037, 0.026, 0.000, 0.028, 0.026, 0.025, 0.000, 0.029, 0.020, 0.018, 0.033, 0.080, 0.072, 0.032, 0.000, 0.063, 0.041, 0.046, 0.082, 0.046

## There are 28 samples which kept less than 90 percent counts.

##      tmrc20001 tmrc20004 tmrc20005 tmrc20029 tmrc20007 tmrc20008 tmrc20027
##      tmrc20028 tmrc20032 tmrc20015 tmrc20009 tmrc20010 tmrc20016 tmrc20011
##      tmrc20012 tmrc20013 tmrc20017 tmrc20014 tmrc20018 tmrc20019 tmrc20020
##      tmrc20021 tmrc20022 tmrc20025 tmrc20024 tmrc20033 tmrc20026 tmrc20031

zymo_subset <- set_expt_conditions(zymo_subset, fact="phenotypiccharacteristics")
## zymo_heat <- plot_sample_heatmap(zymo_subset, row_label=rownames(exprs(snp_subset)))

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[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"))(170)
map1 <- annHeatmap2(
  correlations,
  dendrogram=mydendro,
  annotation=myannot,
  cluster=myclust,
  labels=mylabs)
##  col=hmcols)
plot(map1)

9 Using Variant profiles to make guesses about strains and chronic/self-healing

The following uses the same information to make some guesses about the strains used in the new samples.

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))

mydendro <- list(
  "clustfun" = hclust,
  "lwd" = 2.0)
col_data <- as.data.frame(des[, c("condition")])
row_data <- as.data.frame(des[, c("strain")])
colnames(col_data) <- c("condition")
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"))(170)
map1 <- annHeatmap2(
  correlations,
  dendrogram=mydendro,
  annotation=myannot,
  cluster=myclust,
  labels=mylabs)
##  col=hmcols)
plot(map1)

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

## Using a subset expression.

## There were 28, now there are 15 samples.

pheno_snps <- sm(count_expt_snps(pheno, annot_column="bcftable"))

xref_prop <- table(pheno_snps$conditions)
pheno_snps$conditions

##  [1] "z2.3" "z2.2" "z2.2" "z2.3" "z2.2" "z2.3" "z2.3" "z2.2" "z2.2" "z2.3"
## [11] "z2.2" "z2.2" "z2.3" "z2.3" "z2.2"

idx_tbl <- exprs(pheno_snps) > 5
new_tbl <- data.frame(row.names=rownames(exprs(pheno_snps)))
for (n in names(xref_prop)) {
  new_tbl[[n]] <- 0
  idx_cols <- which(pheno_snps[["conditions"]] == n)
  prop_col <- rowSums(idx_tbl[, idx_cols]) / xref_prop[n]
  new_tbl[n] <- prop_col
}
new_tbl[["ratio"]] <- (new_tbl[["z2.2"]] - new_tbl[["z2.3"]])
keepers <- grepl(x=rownames(new_tbl), pattern="LpaL13")
new_tbl <- new_tbl[keepers, ]
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", "ratio")]
library(CMplot)

## Much appreciate for using CMplot.

## Full description, Bug report, Suggestion and the latest codes:

## https://github.com/YinLiLin/CMplot

CMplot(new_tbl)

##  SNP-Density Plotting.

##  Circular-Manhattan Plotting ratio.
##  Rectangular-Manhattan Plotting ratio.

##  QQ Plotting ratio.

##  Plots are stored in: /mnt/sshfs_10186/cbcbsub00/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_tmrc_2019

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 3866d0ef3d5bf766f01b092108ec06406921447c

## This is hpgltools commit: Mon Mar 22 15:33:04 2021 -0400: 3866d0ef3d5bf766f01b092108ec06406921447c

## Saving to tmrc2_02sample_estimation_v202103.rda.xz

tmp <- loadme(filename=savefile)