TMRC2 202305: Promastigote (mostly) Differential Expression Analyses.

1 Contrasts

zymodeme_keeper <- list(
    "zymodeme" = c("z23", "z22"))
susceptibility_keepers <- list(
    "resistant_sensitive" = c("resistant", "sensitive"),
    "resistant_ambiguous" = c("resistant", "ambiguous"),
    "sensitive_ambiguous" = c("sensitive", "ambiguous"))

1.1 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.

1.1.1 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_six_genes <- exclude_genes_expt(lp_two_strains, ids = my_genes, method = "keep")

## Note, I renamed this to subset_genes().

## remove_genes_expt(), before removal, there were 8710 genes, now there are 6.

## There are 93 samples which kept less than 90 percent counts.

## TMRC20001 TMRC20065 TMRC20005 TMRC20066 TMRC20039 TMRC20037 TMRC20038 TMRC20067 TMRC20068 TMRC20041 TMRC20015 TMRC20009 TMRC20010 TMRC20016 
##   0.12101   0.12835   0.13438   0.10804   0.13756   0.11451   0.11446   0.10858   0.11269   0.12840   0.11072   0.11376   0.10292   0.10451 
## TMRC20011 TMRC20012 TMRC20013 TMRC20017 TMRC20014 TMRC20018 TMRC20019 TMRC20070 TMRC20020 TMRC20021 TMRC20022 TMRC20024 TMRC20036 TMRC20069 
##   0.10967   0.11676   0.11865   0.10594   0.10591   0.11517   0.12027   0.11179   0.11100   0.10764   0.12952   0.11481   0.12100   0.11631 
## TMRC20033 TMRC20026 TMRC20031 TMRC20076 TMRC20073 TMRC20055 TMRC20079 TMRC20071 TMRC20078 TMRC20094 TMRC20042 TMRC20058 TMRC20072 TMRC20059 
##   0.11188   0.13633   0.10013   0.12403   0.12398   0.13783   0.12639   0.12003   0.13420   0.12021   0.13766   0.11900   0.14675   0.10984 
## TMRC20048 TMRC20057 TMRC20088 TMRC20056 TMRC20060 TMRC20077 TMRC20074 TMRC20063 TMRC20053 TMRC20052 TMRC20064 TMRC20075 TMRC20051 TMRC20050 
##   0.10679   0.13443   0.12936   0.13510   0.10882   0.12810   0.12494   0.12239   0.12145   0.11690   0.12051   0.11261   0.13149   0.11526 
## TMRC20049 TMRC20062 TMRC20110 TMRC20080 TMRC20043 TMRC20083 TMRC20054 TMRC20085 TMRC20046 TMRC20093 TMRC20089 TMRC20047 TMRC20090 TMRC20044 
##   0.14480   0.13429   0.13812   0.12120   0.11306   0.12235   0.12723   0.12316   0.13182   0.13588   0.11884   0.12391   0.11566   0.13691 
## TMRC20045 TMRC20105 TMRC20108 TMRC20109 TMRC20098 TMRC20096 TMRC20097 TMRC20101 TMRC20092 TMRC20082 TMRC20102 TMRC20099 TMRC20100 TMRC20091 
##   0.12812   0.12219   0.11601   0.11684   0.11771   0.11364   0.11706   0.11784   0.11465   0.10358   0.11399   0.11888   0.10820   0.12935 
## TMRC20084 TMRC20087 TMRC20103 TMRC20104 TMRC20086 TMRC20107 TMRC20081 TMRC20106 TMRC20095 
##   0.11273   0.12564   0.13704   0.11723   0.10752   0.09364   0.10335   0.09894   0.06566

strain_norm <- normalize_expt(zymo_six_genes, convert="rpkm", filter=TRUE, transform="log2")

## Removing 0 low-count genes (6 remaining).

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

lp_norm <- normalize_expt(lp_two_strains, filter=TRUE, convert="rpkm",
                          norm="quant", transform="log2")

## Removing 151 low-count genes (8559 remaining).

## There appear to be 24 genes without a length.

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

zymo_heatmap_all <- plot_sample_heatmap(lp_norm)
zymo_heatmap_all

1.2 Compare to highly expressed, variant genes

I want to compare the above heatmap with one which is comprised of all genes with some ‘significantly high’ expression value and also a not-negligible coefficient of variance.

zymo_high_genes <- normalize_expt(lp_two_strains, filter = "cv", cv_min = 0.9)

## Removing 5564 low-count genes (3146 remaining).

high_strain_norm <- normalize_expt(zymo_high_genes, convert = "rpkm",
                                   norm = "quant", transform = "log2")

## There appear to be 105 genes without a length.

## transform_counts: Found 238 values equal to 0, adding 1 to the matrix.

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

I think this plot suggests that the difference between the two primary strains is not really one of a few specific genes, but instead a global pattern.

2 Zymodeme differential expression

2.1 No attempt at batch estimation

two_zymo <- set_expt_conditions(lp_two_strains, fact = "zymodemecategorical") %>%
  subset_expt(subset = "condition!='unknown'")

## The numbers of samples by condition are:

## 
## z21 z22 z23 z24 
##   7  43  41   2

## subset_expt(): There were 93, now there are 93 samples.

zymo_de_nobatch <- all_pairwise(two_zymo, filter = TRUE, model_batch = FALSE)

## 
## z21 z22 z23 z24 
##   7  43  41   2

zymo_table_nobatch <- combine_de_tables(
    zymo_de_nobatch, keepers = zymodeme_keeper,
    rda = glue("rda/zymo_tables_nobatch-v{ver}.rda"),
    excel = glue("excel/zymo_tables_nobatch-v{ver}.xlsx"))

## Deleting the file excel/zymo_tables_nobatch-v202305.xlsx before writing the tables.

## Adding venn plots for zymodeme.

## Saving de result as zymo_tables_nobatch-v202305 to rda/zymo_tables_nobatch-v202305.rda.

zymo_sig_nobatch <- extract_significant_genes(
    zymo_table_nobatch,
    according_to = "deseq", current_id = "GID", required_id = "GID",
    gmt = glue("gmt/zymodeme_nobatch-v{ver}.gmt"),
    excel = glue("excel/zymo_sig_nobatch_deseq-v{ver}.xlsx"))

## Deleting the file excel/zymo_sig_nobatch_deseq-v202305.xlsx before writing the tables.

## Number of up IDs in contrast zymodeme: 45.

## Number of down IDs in contrast zymodeme: 87.

2.1.1 Plot DE genes without batch estimation/adjustment

zymo_table_nobatch[["plots"]][["zymodeme"]][["deseq_ma_plots"]][["plot"]]

## NULL

zymo_table_nobatch[["plots"]][["zymodeme"]][["deseq_vol_plots"]][["plot"]]

## NULL

Log ratio, mean average plot and volcano plot of the comparison of the two primary zymodeme transcriptomes. When the transcriptomes of the two main strains (43 and 41 samples of z2.3 and z2.1) were compared without any attempt at batch/surrogate estimation with DESeq2, 45 and 85 genes were observed as significantly higher in strain z2.3 and z2.2 respectively using a cutoff of 1.0 logFC and 0.05 FDR adjusted p-value. There remain a large number of genes which are likely significantly different between the two strains, but fall below the 2-fold difference required for ‘significance.’ This follows prior observations that the parasite transcriptomes are constituitively expressed.

When the same data was plotted via a volcano plot, the relatively small range of fold changes compared to the large range of adjusted p-values is visible.

2.2 Attempt SVA estimate

zymo_de_sva <- all_pairwise(two_zymo, filter = TRUE, model_batch = "svaseq")

## 
## z21 z22 z23 z24 
##   7  43  41   2

## Removing 0 low-count genes (8559 remaining).

## Setting 495 low elements to zero.

## transform_counts: Found 495 values equal to 0, adding 1 to the matrix.

zymo_table_sva <- combine_de_tables(
    zymo_de_sva, keepers = zymodeme_keeper,
    rda = glue("rda/zymo_tables_sva-v{ver}.rda"),
    excel = glue("excel/zymo_tables_sva-v{ver}.xlsx"))

## Deleting the file excel/zymo_tables_sva-v202305.xlsx before writing the tables.

## Adding venn plots for zymodeme.

## Saving de result as zymo_tables_sva-v202305 to rda/zymo_tables_sva-v202305.rda.

zymo_sig_sva <- extract_significant_genes(
    zymo_table_sva,
    according_to = "deseq",
    current_id = "GID", required_id = "GID",
    gmt = glue("gmt/zymodeme_sva-v{ver}.gmt"),
    excel = glue("excel/zymo_sig_sva-v{ver}.xlsx"))

## Deleting the file excel/zymo_sig_sva-v202305.xlsx before writing the tables.

## Number of up IDs in contrast zymodeme: 46.

## Number of down IDs in contrast zymodeme: 86.

2.2.1 Plot zymodeme DE genes with sva batch estimation/adjustment

When estimates from SVA were included in the statistical model used by EdgeR, DESeq2, and limma; a nearly identical view of the data emerged. I think this shows with a high degree of confidence, that sva is not having a significant effect on this dataset.

zymo_table_sva[["plots"]][["zymodeme"]][["deseq_ma_plots"]][["plot"]]

## NULL

zymo_table_sva[["plots"]][["zymodeme"]][["deseq_vol_plots"]][["plot"]]

## NULL

3 Parasite Susceptibility to Drug (Current)

This susceptibility comparison is using the ‘current’ dataset.

sus_de_nobatch <- all_pairwise(lp_susceptibility, filter = TRUE, model_batch = FALSE)

## 
## resistant sensitive   unknown 
##        47        49         5

sus_de_nobatch

## A pairwise differential expression with results from: basic, deseq, edger, limma.

## This used a surrogate/batch estimate from: none.

## The primary analysis performed 6 comparisons.

sus_table_nobatch <- combine_de_tables(
    sus_de_nobatch, keepers = susceptibility_keepers,
    rda = glue("rda/sus_tables_nobatch-v{ver}.rda"),
    excel = glue("excel/sus_tables_nobatch-v{ver}.xlsx"))

## Deleting the file excel/sus_tables_nobatch-v202305.xlsx before writing the tables.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for resistant_ambiguous does not appear in the pairwise
## data.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for sensitive_ambiguous does not appear in the pairwise
## data.

## Adding venn plots for resistant_sensitive.

## Saving de result as sus_tables_nobatch-v202305 to rda/sus_tables_nobatch-v202305.rda.

sus_table_nobatch

## A set of combined differential expression results.

##                             table deseq_sigup deseq_sigdown edger_sigup edger_sigdown limma_sigup limma_sigdown
## 1 sensitive_vs_resistant-inverted          31            75          34            76          48            68

## `geom_line()`: Each group consists of only one observation.
## ℹ Do you need to adjust the group aesthetic?

sus_sig_nobatch <- extract_significant_genes(

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

## Deleting the file excel/sus_sig_nobatch-v202305.xlsx before writing the tables.

sus_de_sva <- all_pairwise(lp_susceptibility, filter = TRUE, model_batch = "svaseq")

## 
## resistant sensitive   unknown 
##        47        49         5

## Removing 0 low-count genes (8576 remaining).

## Setting 636 low elements to zero.

## transform_counts: Found 636 values equal to 0, adding 1 to the matrix.

sus_table_sva <- combine_de_tables(
    sus_de_sva, keepers = susceptibility_keepers,
    rda = glue("rda/sus_tables_sva-v{ver}.rda"),
    excel = glue("excel/sus_tables_sva-v{ver}.xlsx"))

## Deleting the file excel/sus_tables_sva-v202305.xlsx before writing the tables.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for resistant_ambiguous does not appear in the pairwise
## data.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for sensitive_ambiguous does not appear in the pairwise
## data.

## Adding venn plots for resistant_sensitive.

## Saving de result as sus_tables_sva-v202305 to rda/sus_tables_sva-v202305.rda.

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

## Deleting the file excel/sus_sig_sva-v202305.xlsx before writing the tables.

## To get a more true sense of sensitive vs resistant with sva, we kind of need to get rid of the
## unknown samples and perhaps the ambiguous.
no_ambiguous <- subset_expt(lp_susceptibility, subset="condition!='ambiguous'") %>%
  subset_expt(subset="condition!='unknown'")

## subset_expt(): There were 101, now there are 101 samples.

## subset_expt(): There were 101, now there are 96 samples.

no_ambiguous_de_sva <- all_pairwise(no_ambiguous, filter = TRUE, model_batch = "svaseq")

## 
## resistant sensitive 
##        47        49

## Removing 0 low-count genes (8565 remaining).

## Setting 423 low elements to zero.

## transform_counts: Found 423 values equal to 0, adding 1 to the matrix.

## Let us see if my keeper code will fail hard or soft with extra contrasts...
no_ambiguous_table_sva <- combine_de_tables(
    no_ambiguous_de_sva, keepers = susceptibility_keepers,
    excel = glue("excel/no_ambiguous_tables_sva-v{ver}.xlsx"))

## Deleting the file excel/no_ambiguous_tables_sva-v202305.xlsx before writing the tables.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for resistant_ambiguous does not appear in the pairwise
## data.

## Warning in extract_keepers(extracted, keepers, table_names, all_coefficients, : The table for sensitive_ambiguous does not appear in the pairwise
## data.

## Adding venn plots for resistant_sensitive.

no_ambiguous_sig_sva <- extract_significant_genes(
    no_ambiguous_table_sva, according_to = "deseq",
    excel = glue("excel/no_ambiguous_sig_sva-v{ver}.xlsx"))

## Deleting the file excel/no_ambiguous_sig_sva-v202305.xlsx before writing the tables.

3.0.1 Plot Susceptibility DE genes with sva batch estimation/adjustment

sus_table_nobatch[["plots"]][["resistant_sensitive"]][["deseq_ma_plots"]][["plot"]]

## NULL

sus_table_nobatch[["plots"]][["resistant_sensitive"]][["deseq_vol_plots"]][["plot"]]

## NULL

sus_table_sva[["plots"]][["resistant_sensitive"]][["deseq_ma_plots"]][["plot"]]

## NULL

sus_table_sva[["plots"]][["resistant_sensitive"]][["deseq_vol_plots"]][["plot"]]

## NULL

no_ambiguous_table_sva[["plots"]][["resistant_sensitive"]][["deseq_ma_plots"]][["plot"]]

## NULL

no_ambiguous_table_sva[["plots"]][["resistant_sensitive"]][["deseq_vol_plots"]][["plot"]]

## NULL

Given that resistance/sensitivity tends to be correlated with strain, one might expect similar results. One caveat in this context though: there are fewer strains with resistance/sensitivity definitions. This when the analysis was repeated without the ambiguous/unknown samples, a few more genes were observed as significant.

4 Comparing DE results from strain/sensitivity

## zymo_table_sva[["plots"]][["zymodeme"]][["deseq_ma_plots"]][["plot"]]

zy_df <- zymo_table_sva[["data"]][["z23_vs_z22"]]
sus_df <- sus_table_sva[["data"]][["sensitive_vs_resistant"]]

both_df <- merge(zy_df, sus_df, by = "row.names")
plot_df <- both_df[, c("deseq_logfc.x", "deseq_logfc.y")]

## Error in `[.data.frame`(both_df, , c("deseq_logfc.x", "deseq_logfc.y")): undefined columns selected

rownames(plot_df) <- both_df[["Row.names"]]

## Error in rownames(plot_df) <- both_df[["Row.names"]]: object 'plot_df' not found

colnames(plot_df) <- c("z23_vs_z22", "sensitive_vs_resistant")

## Error in colnames(plot_df) <- c("z23_vs_z22", "sensitive_vs_resistant"): object 'plot_df' not found

compare <- plot_linear_scatter(plot_df)

## Error in is.data.frame(x): object 'plot_df' not found

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

## Error in compare$scatter: object of type 'closure' is not subsettable

compare$cor

## Error in compare$cor: object of type 'closure' is not subsettable

5 Parasite Susceptibility to Drug (Historical)

This susceptibility comparison is using the historical dataset.

sushist_de_nobatch <- all_pairwise(lp_susceptibility_historical, filter = TRUE, model_batch = FALSE)

## 
## ambiguous resistant sensitive   unknown 
##         5        12        29        55

sushist_table_nobatch <- combine_de_tables(
    sushist_de_nobatch, keepers = susceptibility_keepers,
    excel = glue("excel/sushist_tables_nobatch-v{ver}.xlsx"))

## Deleting the file excel/sushist_tables_nobatch-v202305.xlsx before writing the tables.

## Adding venn plots for resistant_sensitive.

## Adding venn plots for resistant_ambiguous.

## Adding venn plots for sensitive_ambiguous.

sushist_sig_nobatch <- extract_significant_genes(
    sushist_table_nobatch,
    excel = glue("excel/sushist_sig_nobatch-v{ver}.xlsx"))

## Deleting the file excel/sushist_sig_nobatch-v202305.xlsx before writing the tables.

sushist_de_sva <- all_pairwise(lp_susceptibility_historical, filter = TRUE, model_batch = "svaseq")

## 
## ambiguous resistant sensitive   unknown 
##         5        12        29        55

## Removing 0 low-count genes (8576 remaining).

## Setting 374 low elements to zero.

## transform_counts: Found 374 values equal to 0, adding 1 to the matrix.

sushist_table_sva <- combine_de_tables(
    sushist_de_sva, keepers = susceptibility_keepers,
    excel = glue("excel/sushist_tables_sva-v{ver}.xlsx"))

## Deleting the file excel/sushist_tables_sva-v202305.xlsx before writing the tables.

## Adding venn plots for resistant_sensitive.

## Adding venn plots for resistant_ambiguous.

## Adding venn plots for sensitive_ambiguous.

sushist_sig_sva <- extract_significant_genes(
    sushist_table_sva, according_to = "deseq",
    excel = glue("excel/sushist_sig_sva-v{ver}.xlsx"))

## Deleting the file excel/sushist_sig_sva-v202305.xlsx before writing the tables.

6 Cure/Fail association

##cf_nb_input <- subset_expt(cf_expt, subset="condition!='unknown'")
cf_de_nobatch <- all_pairwise(lp_cf_known, filter = TRUE, model_batch = FALSE)

## 
## cure fail 
##   40   37

cf_table_nobatch <- combine_de_tables(cf_de_nobatch, excel = glue("excel/cf_tables_nobatch-v{ver}.xlsx"))

## Deleting the file excel/cf_tables_nobatch-v202305.xlsx before writing the tables.

## Adding venn plots for fail_vs_cure.

cf_sig_nobatch <- extract_significant_genes(cf_table_nobatch, excel = glue("excel/cf_sig_nobatch-v{ver}.xlsx"))

## Deleting the file excel/cf_sig_nobatch-v202305.xlsx before writing the tables.

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

## 
## cure fail 
##   40   37

## Removing 0 low-count genes (8548 remaining).

## Setting 118 low elements to zero.

## transform_counts: Found 118 values equal to 0, adding 1 to the matrix.

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

## Deleting the file excel/cf_tables-v202305.xlsx before writing the tables.

## Adding venn plots for fail_vs_cure.

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

## Deleting the file excel/cf_sig-v202305.xlsx before writing the tables.

6.1 Cure/Fail DE plots

It is not surprising that few or no genes are deemed significantly differentially expressed across samples which were taken from cure or fail patients.

cf_table_nobatch[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]

## NULL

dev <- pp(file = "images/cf_ma.png")
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]

## NULL

closed <- dev.off()
cf_table[["plots"]][["fail_vs_cure"]][["deseq_ma_plots"]][["plot"]]

## NULL

7 Combining the macrophage infected amastigotes with in-vitro promastigotes

One query we have not yet addressed: what are the similarities and differences among the strains used to infect the macrophage samples and the promastigote samples used in the TMRC2 parasite data?

## I just fixed this in the datasets Rmd, but until that propagates just set it manually
annotation(lp_expt) <- annotation(lp_macrophage)
tmrc2_macrophage_norm <- normalize_expt(lp_macrophage, transform="log2", convert="cpm",
                                        norm="quant", filter=TRUE)

## Removing 0 low-count genes (8710 remaining).

## transform_counts: Found 3735 values equal to 0, adding 1 to the matrix.

all_tmrc2 <- combine_expts(lp_expt, lp_macrophage)

## The numbers of samples by condition are:

## 
##   b2904 unknown    z1.0    z1.5    z2.0    z2.1    z2.2    z2.3    z2.4    z3.0    z3.2 
##       1       2       1       1       1       7      64      70       2       1       1

all_nosb <- all_tmrc2
pData(all_nosb)[["stage"]] <- "promastigote"
na_idx <- is.na(pData(all_nosb)[["macrophagetreatment"]])
pData(all_nosb)[na_idx, "macrophagetreatment"] <- "undefined"
all_nosb <- subset_expt(all_nosb, subset="macrophagetreatment!='inf_sb'")

## subset_expt(): There were 151, now there are 130 samples.

ama_idx <- pData(all_nosb)[["macrophagetreatment"]] == "inf"
pData(all_nosb)[ama_idx, "stage" ] <- "amastigote"
pData(all_nosb)[["batch"]] <- pData(all_nosb)[["stage"]]

I think the above picture is sort of the opposite of what we want to compare in a DE analysis for this set of data, e.g. we want to compare promastigotes from amastigotes?

all_nosb <- set_expt_batches(all_nosb, fact="condition") %>%
  set_expt_conditions(fact="stage")

## The number of samples by batch are:

## 
##   b2904 unknown    z1.0    z1.5    z2.0    z2.1    z2.2    z2.3    z2.4    z3.0    z3.2 
##       1       2       1       1       1       7      57      56       2       1       1

## The numbers of samples by condition are:

## 
##   amastigote promastigote 
##           29          101

two_zymo <- subset_expt(
  all_nosb,
  subset="zymodemecategorical=='z22'|zymodemecategorical=='z23'|zymodemecategorical=='unknown'")

## subset_expt(): There were 130, now there are 86 samples.

pro_ama <- all_pairwise(all_nosb, filter=TRUE, model_batch="svaseq")

## 
##   amastigote promastigote 
##           29          101

## Removing 0 low-count genes (8625 remaining).

## Setting 1811 low elements to zero.

## transform_counts: Found 1811 values equal to 0, adding 1 to the matrix.

pro_ama_table <- combine_de_tables(
    pro_ama,
    excel = glue("excel/tmrc2_pro_vs_ama_table-v{ver}.xlsx"))

## Adding venn plots for promastigote_vs_amastigote.

pro_ama_sig <- extract_significant_genes(
    pro_ama_table,
    excel = glue("excel/tmrc2_pro_vs_ama_sig-v{ver}.xlsx"))

7.0.1 Plot promastigote/amastigote DE genes

pro_ama_table[["plots"]][["promastigote_vs_amastigote"]][["deseq_ma_plots"]][["plot"]]

## NULL

I am a little surprised by this plot, I somewhat expected there to be few genes which passed the 2-fold difference demarcation line.

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 d4aecb6c9f50378b78f8d3c152430d455ccf17a9

## This is hpgltools commit: Tue Jul 11 17:12:15 2023 -0400: d4aecb6c9f50378b78f8d3c152430d455ccf17a9

## Saving to tmrc2_differential_expression_202305.rda.xz

tmp <- loadme(filename = savefile)