1 Introduction

Having established that the TMRC2 macrophage data looks robust and illustrative of a couple of interesting questions, let us perform a couple of differential analyses of it.

1.1 Human data

1.1.1 Primary queries

There is a series of initial questions which make some intuitive sense to me, but these do not necessarily match the set of questions which are intuitive for Olga. I am hoping to pull both of these sets of queries in one.

Before extracting these groups of queries, let us invoke the all_pairwise() function and get all of the likely contrasts along with one or more extras that might prove useful (the ‘extra’ argument).

extra <- "z23drugnodrug_vs_z22drugnodrug = (infsbz23 - infz23) - (infsbz22 - infz22)"
new_conditions <- paste0(pData(hs_macrophage)[["macrophagetreatment"]], "_",
                         pData(hs_macrophage)[["macrophagezymodeme"]])
hs_macrophage <- set_expt_conditions(hs_macrophage, fact = new_conditions)

hs_macrophage_de <- all_pairwise(hs_macrophage, model_batch="svaseq", filter=TRUE,
                                 extra_contrasts = extra)
## This DE analysis will perform all pairwise comparisons among:
## 
##   inf_sb_z2.2   inf_sb_z2.3      inf_z2.2      inf_z2.3    uninf_none 
##             6             6             6             6             2 
## uninf_sb_none 
##             2
## This analysis will include surrogate estimates from: svaseq.
## This will pre-filter the input data using normalize_expt's: TRUE argument.
## Removing 0 low-count genes (11460 remaining).
## Setting 757 low elements to zero.
## transform_counts: Found 757 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
## Used reverse contrast for deseq.
## Used reverse contrast for edger.
## Used reverse contrast for deseq.
## Used reverse contrast for basic.

1.1.1.1 Primary query contrasts

The final contrast in this list is interesting because it depends on the extra contrasts applied to the all_pairwise() above. In my way of thinking, the primary comparisons to consider are either cross-drug or cross-strain, but not both. However I think in at least a few instances Olga is interested in strain+drug / uninfected+nodrug.

tmrc2_human_keepers <- list(
    "z23nosb_vs_uninf" = c("infz23", "uninfnone"),
    "z22nosb_vs_uninf" = c("infz22", "uninfnone"),
    "z23nosb_vs_z22nosb" = c("infz23", "infz22"),
    "z23sb_vs_z22sb" = c("infsbz23", "infsbz22"),
    "z23sb_vs_z23nosb" = c("infsbz23", "infz23"),
    "z22sb_vs_z22nosb" = c("infsbz22", "infz22"),
    "z23sb_vs_sb" = c("infz23", "uninfsbnone"),
    "z22sb_vs_sb" = c("infz22", "uninfsbnone"),
    "z23sb_vs_uninf" = c("infsbz23", "uninfnone"),
    "z22sb_vs_uninf" = c("infsbz22", "uninfnone"),
    "sb_vs_uninf" = c("uninfsbnone", "uninfnone"),
    "extra" = c("z23drugnodrug", "z22drugnodrug"))

1.1.1.2 Write contrast results

Now let us write out the xlsx file containing the above contrasts.

hs_macrophage_table <- combine_de_tables(
    hs_macrophage_de,
    keepers = tmrc2_human_keepers,
    excel=glue::glue("excel/macrophage_human_table-v{ver}.xlsx"))
## Deleting the file excel/macrophage_human_table-v202209.xlsx before writing the tables.
## Warning in combine_single_de_table(li = limma, ed = edger, eb = ebseq, de =
## deseq, : The deseq table seems to be missing.
## Warning in combine_single_de_table(li = limma, ed = edger, eb = ebseq, de =
## deseq, : The basic table seems to be missing.
## Unable to find the table in the set of possible tables.
## The possible tables are: infsbz23_vs_infsbz22, infz22_vs_infsbz22, infz23_vs_infsbz22, uninfnone_vs_infsbz22, uninfsbnone_vs_infsbz22, infz22_vs_infsbz23, infz23_vs_infsbz23, uninfnone_vs_infsbz23, uninfsbnone_vs_infsbz23, infz23_vs_infz22, uninfnone_vs_infz22, uninfsbnone_vs_infz22, uninfnone_vs_infz23, uninfsbnone_vs_infz23, uninfsbnone_vs_uninfnone
hs_macrophage_sig <- extract_significant_genes(
    hs_macrophage_table,
    excel=glue::glue("excel/macrophage_human_sig-v{ver}.xlsx"))
## Deleting the file excel/macrophage_human_sig-v202209.xlsx before writing the tables.
## Unable to find the table in the set of possible tables.
## The possible tables are: infsbz23_vs_infsbz22, infz22_vs_infsbz22, infz23_vs_infsbz22, uninfnone_vs_infsbz22, uninfsbnone_vs_infsbz22, infz22_vs_infsbz23, infz23_vs_infsbz23, uninfnone_vs_infsbz23, uninfsbnone_vs_infsbz23, infz23_vs_infz22, uninfnone_vs_infz22, uninfsbnone_vs_infz22, uninfnone_vs_infz23, uninfsbnone_vs_infz23, uninfsbnone_vs_uninfnone
## Unable to find the table in the set of possible tables.
## The possible tables are: infsbz23_vs_infsbz22, infz22_vs_infsbz22, infz23_vs_infsbz22, uninfnone_vs_infsbz22, uninfsbnone_vs_infsbz22, infz22_vs_infsbz23, infz23_vs_infsbz23, uninfnone_vs_infsbz23, uninfsbnone_vs_infsbz23, infz23_vs_infz22, uninfnone_vs_infz22, uninfsbnone_vs_infz22, uninfnone_vs_infz23, uninfsbnone_vs_infz23, uninfsbnone_vs_uninfnone

1.1.1.3 Plot contrasts of interest

One suggestion I received recently was to set the axes for these volcano plots to be static rather than let ggplot choose its own. I am assuming this is only relevant for pairs of contrasts, but that might not be true.

z23nosb_vs_uninf_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23nosb_vs_uninf"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## The color list must have 4, setting it to the default.
z23nosb_vs_uninf_volcano$plot

plotly::ggplotly(z23nosb_vs_uninf_volcano$plot)
## Warning: `gather_()` was deprecated in tidyr 1.2.0.
## Please use `gather()` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was generated.
## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues

## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
z22nosb_vs_uninf_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z22nosb_vs_uninf"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## The color list must have 4, setting it to the default.
z22nosb_vs_uninf_volcano$plot
## Warning: ggrepel: 14 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

plotly::ggplotly(z22nosb_vs_uninf_volcano$plot)
## Warning in geom2trace.default(dots[[1L]][[3L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
## Warning in geom2trace.default(dots[[1L]][[3L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues

## Warning in geom2trace.default(dots[[1L]][[3L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
hs_macrophage_table[["plots"]][["z23nosb_vs_uninf"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 25) +
  ylim(0, 40)
## Warning: Removed 1 rows containing missing values (geom_point).

hs_macrophage_table[["plots"]][["z22nosb_vs_uninf"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 25) +
  ylim(0, 40)

z23nosb_vs_z22nosb_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23nosb_vs_z22nosb"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## The color list must have 4, setting it to the default.
z23nosb_vs_z22nosb_volcano$plot
## Warning: ggrepel: 1 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

plotly::ggplotly(z23nosb_vs_z22nosb_volcano$plot)
## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
z23sb_vs_z22sb_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23sb_vs_z22sb"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
## The color list must have 4, setting it to the default.
z23sb_vs_z22sb_volcano$plot
## Warning: ggrepel: 5 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

plotly::ggplotly(z23sb_vs_z22sb_volcano$plot)
## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues

## Warning in geom2trace.default(dots[[1L]][[2L]], dots[[2L]][[1L]], dots[[3L]][[1L]]): geom_GeomTextRepel() has yet to be implemented in plotly.
##   If you'd like to see this geom implemented,
##   Please open an issue with your example code at
##   https://github.com/ropensci/plotly/issues
hs_macrophage_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)

hs_macrophage_table[["plots"]][["z23sb_vs_z22sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)

shared <- Vennerable::Venn(list("drug" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23sb_vs_z22sb"]]),
                                "nodrug" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23nosb_vs_z22nosb"]])))
pp(file="images/drug_nodrug_venn_up.png")
Vennerable::plot(shared)
dev.off()
## png 
##   2
Vennerable::plot(shared)

shared <- Vennerable::Venn(list("drug" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23sb_vs_z22sb"]]),
                                "nodrug" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23nosb_vs_z22nosb"]])))
pp(file="images/drug_nodrug_venn_down.png")
Vennerable::plot(shared)
dev.off()
## png 
##   2
hs_macrophage_table[["plots"]][["z23sb_vs_z23nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)

hs_macrophage_table[["plots"]][["z22sb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)

hs_macrophage_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 28) +
  ylim(0, 140)

hs_macrophage_table[["plots"]][["z22sb_vs_sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 28) +
  ylim(0, 140)

shared <- Vennerable::Venn(list("z23" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23sb_vs_sb"]]),
                                "z22" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z22sb_vs_sb"]])))
pp(file="images/z23_z22_drug_venn_up.png")
Vennerable::plot(shared)
dev.off()
## png 
##   2
Vennerable::plot(shared)

shared <- Vennerable::Venn(list("z23" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23sb_vs_sb"]]),
                                "z22" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z22sb_vs_sb"]])))
pp(file="images/z23_z22_drug_venn_down.png")
Vennerable::plot(shared)
dev.off()
## png 
##   2
Vennerable::plot(shared)

1.2 Parasite

lp_macrophage_de <- all_pairwise(lp_macrophage,
                                 model_batch="svaseq", filter=TRUE)
## This DE analysis will perform all pairwise comparisons among:
## 
## z2.2 z2.3 
##    5    6
## This analysis will include surrogate estimates from: svaseq.
## This will pre-filter the input data using normalize_expt's: TRUE argument.
## Removing 0 low-count genes (8522 remaining).
## Setting 45 low elements to zero.
## transform_counts: Found 45 values equal to 0, adding 1 to the matrix.
## Finished running DE analyses, collecting outputs.
## Comparing analyses.
tmrc2_parasite_keepers <- list(
    "z23nosb_vs_z22nosb" = c("z23", "z22"))
lp_macrophage_table <- combine_de_tables(
  lp_macrophage_de, keepers = tmrc2_parasite_keepers,
  excel=glue::glue("excel/macrophage_parasite_infection_de-v{ver}.xlsx"))
## Deleting the file excel/macrophage_parasite_infection_de-v202209.xlsx before writing the tables.
lp_macrophage_sig <- extract_significant_genes(
    lp_macrophage_table,
    excel=glue::glue("excel/macrophage_parasite_sig-v{ver}.xlsx"))
## Deleting the file excel/macrophage_parasite_sig-v202209.xlsx before writing the tables.
pp(file="images/lp_macrophage_z23_z22.png", image=lp_macrophage_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]])
## Warning in pp(file = "images/lp_macrophage_z23_z22.png", image =
## lp_macrophage_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]]
## [["plot"]]): There is no device to shut down.
up_genes <- lp_macrophage_sig[["deseq"]][["ups"]][[1]]
dim(up_genes)
## [1] 40 58
down_genes <- lp_macrophage_sig[["deseq"]][["downs"]][[1]]
dim(down_genes)
## [1] 75 58

2 Over representation searches

all_gp <- all_gprofiler(hs_macrophage_sig)
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 670 against hsapiens.
## GO search found 691 hits.
## Performing gProfiler KEGG search of 670 against hsapiens.
## KEGG search found 11 hits.
## Performing gProfiler REAC search of 670 against hsapiens.
## REAC search found 12 hits.
## Performing gProfiler WP search of 670 against hsapiens.
## WP search found 12 hits.
## Performing gProfiler TF search of 670 against hsapiens.
## TF search found 111 hits.
## Performing gProfiler MIRNA search of 670 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 670 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 670 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 670 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 386 against hsapiens.
## GO search found 61 hits.
## Performing gProfiler KEGG search of 386 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 386 against hsapiens.
## REAC search found 1 hits.
## Performing gProfiler WP search of 386 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 386 against hsapiens.
## TF search found 30 hits.
## Performing gProfiler MIRNA search of 386 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 386 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 386 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 386 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 435 against hsapiens.
## GO search found 189 hits.
## Performing gProfiler KEGG search of 435 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 435 against hsapiens.
## REAC search found 4 hits.
## Performing gProfiler WP search of 435 against hsapiens.
## WP search found 2 hits.
## Performing gProfiler TF search of 435 against hsapiens.
## TF search found 62 hits.
## Performing gProfiler MIRNA search of 435 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 435 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 435 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 435 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 7 against hsapiens.
## GO search found 3 hits.
## Performing gProfiler KEGG search of 7 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 7 against hsapiens.
## REAC search found 0 hits.
## Performing gProfiler WP search of 7 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 7 against hsapiens.
## TF search found 0 hits.
## Performing gProfiler MIRNA search of 7 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 7 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 7 against hsapiens.
## CORUM search found 2 hits.
## Performing gProfiler HP search of 7 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 529 against hsapiens.
## GO search found 557 hits.
## Performing gProfiler KEGG search of 529 against hsapiens.
## KEGG search found 8 hits.
## Performing gProfiler REAC search of 529 against hsapiens.
## REAC search found 13 hits.
## Performing gProfiler WP search of 529 against hsapiens.
## WP search found 12 hits.
## Performing gProfiler TF search of 529 against hsapiens.
## TF search found 109 hits.
## Performing gProfiler MIRNA search of 529 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 529 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 529 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 529 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 730 against hsapiens.
## GO search found 225 hits.
## Performing gProfiler KEGG search of 730 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 730 against hsapiens.
## REAC search found 1 hits.
## Performing gProfiler WP search of 730 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 730 against hsapiens.
## TF search found 63 hits.
## Performing gProfiler MIRNA search of 730 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 730 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 730 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 730 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 484 against hsapiens.
## GO search found 624 hits.
## Performing gProfiler KEGG search of 484 against hsapiens.
## KEGG search found 11 hits.
## Performing gProfiler REAC search of 484 against hsapiens.
## REAC search found 13 hits.
## Performing gProfiler WP search of 484 against hsapiens.
## WP search found 17 hits.
## Performing gProfiler TF search of 484 against hsapiens.
## TF search found 104 hits.
## Performing gProfiler MIRNA search of 484 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 484 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 484 against hsapiens.
## CORUM search found 2 hits.
## Performing gProfiler HP search of 484 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 422 against hsapiens.
## GO search found 60 hits.
## Performing gProfiler KEGG search of 422 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 422 against hsapiens.
## REAC search found 1 hits.
## Performing gProfiler WP search of 422 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 422 against hsapiens.
## TF search found 3 hits.
## Performing gProfiler MIRNA search of 422 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 422 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 422 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 422 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 555 against hsapiens.
## GO search found 141 hits.
## Performing gProfiler KEGG search of 555 against hsapiens.
## KEGG search found 4 hits.
## Performing gProfiler REAC search of 555 against hsapiens.
## REAC search found 85 hits.
## Performing gProfiler WP search of 555 against hsapiens.
## WP search found 4 hits.
## Performing gProfiler TF search of 555 against hsapiens.
## TF search found 25 hits.
## Performing gProfiler MIRNA search of 555 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 555 against hsapiens.
## HPA search found 2 hits.
## Performing gProfiler CORUM search of 555 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 555 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 780 against hsapiens.
## GO search found 743 hits.
## Performing gProfiler KEGG search of 780 against hsapiens.
## KEGG search found 6 hits.
## Performing gProfiler REAC search of 780 against hsapiens.
## REAC search found 12 hits.
## Performing gProfiler WP search of 780 against hsapiens.
## WP search found 1 hits.
## Performing gProfiler TF search of 780 against hsapiens.
## TF search found 201 hits.
## Performing gProfiler MIRNA search of 780 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 780 against hsapiens.
## HPA search found 5 hits.
## Performing gProfiler CORUM search of 780 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 780 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 454 against hsapiens.
## GO search found 118 hits.
## Performing gProfiler KEGG search of 454 against hsapiens.
## KEGG search found 5 hits.
## Performing gProfiler REAC search of 454 against hsapiens.
## REAC search found 88 hits.
## Performing gProfiler WP search of 454 against hsapiens.
## WP search found 8 hits.
## Performing gProfiler TF search of 454 against hsapiens.
## TF search found 30 hits.
## Performing gProfiler MIRNA search of 454 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 454 against hsapiens.
## HPA search found 15 hits.
## Performing gProfiler CORUM search of 454 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 454 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 986 against hsapiens.
## GO search found 835 hits.
## Performing gProfiler KEGG search of 986 against hsapiens.
## KEGG search found 1 hits.
## Performing gProfiler REAC search of 986 against hsapiens.
## REAC search found 8 hits.
## Performing gProfiler WP search of 986 against hsapiens.
## WP search found 2 hits.
## Performing gProfiler TF search of 986 against hsapiens.
## TF search found 258 hits.
## Performing gProfiler MIRNA search of 986 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 986 against hsapiens.
## HPA search found 2 hits.
## Performing gProfiler CORUM search of 986 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 986 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 1081 against hsapiens.
## GO search found 1038 hits.
## Performing gProfiler KEGG search of 1081 against hsapiens.
## KEGG search found 15 hits.
## Performing gProfiler REAC search of 1081 against hsapiens.
## REAC search found 17 hits.
## Performing gProfiler WP search of 1081 against hsapiens.
## WP search found 12 hits.
## Performing gProfiler TF search of 1081 against hsapiens.
## TF search found 166 hits.
## Performing gProfiler MIRNA search of 1081 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 1081 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 1081 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 1081 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 701 against hsapiens.
## GO search found 84 hits.
## Performing gProfiler KEGG search of 701 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 701 against hsapiens.
## REAC search found 1 hits.
## Performing gProfiler WP search of 701 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 701 against hsapiens.
## TF search found 56 hits.
## Performing gProfiler MIRNA search of 701 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 701 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 701 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 701 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 1025 against hsapiens.
## GO search found 659 hits.
## Performing gProfiler KEGG search of 1025 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 1025 against hsapiens.
## REAC search found 4 hits.
## Performing gProfiler WP search of 1025 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 1025 against hsapiens.
## TF search found 188 hits.
## Performing gProfiler MIRNA search of 1025 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 1025 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 1025 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 1025 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 427 against hsapiens.
## GO search found 65 hits.
## Performing gProfiler KEGG search of 427 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 427 against hsapiens.
## REAC search found 56 hits.
## Performing gProfiler WP search of 427 against hsapiens.
## WP search found 2 hits.
## Performing gProfiler TF search of 427 against hsapiens.
## TF search found 31 hits.
## Performing gProfiler MIRNA search of 427 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 427 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 427 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 427 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 910 against hsapiens.
## GO search found 351 hits.
## Performing gProfiler KEGG search of 910 against hsapiens.
## KEGG search found 4 hits.
## Performing gProfiler REAC search of 910 against hsapiens.
## REAC search found 87 hits.
## Performing gProfiler WP search of 910 against hsapiens.
## WP search found 7 hits.
## Performing gProfiler TF search of 910 against hsapiens.
## TF search found 86 hits.
## Performing gProfiler MIRNA search of 910 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 910 against hsapiens.
## HPA search found 1 hits.
## Performing gProfiler CORUM search of 910 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 910 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 983 against hsapiens.
## GO search found 392 hits.
## Performing gProfiler KEGG search of 983 against hsapiens.
## KEGG search found 0 hits.
## Performing gProfiler REAC search of 983 against hsapiens.
## REAC search found 0 hits.
## Performing gProfiler WP search of 983 against hsapiens.
## WP search found 1 hits.
## Performing gProfiler TF search of 983 against hsapiens.
## TF search found 217 hits.
## Performing gProfiler MIRNA search of 983 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 983 against hsapiens.
## HPA search found 7 hits.
## Performing gProfiler CORUM search of 983 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 983 against hsapiens.
## HP search found 5 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 572 against hsapiens.
## GO search found 160 hits.
## Performing gProfiler KEGG search of 572 against hsapiens.
## KEGG search found 4 hits.
## Performing gProfiler REAC search of 572 against hsapiens.
## REAC search found 83 hits.
## Performing gProfiler WP search of 572 against hsapiens.
## WP search found 7 hits.
## Performing gProfiler TF search of 572 against hsapiens.
## TF search found 21 hits.
## Performing gProfiler MIRNA search of 572 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 572 against hsapiens.
## HPA search found 1 hits.
## Performing gProfiler CORUM search of 572 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 572 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 644 against hsapiens.
## GO search found 586 hits.
## Performing gProfiler KEGG search of 644 against hsapiens.
## KEGG search found 8 hits.
## Performing gProfiler REAC search of 644 against hsapiens.
## REAC search found 5 hits.
## Performing gProfiler WP search of 644 against hsapiens.
## WP search found 0 hits.
## Performing gProfiler TF search of 644 against hsapiens.
## TF search found 152 hits.
## Performing gProfiler MIRNA search of 644 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 644 against hsapiens.
## HPA search found 11 hits.
## Performing gProfiler CORUM search of 644 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 644 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 362 against hsapiens.
## GO search found 177 hits.
## Performing gProfiler KEGG search of 362 against hsapiens.
## KEGG search found 4 hits.
## Performing gProfiler REAC search of 362 against hsapiens.
## REAC search found 84 hits.
## Performing gProfiler WP search of 362 against hsapiens.
## WP search found 9 hits.
## Performing gProfiler TF search of 362 against hsapiens.
## TF search found 24 hits.
## Performing gProfiler MIRNA search of 362 against hsapiens.
## MIRNA search found 0 hits.
## Performing gProfiler HPA search of 362 against hsapiens.
## HPA search found 1 hits.
## Performing gProfiler CORUM search of 362 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 362 against hsapiens.
## HP search found 0 hits.
## Redirecting to simple_gprofiler2().
## If you wish to roll the bones with the previous function, try:
## simple_gprofiler_old().
## Performing gProfiler GO search of 503 against hsapiens.
## GO search found 449 hits.
## Performing gProfiler KEGG search of 503 against hsapiens.
## KEGG search found 4 hits.
## Performing gProfiler REAC search of 503 against hsapiens.
## REAC search found 3 hits.
## Performing gProfiler WP search of 503 against hsapiens.
## WP search found 1 hits.
## Performing gProfiler TF search of 503 against hsapiens.
## TF search found 30 hits.
## Performing gProfiler MIRNA search of 503 against hsapiens.
## MIRNA search found 3 hits.
## Performing gProfiler HPA search of 503 against hsapiens.
## HPA search found 6 hits.
## Performing gProfiler CORUM search of 503 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 503 against hsapiens.
## HP search found 0 hits.
tt <- simple_gprofiler2(hs_macrophage_sig$deseq$ups[[1]])
## Performing gProfiler GO search of 670 against hsapiens.
## GO search found 691 hits.
## Performing gProfiler KEGG search of 670 against hsapiens.
## KEGG search found 11 hits.
## Performing gProfiler REAC search of 670 against hsapiens.
## REAC search found 12 hits.
## Performing gProfiler WP search of 670 against hsapiens.
## WP search found 12 hits.
## Performing gProfiler TF search of 670 against hsapiens.
## TF search found 111 hits.
## Performing gProfiler MIRNA search of 670 against hsapiens.
## MIRNA search found 1 hits.
## Performing gProfiler HPA search of 670 against hsapiens.
## HPA search found 0 hits.
## Performing gProfiler CORUM search of 670 against hsapiens.
## CORUM search found 0 hits.
## Performing gProfiler HP search of 670 against hsapiens.
## HP search found 0 hits.
pp(file="images/z23_uninf_reactome_up.png", image=all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["reactome_plot_over"]], height=12, width=9)
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["kegg_plot_over"]]
## NULL
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["mfp_plot_over"]]
## NULL
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["tf_plot_over"]]
## NULL
pp(file="images/z22_uninf_reactome_up.png", image=all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["reactome_plot_over"]], height=12, width=9)
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["kegg_plot_over"]]
## NULL
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["mfp_plot_over"]]
## NULL
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["tf_plot_over"]]
## NULL
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
## NULL
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
## NULL
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
## NULL
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["tf_plot_over"]]
## NULL
all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["reactome_plot_over"]]
## NULL
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
## NULL
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
## NULL
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
## NULL
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["tf_plot_over"]]
## NULL
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
## NULL
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
## NULL
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
## NULL
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["tf_plot_over"]]
## NULL
up_goseq <- simple_goseq(up_genes, go_db=lp_go, length_db=lp_lengths)
## Found 11 go_db genes and 40 length_db genes out of 40.
## Testing that go categories are defined.
## Removing undefined categories.
## Gathering synonyms.
## Gathering category definitions.
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## View categories over represented in the 2.3 samples
up_goseq$pvalue_plots$bpp_plot_over

down_goseq <- simple_goseq(down_genes, go_db=lp_go, length_db=lp_lengths)
## Found 21 go_db genes and 75 length_db genes out of 75.
## Testing that go categories are defined.
## Removing undefined categories.
## Gathering synonyms.
## Gathering category definitions.
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## The score column is null, defaulting to score.
## Possible columns are:
## [1] "category"                 "over_represented_pvalue" 
## [3] "under_represented_pvalue" "numDEInCat"              
## [5] "numInCat"                 "term"                    
## [7] "ontology"                 "qvalue"
## View categories over represented in the 2.2 samples
down_goseq$pvalue_plots$bpp_plot_over

3 GSVA

hs_infected <- subset_expt(hs_macrophage, subset="macrophagetreatment!='uninf'") %>%
  subset_expt(subset="macrophagetreatment!='uninf_sb'")
## subset_expt(): There were 28, now there are 26 samples.
## subset_expt(): There were 26, now there are 24 samples.
hs_gsva_c2 <- simple_gsva(hs_infected)
## Converting the rownames() of the expressionset to ENTREZID.
## 1622 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.
## Before conversion, the expressionset has 21481 entries.
## After conversion, the expressionset has 20013 entries.
hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")
## Converting the rownames() of the expressionset to ENTREZID.
## 1622 ENSEMBL ID's didn't have a matching ENTEREZ ID. Dropping them now.
## Before conversion, the expressionset has 21481 entries.
## After conversion, the expressionset has 20013 entries.
hs_gsva_c2_sig <- get_sig_gsva_categories(hs_gsva_c2, excel = "excel/hs_macrophage_gsva_c2_sig.xlsx")
## Starting limma pairwise comparison.
## libsize was not specified, this parameter has profound effects on limma's result.
## Using the libsize from expt$libsize.
## Limma step 1/6: choosing model.
## Choosing the non-intercept containing model.
## Assuming this data is similar to a micro array and not performign voom.
## Limma step 3/6: running lmFit with method: ls.
## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)
## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.
## Limma step 6/6: Writing limma outputs.
## Limma step 6/6: 1/6: Creating table: infsbz23_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 2/6: Creating table: infz22_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 3/6: Creating table: infz23_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 4/6: Creating table: infz22_vs_infsbz23.  Adjust = BH
## Limma step 6/6: 5/6: Creating table: infz23_vs_infsbz23.  Adjust = BH
## Limma step 6/6: 6/6: Creating table: infz23_vs_infz22.  Adjust = BH
## Limma step 6/6: 1/4: Creating table: infsbz22.  Adjust = BH
## Limma step 6/6: 2/4: Creating table: infsbz23.  Adjust = BH
## Limma step 6/6: 3/4: Creating table: infz22.  Adjust = BH
## Limma step 6/6: 4/4: Creating table: infz23.  Adjust = BH
## The factor inf_sb_z2.2 has 6 rows.
## The factor inf_sb_z2.3 has 6 rows.
## The factor inf_z2.2 has 6 rows.
## The factor inf_z2.3 has 6 rows.
## Testing each factor against the others.
## Scoring inf_sb_z2.2 against everything else.
## Scoring inf_sb_z2.3 against everything else.
## Scoring inf_z2.2 against everything else.
## Scoring inf_z2.3 against everything else.
## Deleting the file excel/hs_macrophage_gsva_c2_sig.xlsx before writing the tables.
hs_gsva_c2_sig$raw_plot

hs_gsva_c7_sig <- get_sig_gsva_categories(hs_gsva_c7, excel = "excel/hs_macrophage_gsva_c7_sig.xlsx")
## Starting limma pairwise comparison.
## libsize was not specified, this parameter has profound effects on limma's result.
## Using the libsize from expt$libsize.
## Limma step 1/6: choosing model.
## Choosing the non-intercept containing model.
## Assuming this data is similar to a micro array and not performign voom.
## Limma step 3/6: running lmFit with method: ls.
## Limma step 4/6: making and fitting contrasts with no intercept. (~ 0 + factors)
## Limma step 5/6: Running eBayes with robust = FALSE and trend = FALSE.
## Limma step 6/6: Writing limma outputs.
## Limma step 6/6: 1/6: Creating table: infsbz23_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 2/6: Creating table: infz22_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 3/6: Creating table: infz23_vs_infsbz22.  Adjust = BH
## Limma step 6/6: 4/6: Creating table: infz22_vs_infsbz23.  Adjust = BH
## Limma step 6/6: 5/6: Creating table: infz23_vs_infsbz23.  Adjust = BH
## Limma step 6/6: 6/6: Creating table: infz23_vs_infz22.  Adjust = BH
## Limma step 6/6: 1/4: Creating table: infsbz22.  Adjust = BH
## Limma step 6/6: 2/4: Creating table: infsbz23.  Adjust = BH
## Limma step 6/6: 3/4: Creating table: infz22.  Adjust = BH
## Limma step 6/6: 4/4: Creating table: infz23.  Adjust = BH
## The factor inf_sb_z2.2 has 6 rows.
## The factor inf_sb_z2.3 has 6 rows.
## The factor inf_z2.2 has 6 rows.
## The factor inf_z2.3 has 6 rows.
## Testing each factor against the others.
## Scoring inf_sb_z2.2 against everything else.
## Scoring inf_sb_z2.3 against everything else.
## Scoring inf_z2.2 against everything else.
## Scoring inf_z2.3 against everything else.
## Deleting the file excel/hs_macrophage_gsva_c7_sig.xlsx before writing the tables.
hs_gsva_c7_sig$raw_plot

4 Try out a new tool

Two reasons: Najib loves him some PCA, this uses wikipathways, which is something I think is neat.

Ok, I spent some time looking through the code and I have some problems with some of the design decisions.

Most importantly, it requires a data.frame() which has the following format:

  1. No rownames, instead column #1 is the sample ID.
  2. Columns 2-m are the categorical/survival/etc metrics.
  3. Columns m-n are 1 gene-per-column with log2 values.

But when I think about it I think I get the idea, they want to be able to do modelling stuff more easily with response factors.

library(pathwayPCA)
library(rWikiPathways)

downloaded <- downloadPathwayArchive(organism = "Homo sapiens", format = "gmt")
data_path <- system.file("extdata", package="pathwayPCA")
wikipathways <- read_gmt(paste0(data_path, "/wikipathways_human_symbol.gmt"), description=TRUE)

expt <- subset_expt(hs_macrophage, subset="macrophagetreatment!='uninf'") %>%
  subset_expt(subset="macrophagetreatment!='uninf_sb'")
expt <- set_expt_conditions(expt, fact="macrophagezymodeme")

symbol_vector <- fData(expt)[[symbol_column]]
names(symbol_vector) <- rownames(fData(expt))
symbol_df <- as.data.frame(symbol_vector)

assay_df <- merge(symbol_df, as.data.frame(exprs(expt)), by = "row.names")
assay_df[["Row.names"]] <- NULL
rownames(assay_df) <- make.names(assay_df[["symbol_vector"]], unique = TRUE)
assay_df[["symbol_vector"]] <- NULL
assay_df <- as.data.frame(t(assay_df))
assay_df[["SampleID"]] <- rownames(assay_df)
assay_df <- dplyr::select(assay_df, "SampleID", everything())

factor_df <- as.data.frame(pData(expt))
factor_df[["SampleID"]] <- rownames(factor_df)
factor_df <- dplyr::select(factor_df, "SampleID", everything())
factor_df <- factor_df[, c("SampleID", factors)]

tt <- CreateOmics(
    assayData_df = assay_df,
    pathwayCollection_ls = wikipathways,
    response = factor_df,
    respType = "categorical",
    minPathSize=5)

super <- AESPCA_pVals(
    object = tt,
    numPCs = 2,
    parallel = FALSE,
    numCores = 8,
    numReps = 2,
    adjustment = "BH")
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 ac8dd60d02c958ad58bae9b99aceddf9788d4ff1
## This is hpgltools commit: Mon Sep 26 15:57:54 2022 -0400: ac8dd60d02c958ad58bae9b99aceddf9788d4ff1
## Saving to tmrc2_macrophage_visualization_202209.rda.xz
tmp <- loadme(filename = savefile)
---
title: "L. panamensis 202209: TMRC2 Macrophage Differential Expression."
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(ggplot2)
library(Heatplus)
library(hpgltools)
tt <- 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 <- "202209"
previous_file <- ""
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_macrophage_visualization_{ver}.Rmd")
loaded <- load(file=glue::glue("rda/tmrc2_data_structures-v{ver}.rda"))
savefile <- gsub(pattern = "\\.Rmd", replace = "\\.rda\\.xz", x = rmd_file)
```

# Introduction

Having established that the TMRC2 macrophage data looks robust and
illustrative of a couple of interesting questions, let us perform a
couple of differential analyses of it.

## Human data

### Primary queries

There is a series of initial questions which make some intuitive sense
to me, but these do not necessarily match the set of questions which
are intuitive for Olga.  I am hoping to pull both of these sets of
queries in one.

Before extracting these groups of queries, let us invoke the
all_pairwise() function and get all of the likely contrasts along with
one or more extras that might prove useful (the 'extra' argument).

```{r hs_de}
extra <- "z23drugnodrug_vs_z22drugnodrug = (infsbz23 - infz23) - (infsbz22 - infz22)"
new_conditions <- paste0(pData(hs_macrophage)[["macrophagetreatment"]], "_",
                         pData(hs_macrophage)[["macrophagezymodeme"]])
hs_macrophage <- set_expt_conditions(hs_macrophage, fact = new_conditions)

hs_macrophage_de <- all_pairwise(hs_macrophage, model_batch="svaseq", filter=TRUE,
                                 extra_contrasts = extra)
```

#### Primary query contrasts

The final contrast in this list is interesting because it depends on
the extra contrasts applied to the all_pairwise() above.  In my way of
thinking, the primary comparisons to consider are either cross-drug or
cross-strain, but not both.  However I think in at least a few
instances Olga is interested in strain+drug / uninfected+nodrug.

```{r tumrc2_human_keepers}
tmrc2_human_keepers <- list(
    "z23nosb_vs_uninf" = c("infz23", "uninfnone"),
    "z22nosb_vs_uninf" = c("infz22", "uninfnone"),
    "z23nosb_vs_z22nosb" = c("infz23", "infz22"),
    "z23sb_vs_z22sb" = c("infsbz23", "infsbz22"),
    "z23sb_vs_z23nosb" = c("infsbz23", "infz23"),
    "z22sb_vs_z22nosb" = c("infsbz22", "infz22"),
    "z23sb_vs_sb" = c("infz23", "uninfsbnone"),
    "z22sb_vs_sb" = c("infz22", "uninfsbnone"),
    "z23sb_vs_uninf" = c("infsbz23", "uninfnone"),
    "z22sb_vs_uninf" = c("infsbz22", "uninfnone"),
    "sb_vs_uninf" = c("uninfsbnone", "uninfnone"),
    "extra" = c("z23drugnodrug", "z22drugnodrug"))
```

#### Write contrast results

Now let us write out the xlsx file containing the above contrasts.

```{r make_tables_tmrc2}
hs_macrophage_table <- combine_de_tables(
    hs_macrophage_de,
    keepers = tmrc2_human_keepers,
    excel=glue::glue("excel/macrophage_human_table-v{ver}.xlsx"))
hs_macrophage_sig <- extract_significant_genes(
    hs_macrophage_table,
    excel=glue::glue("excel/macrophage_human_sig-v{ver}.xlsx"))
```

#### Plot contrasts of interest

One suggestion I received recently was to set the axes for these
volcano plots to be static rather than let ggplot choose its own.  I
am assuming this is only relevant for pairs of contrasts, but that
might not be true.

```{r hs_macrophage_sig_genes_2322vsuninf}
z23nosb_vs_uninf_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23nosb_vs_uninf"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
z23nosb_vs_uninf_volcano$plot
plotly::ggplotly(z23nosb_vs_uninf_volcano$plot)
z22nosb_vs_uninf_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z22nosb_vs_uninf"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
z22nosb_vs_uninf_volcano$plot
plotly::ggplotly(z22nosb_vs_uninf_volcano$plot)

hs_macrophage_table[["plots"]][["z23nosb_vs_uninf"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 25) +
  ylim(0, 40)
hs_macrophage_table[["plots"]][["z22nosb_vs_uninf"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 25) +
  ylim(0, 40)
```

```{r z22z23_comparison_plots}
z23nosb_vs_z22nosb_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23nosb_vs_z22nosb"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
z23nosb_vs_z22nosb_volcano$plot
plotly::ggplotly(z23nosb_vs_z22nosb_volcano$plot)

z23sb_vs_z22sb_volcano <- plot_volcano_de(
    table = hs_macrophage_table[["data"]][["z23sb_vs_z22sb"]],
    fc_col = "deseq_logfc", p_col = "deseq_adjp",
    shapes_by_state = FALSE, color_by = "fc",  label = 10, label_column = "hgncsymbol")
z23sb_vs_z22sb_volcano$plot
plotly::ggplotly(z23sb_vs_z22sb_volcano$plot)

hs_macrophage_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)
hs_macrophage_table[["plots"]][["z23sb_vs_z22sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 10) +
  ylim(0, 60)

shared <- Vennerable::Venn(list("drug" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23sb_vs_z22sb"]]),
                                "nodrug" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23nosb_vs_z22nosb"]])))
pp(file="images/drug_nodrug_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)

shared <- Vennerable::Venn(list("drug" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23sb_vs_z22sb"]]),
                                "nodrug" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23nosb_vs_z22nosb"]])))
pp(file="images/drug_nodrug_venn_down.png")
Vennerable::plot(shared)
dev.off()
```

```{r z23drug_z23nodrug_z22drug_z22nodrug_plots}
hs_macrophage_table[["plots"]][["z23sb_vs_z23nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)
hs_macrophage_table[["plots"]][["z22sb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-8, 8) +
  ylim(0, 210)

hs_macrophage_table[["plots"]][["z23sb_vs_sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 28) +
  ylim(0, 140)
hs_macrophage_table[["plots"]][["z22sb_vs_sb"]][["deseq_vol_plots"]][["plot"]] +
  xlim(-10, 28) +
  ylim(0, 140)

shared <- Vennerable::Venn(list("z23" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z23sb_vs_sb"]]),
                                "z22" = rownames(hs_macrophage_sig[["deseq"]][["ups"]][["z22sb_vs_sb"]])))
pp(file="images/z23_z22_drug_venn_up.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)

shared <- Vennerable::Venn(list("z23" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z23sb_vs_sb"]]),
                                "z22" = rownames(hs_macrophage_sig[["deseq"]][["downs"]][["z22sb_vs_sb"]])))
pp(file="images/z23_z22_drug_venn_down.png")
Vennerable::plot(shared)
dev.off()
Vennerable::plot(shared)
```

## Parasite

```{r lp_de}
lp_macrophage_de <- all_pairwise(lp_macrophage,
                                 model_batch="svaseq", filter=TRUE)
tmrc2_parasite_keepers <- list(
    "z23nosb_vs_z22nosb" = c("z23", "z22"))
lp_macrophage_table <- combine_de_tables(
  lp_macrophage_de, keepers = tmrc2_parasite_keepers,
  excel=glue::glue("excel/macrophage_parasite_infection_de-v{ver}.xlsx"))
lp_macrophage_sig <- extract_significant_genes(
    lp_macrophage_table,
    excel=glue::glue("excel/macrophage_parasite_sig-v{ver}.xlsx"))

pp(file="images/lp_macrophage_z23_z22.png", image=lp_macrophage_table[["plots"]][["z23nosb_vs_z22nosb"]][["deseq_vol_plots"]][["plot"]])

up_genes <- lp_macrophage_sig[["deseq"]][["ups"]][[1]]
dim(up_genes)
down_genes <- lp_macrophage_sig[["deseq"]][["downs"]][[1]]
dim(down_genes)
```

# Over representation searches

```{r over_represent_data}
all_gp <- all_gprofiler(hs_macrophage_sig)

tt <- simple_gprofiler2(hs_macrophage_sig$deseq$ups[[1]])

pp(file="images/z23_uninf_reactome_up.png", image=all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["reactome_plot_over"]], height=12, width=9)
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["kegg_plot_over"]]
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["mfp_plot_over"]]
all_gp[["z23nosb_vs_uninf_up"]][["pvalue_plots"]][["tf_plot_over"]]

pp(file="images/z22_uninf_reactome_up.png", image=all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["reactome_plot_over"]], height=12, width=9)
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["kegg_plot_over"]]
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["mfp_plot_over"]]
all_gp[["z22nosb_vs_uninf_up"]][["pvalue_plots"]][["tf_plot_over"]]

all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
all_gp[["z23sb_vs_z22sb_up"]][["pvalue_plots"]][["tf_plot_over"]]

all_gp[["z23sb_vs_z22sb_down"]][["pvalue_plots"]][["reactome_plot_over"]]

all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
all_gp[["z23sb_vs_z23nosb_up"]][["pvalue_plots"]][["tf_plot_over"]]

all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["reactome_plot_over"]]
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["kegg_plot_over"]]
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["mfp_plot_over"]]
all_gp[["z22sb_vs_z22nosb_up"]][["pvalue_plots"]][["tf_plot_over"]]
```

```{r goseq_lp}
up_goseq <- simple_goseq(up_genes, go_db=lp_go, length_db=lp_lengths)
## View categories over represented in the 2.3 samples
up_goseq$pvalue_plots$bpp_plot_over
down_goseq <- simple_goseq(down_genes, go_db=lp_go, length_db=lp_lengths)
## View categories over represented in the 2.2 samples
down_goseq$pvalue_plots$bpp_plot_over
```

# GSVA

```{r gsva}
hs_infected <- subset_expt(hs_macrophage, subset="macrophagetreatment!='uninf'") %>%
  subset_expt(subset="macrophagetreatment!='uninf_sb'")
hs_gsva_c2 <- simple_gsva(hs_infected)
hs_gsva_c7 <- simple_gsva(hs_infected, signature_category = "c7")

hs_gsva_c2_sig <- get_sig_gsva_categories(hs_gsva_c2, excel = "excel/hs_macrophage_gsva_c2_sig.xlsx")
hs_gsva_c2_sig$raw_plot

hs_gsva_c7_sig <- get_sig_gsva_categories(hs_gsva_c7, excel = "excel/hs_macrophage_gsva_c7_sig.xlsx")
hs_gsva_c7_sig$raw_plot
```

# Try out a new tool

Two reasons: Najib loves him some PCA, this uses wikipathways, which is something I think is neat.

Ok, I spent some time looking through the code and I have some
problems with some of the design decisions.

Most importantly, it requires a data.frame() which has the following format:

1.  No rownames, instead column #1 is the sample ID.
2.  Columns 2-m are the categorical/survival/etc metrics.
3.  Columns m-n are 1 gene-per-column with log2 values.

But when I think about it I think I get the idea, they want to be able to do modelling stuff
more easily with response factors.

```{r pathwayPCA, eval=FALSE}
library(pathwayPCA)
library(rWikiPathways)

downloaded <- downloadPathwayArchive(organism = "Homo sapiens", format = "gmt")
data_path <- system.file("extdata", package="pathwayPCA")
wikipathways <- read_gmt(paste0(data_path, "/wikipathways_human_symbol.gmt"), description=TRUE)

expt <- subset_expt(hs_macrophage, subset="macrophagetreatment!='uninf'") %>%
  subset_expt(subset="macrophagetreatment!='uninf_sb'")
expt <- set_expt_conditions(expt, fact="macrophagezymodeme")

symbol_vector <- fData(expt)[[symbol_column]]
names(symbol_vector) <- rownames(fData(expt))
symbol_df <- as.data.frame(symbol_vector)

assay_df <- merge(symbol_df, as.data.frame(exprs(expt)), by = "row.names")
assay_df[["Row.names"]] <- NULL
rownames(assay_df) <- make.names(assay_df[["symbol_vector"]], unique = TRUE)
assay_df[["symbol_vector"]] <- NULL
assay_df <- as.data.frame(t(assay_df))
assay_df[["SampleID"]] <- rownames(assay_df)
assay_df <- dplyr::select(assay_df, "SampleID", everything())

factor_df <- as.data.frame(pData(expt))
factor_df[["SampleID"]] <- rownames(factor_df)
factor_df <- dplyr::select(factor_df, "SampleID", everything())
factor_df <- factor_df[, c("SampleID", factors)]

tt <- CreateOmics(
    assayData_df = assay_df,
    pathwayCollection_ls = wikipathways,
    response = factor_df,
    respType = "categorical",
    minPathSize=5)

super <- AESPCA_pVals(
    object = tt,
    numPCs = 2,
    parallel = FALSE,
    numCores = 8,
    numReps = 2,
    adjustment = "BH")
```

```{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)
```
