1 Annotation version: 20200706
1.1 Genome annotation input
There are a few methods of importing annotation data into R. The following are two attempts, the second is currently being used in these analyses.
meta <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
lm_entry <- EuPathDB::get_eupath_entry(species="Leishmania major", metadata=meta)## Found the following hits: Leishmania major strain Friedlin, Leishmania major strain LV39c5, Leishmania major strain SD 75.1, choosing the first.## Using: Leishmania major strain Friedlin.lp_entry <- EuPathDB::get_eupath_entry(species="Leishmania panamensis", metadata=meta)## Found the following hits: Leishmania panamensis MHOM/COL/81/L13, Leishmania panamensis strain MHOM/PA/94/PSC-1, choosing the first.## Using: Leishmania panamensis MHOM/COL/81/L13.lmex_entry <- EuPathDB::get_eupath_entry(species="Leishmania mexicana", metadata=meta)## Found: Leishmania mexicana MHOM/GT/2001/U1103lama_entry <- EuPathDB::get_eupath_entry(species="Leishmania amazonensis", metadata=meta)## Found: Leishmania amazonensis MHOM/BR/71973/M2269lb_entry <- EuPathDB::get_eupath_entry(species="2904", metadata=meta)## Found the following hits: Leishmania braziliensis MHOM/BR/75/M2904, Leishmania braziliensis MHOM/BR/75/M2904 2019, choosing the first.## Using: Leishmania braziliensis MHOM/BR/75/M2904.ld_entry <- EuPathDB::get_eupath_entry(species="donovani", metadata=meta)## Found the following hits: Leishmania donovani BPK282A1, Leishmania donovani CL-SL, Leishmania donovani strain BHU 1220, Leishmania donovani strain LV9, choosing the first.## Using: Leishmania donovani BPK282A1.crit_entry <- EuPathDB::get_eupath_entry(species="Crith", metadata=meta)## Found: Crithidia fasciculata strain Cf-Cltesting_panamensis <- EuPathDB::make_eupath_orgdb(entry=lp_entry)## org.Lpanamensis.MHOMCOL81L13.v46.eg.db is already installed.testing_braziliensis <- EuPathDB::make_eupath_orgdb(entry=lb_entry)## org.Lbraziliensis.MHOMBR75M2904.v46.eg.db is already installed.testing_donovani <- EuPathDB::make_eupath_orgdb(entry=ld_entry)## org.Ldonovani.BPK282A1.v46.eg.db is already installed.testing_mexicana <- EuPathDB::make_eupath_orgdb(entry=lmex_entry)## org.Lmexicana.MHOMGT2001U1103.v46.eg.db is already installed.testing_major <- EuPathDB::make_eupath_orgdb(entry=lm_entry)## org.Lmajor.Friedlin.v46.eg.db is already installed.testing_crith <- EuPathDB::make_eupath_orgdb(entry=crit_entry)## org.Cfasciculata.Cf.Cl.v46.eg.db is already installed.Assuming the above packages got created, we may load them and extract the annotation data.
wanted_fields <- c("annot_cds_length", "annot_chromosome", "annot_gene_entrez_id",
"annot_gene_name", "annot_strand", "gid", "go_go_id",
"go_go_term_name", "go_ontology",
"interpro_description" ,"interpro_e_value", "type_gene_type")
lm_org <- sm(EuPathDB::load_eupath_annotations(query=lm_entry))
lp_org <- sm(EuPathDB::load_eupath_annotations(query=lp_entry))
lb_org <- sm(EuPathDB::load_eupath_annotations(query=lb_entry))
ld_org <- sm(EuPathDB::load_eupath_annotations(query=ld_entry))
lmex_org <- sm(EuPathDB::load_eupath_annotations(query=lmex_entry))
cf_ort <- sm(EuPathDB::load_eupath_annotations(query=crit_entry))1.2 Read a gff file
In contrast, it is possible to load most annotations of interest directly from the gff files used in the alignments. More in-depth information for the human transcriptome may be extracted from biomart.
## The old way of getting genome/annotation data
lp_gff <- "reference/lpanamensis.gff"
lb_gff <- "reference/lbraziliensis.gff"
hs_gff <- "reference/hsapiens.gtf"
lp_fasta <- "reference/lpanamensis.fasta.xz"
lb_fasta <- "reference/lbraziliensis.fasta.xz"
hs_fasta <- "reference/hsapiens.fasta.xz"
lp_annotations <- sm(load_gff_annotations(lp_gff, type="gene"))
rownames(lp_annotations) <- paste0("exon_", lp_annotations$web_id, ".1")
lb_annotations <- sm(load_gff_annotations(lb_gff, type="gene"))
hs_gff_annot <- sm(load_gff_annotations(hs_gff, id_col="gene_id"))
hs_annotations <- sm(load_biomart_annotations())$annotation
hs_annotations$ID <- hs_annotations$geneID
rownames(hs_annotations) <- make.names(hs_annotations[["ensembl_gene_id"]], unique=TRUE)
dim(hs_annotations)## [1] 197995 12lp_size_dist <- plot_histogram(lp_annotations[["width"]])
lp_size_dist
hs_size_dist <- plot_histogram(hs_annotations[["cds_length"]])
hs_size_dist +
ggplot2::scale_x_continuous(limits=c(0, 20000))## Warning: Removed 103681 rows containing non-finite values (stat_bin).## Warning: Removed 103681 rows containing non-finite values (stat_density).## Warning: Removed 2 rows containing missing values (geom_bar).
1.3 Extracting Cell Types
Maria Adelaida requested adding the xCell cell types to the data.
library(xCell)
data("xCell.data", package="xCell")
summary(xCell.data)## Length Class Mode
## spill 3 -none- list
## spill.array 3 -none- list
## signatures 489 GeneSetCollection list
## genes 10808 -none- characterlibrary(GSEABase)## Loading required package: annotate## Loading required package: XML## Loading required package: graph##
## Attaching package: 'graph'## The following object is masked from 'package:XML':
##
## addNodedetails(xCell.data$signatures[[1]])## setName: aDC%HPCA%1.txt
## geneIds: C1QA, C1QB, ..., CCL22 (total: 8)
## geneIdType: Null
## collectionType: Null
## setIdentifier: PEDS-092FVH8-LT:623:Tue Jun 6 14:36:33 2017:2
## description:
## organism:
## pubMedIds:
## urls:
## contributor:
## setVersion: 0.0.1
## creationDate:sigs <- xCell.data$signatures
head(names(sigs), n=10)## [1] "aDC%HPCA%1.txt" "aDC%HPCA%2.txt"
## [3] "aDC%HPCA%3.txt" "aDC%IRIS%1.txt"
## [5] "aDC%IRIS%2.txt" "aDC%IRIS%3.txt"
## [7] "Adipocytes%ENCODE%1.txt" "Adipocytes%ENCODE%2.txt"
## [9] "Adipocytes%ENCODE%3.txt" "Adipocytes%FANTOM%1.txt"## Here we see that the signatures are encoded as 3 element lists, the first element is the
## cell type, followed by source, followed by replicate.txt.
cell_types <- unlist(lapply(strsplit(x=names(sigs), split="%"), function(x) { x[[1]] }))
cell_sources <- unlist(lapply(strsplit(x=names(sigs), split="%"), function(x) { x[[2]] }))
type_fact <- as.factor(cell_types)
types <- levels(type_fact)
celltypes_to_genes <- list()
for (c in 1:length(types)) {
type <- types[c]
idx <- cell_types == type
set <- sigs[idx]
genes <- set %>%
geneIds() %>%
unlist()
celltypes_to_genes[[type]] <- as.character(genes)
}
genes_to_celltypes <- Biobase::reverseSplit(celltypes_to_genes)
g2c_df <- data.frame(row.names=unique(names(genes_to_celltypes)))
g2c_df[["found"]] <- 0
for (c in 1:length(celltypes_to_genes)) {
celltype_name <- names(celltypes_to_genes)[[c]]
message("Starting ", c, ": ", celltype_name)
celltype_column <- as.data.frame(celltypes_to_genes[[c]])
colnames(celltype_column) <- celltype_name
rownames(celltype_column) <- make.names(celltype_column[[1]], unique=TRUE)
celltype_column[[1]] <- TRUE
g2c_df <- merge(g2c_df, celltype_column, by="row.names", all.x=TRUE)
rownames(g2c_df) <- g2c_df[[1]]
g2c_df <- g2c_df[, -1]
}## Starting 1: aDC## Starting 2: Adipocytes## Starting 3: Astrocytes## Starting 4: B-cells## Starting 5: Basophils## Starting 6: CD4+ memory T-cells## Starting 7: CD4+ naive T-cells## Starting 8: CD4+ T-cells## Starting 9: CD4+ Tcm## Starting 10: CD4+ Tem## Starting 11: CD8+ naive T-cells## Starting 12: CD8+ T-cells## Starting 13: CD8+ Tcm## Starting 14: CD8+ Tem## Starting 15: cDC## Starting 16: Chondrocytes## Starting 17: Class-switched memory B-cells## Starting 18: CLP## Starting 19: CMP## Starting 20: DC## Starting 21: Endothelial cells## Starting 22: Eosinophils## Starting 23: Epithelial cells## Starting 24: Erythrocytes## Starting 25: Fibroblasts## Starting 26: GMP## Starting 27: Hepatocytes## Starting 28: HSC## Starting 29: iDC## Starting 30: Keratinocytes## Starting 31: ly Endothelial cells## Starting 32: Macrophages## Starting 33: Macrophages M1## Starting 34: Macrophages M2## Starting 35: Mast cells## Starting 36: Megakaryocytes## Starting 37: Melanocytes## Starting 38: Memory B-cells## Starting 39: MEP## Starting 40: Mesangial cells## Starting 41: Monocytes## Starting 42: MPP## Starting 43: MSC## Starting 44: mv Endothelial cells## Starting 45: Myocytes## Starting 46: naive B-cells## Starting 47: Neurons## Starting 48: Neutrophils## Starting 49: NK cells## Starting 50: NKT## Starting 51: Osteoblast## Starting 52: pDC## Starting 53: Pericytes## Starting 54: Plasma cells## Starting 55: Platelets## Starting 56: Preadipocytes## Starting 57: pro B-cells## Starting 58: Sebocytes## Starting 59: Skeletal muscle## Starting 60: Smooth muscle## Starting 61: Tgd cells## Starting 62: Th1 cells## Starting 63: Th2 cells## Starting 64: Tregshead(g2c_df)## found aDC Adipocytes Astrocytes B-cells Basophils CD4+ memory T-cells
## A1CF 0 NA NA NA NA NA NA
## A4GALT 0 NA NA NA NA NA NA
## AAAS 0 NA NA NA NA NA NA
## AADAC 0 NA NA NA NA NA NA
## AAK1 0 NA NA NA NA NA NA
## AAMP 0 NA NA NA NA NA TRUE
## CD4+ naive T-cells CD4+ T-cells CD4+ Tcm CD4+ Tem CD8+ naive T-cells
## A1CF NA NA NA NA NA
## A4GALT NA NA NA NA NA
## AAAS NA NA NA NA NA
## AADAC NA NA NA NA NA
## AAK1 TRUE TRUE TRUE NA NA
## AAMP NA NA NA NA NA
## CD8+ T-cells CD8+ Tcm CD8+ Tem cDC Chondrocytes
## A1CF NA NA NA NA NA
## A4GALT NA NA NA NA NA
## AAAS NA NA NA NA NA
## AADAC NA NA NA NA NA
## AAK1 TRUE NA TRUE NA NA
## AAMP NA NA NA NA NA
## Class-switched memory B-cells CLP CMP DC Endothelial cells Eosinophils
## A1CF NA NA NA NA NA NA
## A4GALT NA NA NA NA NA NA
## AAAS NA NA NA NA NA NA
## AADAC NA NA NA NA NA NA
## AAK1 NA NA NA NA NA NA
## AAMP NA NA NA NA NA NA
## Epithelial cells Erythrocytes Fibroblasts GMP Hepatocytes HSC iDC
## A1CF NA NA NA NA TRUE NA NA
## A4GALT TRUE NA NA NA NA NA NA
## AAAS NA NA NA NA NA NA NA
## AADAC NA NA NA NA TRUE NA NA
## AAK1 NA NA NA NA NA NA NA
## AAMP NA NA NA NA NA NA NA
## Keratinocytes ly Endothelial cells Macrophages Macrophages M1
## A1CF NA NA NA NA
## A4GALT NA NA NA NA
## AAAS NA NA NA NA
## AADAC NA NA NA NA
## AAK1 NA NA NA NA
## AAMP NA NA NA NA
## Macrophages M2 Mast cells Megakaryocytes Melanocytes Memory B-cells MEP
## A1CF NA NA NA NA NA NA
## A4GALT NA NA NA NA NA NA
## AAAS NA NA NA NA NA NA
## AADAC NA NA NA NA NA NA
## AAK1 NA NA NA NA NA NA
## AAMP NA NA NA NA NA NA
## Mesangial cells Monocytes MPP MSC mv Endothelial cells Myocytes
## A1CF NA NA NA NA NA NA
## A4GALT NA NA NA NA NA NA
## AAAS NA NA NA TRUE NA NA
## AADAC NA NA NA NA NA NA
## AAK1 NA NA NA NA NA NA
## AAMP NA NA NA NA NA NA
## naive B-cells Neurons Neutrophils NK cells NKT Osteoblast pDC Pericytes
## A1CF NA NA NA NA NA NA NA NA
## A4GALT NA NA NA NA NA NA NA NA
## AAAS NA NA NA NA NA NA NA NA
## AADAC NA NA NA NA NA NA NA NA
## AAK1 NA NA NA NA NA NA NA NA
## AAMP NA NA NA NA NA NA NA NA
## Plasma cells Platelets Preadipocytes pro B-cells Sebocytes
## A1CF NA NA NA NA NA
## A4GALT NA NA NA NA NA
## AAAS NA NA NA NA NA
## AADAC NA NA NA NA NA
## AAK1 NA NA NA NA NA
## AAMP NA NA NA NA NA
## Skeletal muscle Smooth muscle Tgd cells Th1 cells Th2 cells Tregs
## A1CF NA NA NA NA NA NA
## A4GALT NA NA NA NA NA NA
## AAAS NA NA NA NA NA NA
## AADAC NA NA NA NA NA NA
## AAK1 NA NA NA NA NA NA
## AAMP NA NA NA NA NA NAna_idx <- is.na(g2c_df)
g2c_df[na_idx] <- FALSE1.4 Getting ontology data
Annotation for gene ontologies may be gathered from a similarly large number of sources. The following are a couple.
## Try using biomart
hs_go <- sm(load_biomart_go())
## or the org.Hs.eg.db sqlite database
tt <- sm(library("Homo.sapiens"))
hs <- Homo.sapiens
##hs_go_ensembl <- load_orgdb_go(hs, hs_annotations$geneID)
##dim(hs_go_biomart)
##dim(hs_go_ensembl)
##hs_goids <- hs_go_biomart
## While testing, I called this desc, that will need to change.
##lp_tooltips <- make_tooltips(lp_annotations)
##lb_tooltips <- make_tooltips(lb_annotations)
lp_lengths <- lp_annotations[, c("ID", "width")]
lb_lengths <- lb_annotations[, c("ID", "width")]
hs_lengths <- hs_annotations[, c("ensembl_gene_id", "cds_length")]
colnames(hs_lengths) <- c("ID", "width")
lp_goids <- read.csv(file="reference/lpan_go.txt.xz", sep="\t", header=FALSE)
lb_goids <- read.csv(file="reference/lbraz_go.txt.xz", sep="\t", header=FALSE)
colnames(lp_goids) <- c("ID","GO","ont","name","source","tag")
colnames(lb_goids) <- c("ID","GO","ont","name","source","tag")2 Putting the pieces together
The macrophage experiment has samples across 2 contexts, the host and parasite. The following block sets up one experiment for each. If you open the all_samples-species.xlsx files, you will note immediately that a few different attempts were made at ascertaining the most likely experimental factors that contributed to the readily apparent batch effects.
2.1 The human transcriptome mappings
Keep in mind that if I change the experimental design with new annotations, I must therefore regenerate the following.
hs_final_annotations <- hs_annotations
hs_final_annotations <- hs_final_annotations[, c("ensembl_transcript_id", "ensembl_gene_id", "cds_length",
"hgnc_symbol", "description", "gene_biotype")]
hs_final_annotations$rn <- rownames(hs_final_annotations)
note <- "New experimental design factors by snp added 2016-09-20"
hs_final_annotations <- merge(hs_final_annotations, g2c_df,
by.x="hgnc_symbol", by.y="row.names", all.x=TRUE)
rownames(hs_final_annotations) <- hs_final_annotations$rn
hs_final_annotations$rn <- NULL
na_idx <- is.na(hs_final_annotations$xcell_types)
hs_final_annotations[na_idx, "xcell_types"] <- ""
hs_macr <- create_expt(
metadata=glue::glue("sample_sheets/macrophage_samples_{ver}.xlsx"),
gene_info=hs_final_annotations,
file_column="humanfile",
notes=note)## Reading the sample metadata.## Dropped 1 rows from the sample metadata because they were blank.## The sample definitions comprises: 11 rows(samples) and 56 columns(metadata fields).## Reading count tables.## Reading count files with read.table().## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0241/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0242/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0243/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0244/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0245/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0246/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0247/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0248/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0637/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0638/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## /mnt/sshfs/cbcbsub01/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/preprocessing/hpgl0639/outputs/tophat_hsapiens/accepted_paired.count.xz contains 51046 rows and merges to 51046 rows.## Finished reading count data.## Matched 43897 annotations and counts.## Bringing together the count matrix and gene information.## Some annotations were lost in merging, setting them to 'undefined'.## Saving the expressionset to 'expt.rda'.## The final expressionset has 51041 rows and 11 columns.hs_annotations <- fData(hs_macr)
undef_idx <- hs_annotations == "undefined"
hs_annotations[undef_idx] <- FALSE
fData(hs_macr[["expressionset"]]) <- hs_annotations
knitr::kable(head(hs_macr$design, n=1))| sampleid | pathogenstrain | experimentname | tubelabel | alias | condition | batch | anotherbatch | snpclade | snpcladev2 | snpcladev3 | pathogenstrain1 | label | donor | time | pctmappedparasite | pctcategory | state | sourcelab | expperson | pathogen | host | hostcelltype | noofhostcells | infectionperiodhpitimeofharvest | moiexposure | parasitespercell | pctinf | rnangul | rnaqcpassed | libraryconst | libqcpassed | index | descriptonandremarks | observation | lowercaseid | humanfile | parasitefile | bcftable | salmonreads | hssalmonmapped | hssalmonmaprate | lpsalmonmapped | lpsalmonmaprate | tophatpairs | hstophataligned | hstophatpct | hstophatmulti | hstophatdiscordant | hstophatconcordantpct | lptophataligned | lptophatpct | lptophatmulti | lptophatdiscordant | lpconcordantpct | variantpositions | file | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HPGL0241 | HPGL0241 | none | macrophage | TM130-Nil (Blue label) | Nil | uninf | a | a | undef | undef | undef | none | uninf_2 | d130 | undef | undef | 0 | uninfected | Ade | Adriana | none | Human | Human macs | Max 2 mill | 2h - 24h chase period | NA | unknown | unknown | 468 | Y | Wanderson | Y | 1 | Uninfected human macrophages | NA | hpgl0241 | preprocessing/hpgl0241/outputs/tophat_hsapiens/accepted_paired.count.xz | undef | undef | 46628648 | 26156539 | 0.561 | NA | NA | 46319335 | 40905961 | 0.8831 | 1374099 | 1430888 | 0.8522 | NA | NA | NA | NA | NA | NA | null |
##cds_entries <- fData(hs_macr)
##cds_entries <- cds_entries[["gene_biotype"]] == "protein_coding"
##hs_cds_macr <- hs_macr
##hs_cds_macr$expressionset <- hs_cds_macr$expressionset[cds_entries, ]
##new_cds_entries <- fData(hs_cds_macr)
hs_cds_macr <- exclude_genes_expt(hs_macr, method="keep",
column="gene_biotype",
patterns="protein_coding")## Before removal, there were 51041 entries.## Now there are 18847 entries.## Percent kept: 93.209, 95.095, 95.545, 96.588, 95.913, 96.249, 95.758, 96.106, 96.506, 96.391, 95.414## Percent removed: 6.791, 4.905, 4.455, 3.412, 4.087, 3.751, 4.242, 3.894, 3.494, 3.609, 4.5862.2 The parasite transcriptome mappings
lp_macr <- sm(create_expt(
metadata=glue::glue("sample_sheets/macrophage_samples_{ver}.xlsx"),
gene_info=lp_annotations, file_column="parasitefile"))
knitr::kable(head(lp_macr$design, n=3),
caption="The first three rows of the parasite experimental design.")| sampleid | pathogenstrain | experimentname | tubelabel | alias | condition | batch | anotherbatch | snpclade | snpcladev2 | snpcladev3 | pathogenstrain1 | label | donor | time | pctmappedparasite | pctcategory | state | sourcelab | expperson | pathogen | host | hostcelltype | noofhostcells | infectionperiodhpitimeofharvest | moiexposure | parasitespercell | pctinf | rnangul | rnaqcpassed | libraryconst | libqcpassed | index | descriptonandremarks | observation | lowercaseid | humanfile | parasitefile | bcftable | salmonreads | hssalmonmapped | hssalmonmaprate | lpsalmonmapped | lpsalmonmaprate | tophatpairs | hstophataligned | hstophatpct | hstophatmulti | hstophatdiscordant | hstophatconcordantpct | lptophataligned | lptophatpct | lptophatmulti | lptophatdiscordant | lpconcordantpct | variantpositions | file | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| HPGL0242 | HPGL0242 | s2271 | macrophage | TM130-2271 | Self-Healing | sh | a | a | white | whitepink | right | s2271 | sh_2271 | d130 | undef | 30 | 3 | self_heal | Ade | Adriana | Lp | Human | Human macs | Max 2 mill | 2h - 24h chase period | 0.0486 | unknown | unknown | 276 | Y | Wanderson | Y | 8 | Infected human macrophages. | NA | hpgl0242 | preprocessing/hpgl0242/outputs/tophat_hsapiens/accepted_paired.count.xz | preprocessing/hpgl0242/outputs/tophat_lpanamensis/accepted_paired.count.xz | preprocessing/outputs/hpgl0242_parsed_count.txt | 42742857 | 17945935 | 0.4199 | 8023463 | 0.1877 | 42612353 | 25394266 | 0.5959 | 869649 | 784620 | 0.5775 | 13117819 | 0.3078 | 350277 | 263923 | 0.3016 | 3930 | null |
| HPGL0243 | HPGL0243 | s2272 | macrophage | TM130-2272 | Self-Healing | sh | a | a | white | whitepink | right | s2272 | sh_2272 | d130 | undef | 30 | 3 | self_heal | Ade | Adriana | Lp | Human | Human macs | Max 2 mill | 2h - 24h chase period | 0.0486 | unknown | unknown | 532 | Y | Wanderson | Y | 10 | Infected human macrophages | NA | hpgl0243 | preprocessing/hpgl0243/outputs/tophat_hsapiens/accepted_paired.count.xz | preprocessing/hpgl0243/outputs/tophat_lpanamensis/accepted_paired.count.xz | preprocessing/outputs/hpgl0243_parsed_count.txt | 46796079 | 21046460 | 0.4497 | 6823750 | 0.1458 | 47344642 | 31160297 | 0.6582 | 1000248 | 924296 | 0.6386 | 11581460 | 0.2446 | 319338 | 245169 | 0.2394 | NA | null |
| HPGL0244 | HPGL0244 | s5433 | macrophage | TM130-5433 | Chronic | chr | a | a | blue_self | blue | left | s5433 | chr_5433 | d130 | undef | 15 | 1 | chronic | Ade | Adriana | Lp | Human | Human macs | Max 2 mill | 2h - 24h chase period | 0.0486 | unknown | unknown | 261 | Y | Wanderson | Y | 27 | Infected human macrophages | NA | hpgl0244 | preprocessing/hpgl0244/outputs/tophat_hsapiens/accepted_paired.count.xz | preprocessing/hpgl0244/outputs/tophat_lpanamensis/accepted_paired.count.xz | preprocessing/outputs/hpgl0244_parsed_count.txt | 47150925 | 25281958 | 0.5362 | 3761371 | 0.0798 | 46925604 | 36379602 | 0.7753 | 1070964 | 991929 | 0.7541 | 5755998 | 0.1227 | 154830 | 116414 | 0.1202 | 85981 | null |
3 Supplemental Table 1
Table S1 is going to be a summary of the metadata in all_samples-combined This may also include some of the numbers regarding mapping %, etc.
Wanted columns:
- Sample ID: HPGLxxxx
- Donor Code: TM130 or PG1xx
- Cell Type: Macrophage or PBMC
- Infection Status: Infected or Uninfected
- Disease Outcome: Chronic or Self-Healing or NA
- Batch: A or B (macrophage); NA for PBMC
- Number of reads that passed Illumina filter
- Number of reads after trimming
- Number of reads mapped - human
- % reads mapped - human
- Number of reads mapped - L.panamensis
- % reads mapped - L.panamensis
This table is maintained as an excel file in the sample_sheets/ directory.
4 Sample Estimation, Macrophages: 20200706
This document is concerned with analyzing RNAseq data of human and parasite during the infection of human macrophages. A few different strains of L. panamensis were used; the experiment is therefore segregated into groups named ‘self-healing’, ‘chronic’, and ‘uninfected’. Two separate sets of libraries were generated, one earlier set with greater coverage, and a later set including only 1 uninfected sample, and 2-3 chronic samples.
4.1 Extract the macrophage experiment
The following subset operation extract the samples used for the macrophage experiment. The next three lines then change the colors from the defaults.
new_colors <- c("#009900", "#990000", "#000099")
names(new_colors) <- c("uninf", "chr", "sh")
hs_macr <- set_expt_colors(hs_macr, colors=new_colors)## The new colors are a character, changing according to condition.labels <- as.character(pData(hs_macr)[["label"]])
hs_macr <- set_expt_samplenames(hs_macr, labels)
hs_cds_macr <- set_expt_colors(hs_cds_macr, colors=new_colors)## The new colors are a character, changing according to condition.hs_cds_macr <- set_expt_samplenames(hs_cds_macr, labels)5 Compare methods
In our meeting this morning (20190708), Najib and Maria Adelaida asked for how the data looks depending on batch methodology.
start <- normalize_expt(hs_cds_macr, filter=TRUE, convert="cpm", norm="quant", transform="log2")## This function will replace the expt$expressionset slot with:## log2(cpm(quant(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Not correcting the count-data for batch effects. If batch is
## included in EdgerR/limma's model, then this is probably wise; but in extreme
## batch effects this is a good parameter to play with.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 3 values equal to 0, adding 1 to the matrix.## Step 5: not doing batch correction.pp(file="cpm_quant_filter_pca.png", image=plot_pca(start)$plot)## Writing the image to: cpm_quant_filter_pca.png and calling dev.off().
limma_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm", norm="quant",
transform="log2", batch="limma")## This function will replace the expt$expressionset slot with:## log2(limma(cpm(quant(cbcb(data)))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 3 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with limma.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128654 entries are x>1: 99%.## batch_counts: Before batch/surrogate estimation, 3 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 1737 entries are 0<x<1: 1%.## The be method chose 1 surrogate variables.## batch_counts: Using limma's removeBatchEffect to remove batch effect.## If you receive a warning: 'NANs produced', one potential reason is that the data was quantile normalized.## There are 2 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_limma.png", image=plot_pca(limma_batch)$plot)## Writing the image to: lcqf_limma.png and calling dev.off().
pca_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm", norm="quant",
transform="log2", batch="pca")## This function will replace the expt$expressionset slot with:## log2(pca(cpm(quant(cbcb(data)))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: normalizing the data with quant.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 3 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with pca.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128654 entries are x>1: 99%.## batch_counts: Before batch/surrogate estimation, 3 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 1737 entries are 0<x<1: 1%.## The be method chose 1 surrogate variables.## Attempting pca surrogate estimation with 1 surrogate.## There are 1 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_pca.png", image=plot_pca(pca_batch)$plot)## Writing the image to: lcqf_pca.png and calling dev.off().
svaseq_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm",
transform="log2", batch="svaseq")## This function will replace the expt$expressionset slot with:## log2(svaseq(cpm(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 77 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with svaseq.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## Attempting svaseq estimation with 1 surrogate.## There are 2 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_svaseq.png", image=plot_pca(svaseq_batch)$plot)## Writing the image to: lcqf_svaseq.png and calling dev.off().
sva_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm",
transform="log2", batch="fsva")## This function will replace the expt$expressionset slot with:## log2(fsva(cpm(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 77 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with fsva.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## Attempting fsva surrogate estimation with 1 surrogate.## There are 2 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_sva.png", image=plot_pca(sva_batch)$plot)## Writing the image to: lcqf_sva.png and calling dev.off().
combat_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm",
transform="log2", batch="combat")## This function will replace the expt$expressionset slot with:## log2(combat(cpm(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 77 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with combat.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## batch_counts: Using combat with a prior, no scaling, and a null model.## There are 13 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_combat.png", image=plot_pca(combat_batch)$plot)## Writing the image to: lcqf_combat.png and calling dev.off().
combatnp_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm",
transform="log2", batch="combat_noprior")## This function will replace the expt$expressionset slot with:## log2(combat_noprior(cpm(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 77 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with combat_noprior.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## batch_counts: Using combat without a prior and no scaling.## This takes a long time!## There are 18 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.pp(file="lcqf_combatnp.png", image=plot_pca(combatnp_batch)$plot)## Writing the image to: lcqf_combatnp.png and calling dev.off().
6 Figure S1
Figure S1 should include nice versions of the sample metrics. The normalization chosen is batch-in-model.
First, however, we will make some plots of the raw data.
Sample names are going to be ‘infectionstate_strainnumber’ : chr_7721
- Panel A: Library sizes.
- Panel B: Heatmap distance raw.
- Panel C: PCA
- Panel D: TSNE
fig_s1 <- sm(write_expt(
hs_cds_macr, norm="raw", violin=FALSE, convert="cpm",
transform="log2", batch="svaseq", filter=TRUE,
excel=paste0("excel/figure_s1_sample_estimation-v", ver, ".xlsx")))pp(file="fig_s1a_libsizes.tif", image=fig_s1$raw_libsize)## Writing the image to: fig_s1a_libsizes.tif and calling dev.off().pp(file="fig_s1b_heatmap.tif", image=fig_s1$norm_disheat)## Writing the image to: fig_s1b_heatmap.tif and calling dev.off().pp(file="fig_s1c_raw_pca.tif", image=fig_s1$raw_scaled_pca)## Writing the image to: fig_s1c_raw_pca.tif and calling dev.off().pp(file="fig_1a_norm_pca.tif", image=fig_s1$norm_pca)## Writing the image to: fig_1a_norm_pca.tif and calling dev.off().7 Differential Expression, Macrophage: 20200706
8 Differential expression analyses
The most likely batch method from the above is svaseq. Let us perform differential expression analyses with it along with a few other methods.
hs_contrasts <- list(
"macro_chr-sh" = c("chr","sh"),
"macro_chr-nil" = c("chr","uninf"),
"macro_sh-nil" = c("sh", "uninf"))
## Set up the data used in the comparative contrast sets.8.1 No batch in the model
8.1.1 Set up no batch
Print a reminder of what we can expect when doing this with no batch information.
hs_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
hs_lowfilt_pca <- sm(plot_pca(hs_cds_macr, transform="log2"))
hs_lowfilt_pca$plot
hs_macr_nobatch <- all_pairwise(input=hs_cds_macr, model_batch=FALSE,
limma_method="robust")## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Assuming no batch in model for testing pca.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.## wow, all tools including basic agree almost completely
medians_by_condition <- hs_macr_nobatch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_contr-v{ver}.xlsx")
hs_macr_nobatch_tables <- sm(combine_de_tables(hs_macr_nobatch,
excel=excel_file,
keepers=hs_contrasts,
extra_annot=medians_by_condition))excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_sig-v{ver}.xlsx")
hs_macr_nobatch_sig <- sm(extract_significant_genes(hs_macr_nobatch_tables,
excel=excel_file,
according_to="all"))8.2 Batch in the model
8.2.1 Batch setup
hs_lowfilt_batch_pca <- sm(plot_pca(hs_cds_macr, transform="log2",
convert="cpm", batch="limma", filter=TRUE))
hs_lowfilt_batch_pca$plot
In this attempt, we add a batch factor in the experimental model and see how it does.
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_batch <- all_pairwise(input=hs_cds_macr, batch=TRUE, limma_method="robust")## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Using limma's removeBatchEffect to visualize with(out) batch inclusion.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.medians_by_condition <- hs_macr_batch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_contr-v{ver}.xlsx")
hs_macr_batch_tables <- combine_de_tables(
hs_macr_batch,
keepers=hs_contrasts,
extra_annot=medians_by_condition,
excel=excel_file)## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/3: macro_chr-sh which is: chr/sh.## Found inverse table with sh_vs_chr## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, b## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.174 1.023
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.072 1.005
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.00197 0.99550## Working on 2/3: macro_chr-nil which is: chr/uninf.## Found inverse table with uninf_vs_chr## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, b## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.153 1.019
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.126 1.008
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.0314 0.9979## Working on 3/3: macro_sh-nil which is: sh/uninf.## Found inverse table with uninf_vs_sh## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, b## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.00696 0.99847
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.0265 1.0021
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.0136 1.0005## Adding venn plots for macro_chr-sh.## Limma expression coefficients for macro_chr-sh; R^2: 0.994; equation: y = 0.996x - 0.00197## Deseq expression coefficients for macro_chr-sh; R^2: 0.987; equation: y = 1.02x - 0.174## Edger expression coefficients for macro_chr-sh; R^2: 0.992; equation: y = 1.01x - 0.072## Adding venn plots for macro_chr-nil.## Limma expression coefficients for macro_chr-nil; R^2: 0.992; equation: y = 0.998x - 0.0314## Deseq expression coefficients for macro_chr-nil; R^2: 0.981; equation: y = 1.02x - 0.153## Edger expression coefficients for macro_chr-nil; R^2: 0.989; equation: y = 1.01x - 0.126## Adding venn plots for macro_sh-nil.## Limma expression coefficients for macro_sh-nil; R^2: 0.998; equation: y = 1x - 0.0136## Deseq expression coefficients for macro_sh-nil; R^2: 0.993; equation: y = 0.998x + 0.00696## Edger expression coefficients for macro_sh-nil; R^2: 0.998; equation: y = 1x - 0.0265## Writing summary information, compare_plot is: TRUE.## Performing save of excel/20200715_hs_macr_batchmodel_contr-v20200706.xlsx.excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_sig-v{ver}.xlsx")
hs_macr_batch_sig <- extract_significant_genes(
hs_macr_batch_tables, excel=excel_file,
according_to="deseq")## Writing a legend of columns.## Printing significant genes to the file: excel/20200715_hs_macr_batchmodel_sig-v20200706.xlsx## 1/3: Creating significant table up_deseq_macro_chr-sh## 2/3: Creating significant table up_deseq_macro_chr-nil## The up table macro_sh-nil is empty.## The down table macro_sh-nil is empty.## Adding significance bar plots.excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_abund-v{ver}.xlsx")
hs_macr_batch_abun <- sm(extract_abundant_genes(
hs_macr_batch_tables, excel=excel_file,
according_to="deseq"))8.3 SVA
8.3.1 sva setup
hs_lowfilt_sva_pca <- sm(plot_pca(hs_cds_macr, transform="log2",
convert="cpm", batch="svaseq", filter=TRUE))
hs_lowfilt_sva_pca$plot
In this attempt, we tell all pairwise to invoke svaseq.
hs_macr_sva <- all_pairwise(input=hs_cds_macr, model_batch="svaseq", filter=TRUE, limma_method="robust")## batch_counts: Before batch/surrogate estimation, 130219 entries are x>1: 100%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## The be method chose 1 surrogate variables.## Attempting svaseq estimation with 1 surrogate.## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Using svaseq to visualize before/after batch inclusion.## Performing a test normalization with: raw## This function will replace the expt$expressionset slot with:## log2(svaseq(cpm(cbcb(data))))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 0 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: transforming the data with log2.## transform_counts: Found 77 values equal to 0, adding 1 to the matrix.## Step 5: doing batch correction with svaseq.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## Attempting svaseq estimation with 1 surrogate.## There are 2 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.medians_by_condition <- hs_macr_sva$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_contr-v{ver}.xlsx")
hs_macr_sva_tables <- combine_de_tables(
hs_macr_sva,
keepers=hs_contrasts,
extra_annot=medians_by_condition,
excel=excel_file)## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/3: macro_chr-sh which is: chr/sh.## Found inverse table with sh_vs_chr## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, SV1## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.113 1.016
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.105 1.016
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.00764 1.00080## Working on 2/3: macro_chr-nil which is: chr/uninf.## Found inverse table with uninf_vs_chr## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, SV1## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.320 0.967
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.213 0.968
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.0146 0.9958## Working on 3/3: macro_sh-nil which is: sh/uninf.## Found inverse table with uninf_vs_sh## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf, SV1## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.408 0.955
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.264 0.955
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.0308 0.9953## Adding venn plots for macro_chr-sh.## Limma expression coefficients for macro_chr-sh; R^2: 0.976; equation: y = 1x - 0.00764## Deseq expression coefficients for macro_chr-sh; R^2: 0.975; equation: y = 1.02x - 0.113## Edger expression coefficients for macro_chr-sh; R^2: 0.975; equation: y = 1.02x - 0.105## Adding venn plots for macro_chr-nil.## Limma expression coefficients for macro_chr-nil; R^2: 0.945; equation: y = 0.996x + 0.0146## Deseq expression coefficients for macro_chr-nil; R^2: 0.94; equation: y = 0.967x + 0.32## Edger expression coefficients for macro_chr-nil; R^2: 0.94; equation: y = 0.968x + 0.213## Adding venn plots for macro_sh-nil.## Limma expression coefficients for macro_sh-nil; R^2: 0.98; equation: y = 0.995x + 0.0308## Deseq expression coefficients for macro_sh-nil; R^2: 0.978; equation: y = 0.955x + 0.408## Edger expression coefficients for macro_sh-nil; R^2: 0.978; equation: y = 0.955x + 0.264## Writing summary information, compare_plot is: TRUE.## Performing save of excel/20200715_hs_macr_svamodel_contr-v20200706.xlsx.excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_sig-v{ver}.xlsx")
hs_macr_sva_sig <- extract_significant_genes(
hs_macr_sva_tables, excel=excel_file,
according_to="deseq")## Writing a legend of columns.## Printing significant genes to the file: excel/20200715_hs_macr_svamodel_sig-v20200706.xlsx## 1/3: Creating significant table up_deseq_macro_chr-sh## 2/3: Creating significant table up_deseq_macro_chr-nil## 3/3: Creating significant table up_deseq_macro_sh-nil## Adding significance bar plots.excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_abund-v{ver}.xlsx")
hs_macr_sva_abun <- sm(extract_abundant_genes(
hs_macr_sva_tables, excel=excel_file,
according_to="deseq"))8.4 Combat
As you know, combat actually changes the data, so it will require a slightly different setup.
8.4.1 combat setup
combatnp_input <- normalize_expt(hs_cds_macr, filter=TRUE,
convert="cpm",
batch="combat_noprior")## This function will replace the expt$expressionset slot with:## combat_noprior(cpm(cbcb(data)))## It will save copies of each step along the way
## in expt$normalized with the corresponding libsizes. Keep libsizes in mind
## when invoking limma. The appropriate libsize is non-log(cpm(normalized)).
## This is most likely kept at:
## 'new_expt$normalized$intermediate_counts$normalization$libsizes'
## A copy of this may also be found at:
## new_expt$best_libsize## Leaving the data in its current base format, keep in mind that
## some metrics are easier to see when the data is log2 transformed, but
## EdgeR/DESeq do not accept transformed data.## Leaving the data unnormalized. This is necessary for DESeq, but
## EdgeR/limma might benefit from normalization. Good choices include quantile,
## size-factor, tmm, etc.## Step 1: performing count filter with option: cbcb## Removing 6993 low-count genes (11854 remaining).## Step 2: not normalizing the data.## Step 3: converting the data with cpm.## Step 4: not transforming the data.## Step 5: doing batch correction with combat_noprior.## Note to self: If you get an error like 'x contains missing values' The data has too many 0's and needs a stronger low-count filter applied.## Passing off to all_adjusters.## batch_counts: Before batch/surrogate estimation, 128099 entries are x>1: 98%.## batch_counts: Before batch/surrogate estimation, 77 entries are x==0: 0%.## batch_counts: Before batch/surrogate estimation, 2218 entries are 0<x<1: 2%.## The be method chose 1 surrogate variables.## batch_counts: Using combat without a prior and no scaling.## This takes a long time!## There are 133 (0%) elements which are < 0 after batch correction.## Setting low elements to zero.In this attempt, we tell all pairwise to invoke svaseq.
hs_macr_combat <- all_pairwise(input=combatnp_input, model_batch=FALSE,
force=TRUE, limma_method="robust")## Plotting a PCA before surrogate/batch inclusion.## Not putting labels on the PC plot.## Assuming no batch in model for testing pca.## Not putting labels on the PC plot.## Finished running DE analyses, collecting outputs.## Comparing analyses.excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_tables <- combine_de_tables(
hs_macr_combat,
keepers=hs_contrasts,
extra_annot=medians_by_condition,
excel=excel_file)## Writing a legend of columns.## Printing a pca plot before/after surrogates/batch estimation.## Working on 1/3: macro_chr-sh which is: chr/sh.## Found inverse table with sh_vs_chr## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.234 1.045
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.25 1.04
##
## sh_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing sh and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.0195 0.9839## Working on 2/3: macro_chr-nil which is: chr/uninf.## Found inverse table with uninf_vs_chr## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.244 1.047
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.267 1.046
##
## uninf_vs_chr## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and chr.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## 0.0203 0.9785## Working on 3/3: macro_sh-nil which is: sh/uninf.## Found inverse table with uninf_vs_sh## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.0274 1.0053
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.038 1.005
##
## uninf_vs_sh## This can do comparisons among the following columns in the pairwise result:## chr, sh, uninf## Actually comparing uninf and sh.##
## Call:
## robustbase::lmrob(formula = second ~ first, data = df, method = "SMDM")
##
## Coefficients:
## (Intercept) first
## -0.000921 0.998186## Adding venn plots for macro_chr-sh.## Limma expression coefficients for macro_chr-sh; R^2: 0.98; equation: y = 0.984x + 0.0195## Deseq expression coefficients for macro_chr-sh; R^2: 0.979; equation: y = 1.05x - 0.234## Edger expression coefficients for macro_chr-sh; R^2: 0.979; equation: y = 1.04x - 0.25## Adding venn plots for macro_chr-nil.## Limma expression coefficients for macro_chr-nil; R^2: 0.968; equation: y = 0.978x + 0.0203## Deseq expression coefficients for macro_chr-nil; R^2: 0.968; equation: y = 1.05x - 0.244## Edger expression coefficients for macro_chr-nil; R^2: 0.968; equation: y = 1.05x - 0.267## Adding venn plots for macro_sh-nil.## Limma expression coefficients for macro_sh-nil; R^2: 0.992; equation: y = 0.998x - 0.000921## Deseq expression coefficients for macro_sh-nil; R^2: 0.992; equation: y = 1.01x - 0.0274## Edger expression coefficients for macro_sh-nil; R^2: 0.992; equation: y = 1.01x - 0.038## Writing summary information, compare_plot is: TRUE.## Performing save of excel/20200715_hs_macr_combat_contr-v20200706.xlsx.excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_sig-v{ver}.xlsx")
hs_macr_combat_sig <- extract_significant_genes(
hs_macr_combat_tables, excel=excel_file,
according_to="deseq")## Writing a legend of columns.## Printing significant genes to the file: excel/20200715_hs_macr_combat_sig-v20200706.xlsx## 1/3: Creating significant table up_deseq_macro_chr-sh## 2/3: Creating significant table up_deseq_macro_chr-nil## The up table macro_sh-nil is empty.## The down table macro_sh-nil is empty.## Adding significance bar plots.excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_abund-v{ver}.xlsx")
hs_macr_combat_abun <- sm(extract_abundant_genes(
hs_macr_combat_tables, excel=excel_file,
according_to="deseq"))8.5 Compare these three results
nobatch_batch <- compare_de_results(first=hs_macr_nobatch_tables,
second=hs_macr_batch_tables)## Testing method: limma.## Adding method: limma to the set.## Testing method: deseq.## Adding method: deseq to the set.## Testing method: edger.## Adding method: edger to the set.## Starting method limma, table macro_chr-sh.## Starting method limma, table macro_chr-nil.## Starting method limma, table macro_sh-nil.## Starting method deseq, table macro_chr-sh.## Starting method deseq, table macro_chr-nil.## Starting method deseq, table macro_sh-nil.## Warning in cor(x = fs[["x"]], y = fs[["y"]], method = cor_method): the standard
## deviation is zero## Starting method edger, table macro_chr-sh.## Starting method edger, table macro_chr-nil.## Starting method edger, table macro_sh-nil.
nobatch_batch$logfc## macro_chr-sh macro_chr-nil macro_sh-nil
## 0.6481 0.6568 0.9817batch_sva <- compare_de_results(first=hs_macr_batch_tables,
second=hs_macr_sva_tables)## Testing method: limma.## Adding method: limma to the set.## Testing method: deseq.## Adding method: deseq to the set.## Testing method: edger.## Adding method: edger to the set.## Starting method limma, table macro_chr-sh.## Starting method limma, table macro_chr-nil.## Starting method limma, table macro_sh-nil.## Starting method deseq, table macro_chr-sh.## Starting method deseq, table macro_chr-nil.## Starting method deseq, table macro_sh-nil.## Starting method edger, table macro_chr-sh.## Starting method edger, table macro_chr-nil.## Starting method edger, table macro_sh-nil.
batch_sva$logfc## macro_chr-sh macro_chr-nil macro_sh-nil
## 0.6972 0.3393 0.4762batch_combat <- compare_de_results(first=hs_macr_batch_tables,
second=hs_macr_combat_tables)## Testing method: limma.## Adding method: limma to the set.## Testing method: deseq.## Adding method: deseq to the set.## Testing method: edger.## Adding method: edger to the set.## Starting method limma, table macro_chr-sh.## Starting method limma, table macro_chr-nil.## Starting method limma, table macro_sh-nil.## Starting method deseq, table macro_chr-sh.## Starting method deseq, table macro_chr-nil.## Starting method deseq, table macro_sh-nil.## Starting method edger, table macro_chr-sh.## Starting method edger, table macro_chr-nil.## Starting method edger, table macro_sh-nil.
batch_combat$logfc## macro_chr-sh macro_chr-nil macro_sh-nil
## 0.9207 0.9153 0.8649sva_combat <- compare_de_results(first=hs_macr_sva_tables,
second=hs_macr_combat_tables)## Testing method: limma.## Adding method: limma to the set.## Testing method: deseq.## Adding method: deseq to the set.## Testing method: edger.## Adding method: edger to the set.## Starting method limma, table macro_chr-sh.## Starting method limma, table macro_chr-nil.## Starting method limma, table macro_sh-nil.## Starting method deseq, table macro_chr-sh.## Starting method deseq, table macro_chr-nil.## Starting method deseq, table macro_sh-nil.## Starting method edger, table macro_chr-sh.## Starting method edger, table macro_chr-nil.## Starting method edger, table macro_sh-nil.
sva_combat$logfc## macro_chr-sh macro_chr-nil macro_sh-nil
## 0.6693 0.3243 0.3767## Interesting how much sva and combat disagree.8.6 Two likely volcano plots
batchmodel_volcano <- plot_volcano_de(
table=hs_macr_batch_tables[["data"]][["macro_chr-sh"]],
color_by="state",
fc_col="deseq_logfc",
p_col="deseq_adjp",
shapes_by_state=FALSE,
line_position="top")## The color list must have 4, setting it to the default.batchmodel_volcano$plot
svamodel_volcano <- plot_volcano_de(
table=hs_macr_sva_tables[["data"]][["macro_chr-sh"]],
color_by="state",
fc_col="deseq_logfc",
p_col="deseq_adjp",
shapes_by_state=FALSE,
line_position="top")## The color list must have 4, setting it to the default.svamodel_volcano$plot
8.7 PROPER
hs_proper <- simple_proper(de_tables=hs_macr_batch_tables)## Working on contrast 1: sh_vs_chr.## Warning in 1:Nreps: numerical expression has 4 elements: only the first used## Loading required package: edgeR## Loading required package: limma##
## Attaching package: 'limma'## The following object is masked from 'package:BiocGenerics':
##
## plotMA## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## SS=3,3 SS=5,5 SS=7,7 SS=10,10
## 0.2 0.51 0.54 0.55 NaN
## 0.5 0.61 0.62 0.63 NaN
## 1 0.67 0.67 0.67 NaN
## 2 0.72 0.71 0.71 NaN
## 5 0.77 0.75 0.74 NaN
## 10 0.80 0.78 0.77 NaN## Working on contrast 2: uninf_vs_chr.## Warning in 1:Nreps: numerical expression has 4 elements: only the first used## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
## 0.05, : zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(stratas, TD.lo[, j], stratas, TD.hi[, j], length = 0.05, :
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## zero-length arrow is of indeterminate angle and so skipped## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
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## SS=3,3 SS=5,5 SS=7,7 SS=10,10
## 0.2 0.54 0.56 0.56 NaN
## 0.5 0.63 0.64 0.64 NaN
## 1 0.69 0.68 0.68 NaN
## 2 0.73 0.72 0.71 NaN
## 5 0.78 0.76 0.75 NaN
## 10 0.81 0.79 0.77 NaN## Working on contrast 3: uninf_vs_sh.## Warning in 1:Nreps: numerical expression has 4 elements: only the first used## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
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## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
## Warning in 1:Nreps: numerical expression has 4 elements: only the first used
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## Warning in arrows(stratas, power.lo[, j], stratas, power.hi[, j], length =
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## zero-length arrow is of indeterminate angle and so skipped
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## zero-length arrow is of indeterminate angle and so skipped
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## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
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## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
## zero-length arrow is of indeterminate angle and so skipped
## Warning in arrows(stratas, FD.lo[, j], stratas, FD.hi[, j], length = 0.05, :
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## SS=3,3 SS=5,5 SS=7,7 SS=10,10
## 0.2 0.54 0.55 0.57 NaN
## 0.5 0.63 0.64 0.64 NaN
## 1 0.69 0.68 0.68 NaN
## 2 0.73 0.71 0.71 NaN
## 5 0.77 0.75 0.75 NaN
## 10 0.80 0.79 0.78 NaN8.8 Ontology searching against the sva results.
Recall that I made the variables ‘hs_macr_sva_sig’ and ‘hs_macr_sva_abun’ to hold the results of the most significantly changed and abundant genes.
gProfiler is my favorite tool for ontology searching, however they recently had a big update and split their code. The new version has all sorts of cool toys, but as of the last time I tried it, did not work. Thus the following is still using the older methods.
lfs_up <- hs_macr_sva_sig[["deseq"]][["ups"]][["macro_chr-sh"]]
lfs_down <- hs_macr_sva_sig[["deseq"]][["downs"]][["macro_chr-sh"]]
up_gp <- simple_gprofiler(sig_genes=lfs_up, species="hsapiens")## Performing gProfiler GO search of 108 genes against hsapiens.## GO search found 48 hits.## Performing gProfiler KEGG search of 108 genes against hsapiens.## KEGG search found 0 hits.## Performing gProfiler REAC search of 108 genes against hsapiens.## REAC search found 1 hits.## Performing gProfiler MI search of 108 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 108 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 108 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 108 genes against hsapiens.## HP search found 0 hits.up_gp[["pvalue_plots"]][["bpp_plot_over"]]
down_gp <- simple_gprofiler(sig_genes=lfs_down, species="hsapiens")## Performing gProfiler GO search of 55 genes against hsapiens.## GO search found 10 hits.## Performing gProfiler KEGG search of 55 genes against hsapiens.## KEGG search found 2 hits.## Performing gProfiler REAC search of 55 genes against hsapiens.## REAC search found 0 hits.## Performing gProfiler MI search of 55 genes against hsapiens.## MI search found 0 hits.## Performing gProfiler TF search of 55 genes against hsapiens.## TF search found 2 hits.## Performing gProfiler CORUM search of 55 genes against hsapiens.## CORUM search found 0 hits.## Performing gProfiler HP search of 55 genes against hsapiens.## HP search found 1 hits.down_gp[["pvalue_plots"]][["bpp_plot_over"]]
up_goseq <- simple_goseq(sig_genes=lfs_up, go_db=hs_go[["go"]], length_db=hs_lengths)## Using the row names of your table.## Found 108 genes out of 108 from the sig_genes in the go_db.## Found 108 genes out of 108 from the sig_genes in the length_db.## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columns## simple_goseq(): Calculating q-values## simple_goseq(): Filling godata with terms, this is slow.## Testing that go categories are defined.## Removing undefined categories.## Gathering synonyms.## Gathering category definitions.## simple_goseq(): Making pvalue plots for the ontologies.
up_goseq[["pvalue_plots"]][["bpp_plot_over"]]
down_goseq <- simple_goseq(sig_genes=lfs_down, go_db=hs_go[["go"]], length_db=hs_lengths)## Using the row names of your table.## Found 55 genes out of 55 from the sig_genes in the go_db.## Found 55 genes out of 55 from the sig_genes in the length_db.## Using manually entered categories.## Calculating the p-values...## 'select()' returned 1:1 mapping between keys and columns## simple_goseq(): Calculating q-values## simple_goseq(): Filling godata with terms, this is slow.## Testing that go categories are defined.## Removing undefined categories.## Gathering synonyms.## Gathering category definitions.## simple_goseq(): Making pvalue plots for the ontologies.
down_goseq[["pvalue_plots"]][["bpp_plot_over"]]
up_topgo <- simple_topgo(sig_genes=lfs_up, go_db=hs_go[["go"]])## simple_topgo(): Set densities=TRUE for ontology density plots.up_topgo[["pvalue_plots"]][["bpp_plot_over"]]
down_topgo <- simple_topgo(sig_genes=lfs_down, go_db=hs_go[["go"]])## simple_topgo(): Set densities=TRUE for ontology density plots.down_topgo[["pvalue_plots"]][["bpp_plot_over"]]
up_cp <- simple_clusterprofiler(sig_genes=lfs_up, do_david=FALSE, do_gsea=FALSE, orgdb="org.Hs.eg.db", fc_column="deseq_logfc")## Testing available OrgDb keytypes for the best mapping to entrez.## Chose keytype: OMIM for all genes because it had 130 out of 61217 genes.## Chose keytype: ENSEMBL for sig genes because it had 108 out of 108 genes.## Unable to find the fold-change column in the de table, not doing gsea.## Calculating GO groups.## Found 151 MF, 527 BP, and 979 CC hits.## Calculating enriched GO groups.## Found 15 MF, 93 BP, and 9 CC enriched hits.## Found 107 matches between the significant gene list and kegg universe.## Performing KEGG analyses.## Reading KEGG annotation online:
##
## Reading KEGG annotation online:## Found 0 KEGG enriched hits.## Plotting results.up_cp[["pvalue_plots"]][["ego_all_bp"]]
up_cp[["plots"]][["dot_all_bp"]]
down_cp <- simple_clusterprofiler(sig_genes=lfs_down, do_david=FALSE, do_gsea=FALSE, orgdb="org.Hs.eg.db", fc_column="deseq_logfc")## Testing available OrgDb keytypes for the best mapping to entrez.## Chose keytype: OMIM for all genes because it had 130 out of 61217 genes.## Chose keytype: ENSEMBL for sig genes because it had 55 out of 55 genes.## Unable to find the fold-change column in the de table, not doing gsea.## Calculating GO groups.## Found 151 MF, 527 BP, and 979 CC hits.## Calculating enriched GO groups.## Found 16 MF, 18 BP, and 7 CC enriched hits.## Found 54 matches between the significant gene list and kegg universe.## Performing KEGG analyses.## Found 0 KEGG enriched hits.## Plotting results.down_cp[["pvalue_plots"]][["ego_all_bp"]]
down_cp[["plots"]][["dot_all_bp"]]
pander::pander(sessionInfo())R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C
attached base packages: grid, stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base
other attached packages: topGO(v.2.40.0), SparseM(v.1.78), edgeR(v.3.30.3), limma(v.3.44.3), ruv(v.0.9.7.1), Homo.sapiens(v.1.3.1), TxDb.Hsapiens.UCSC.hg19.knownGene(v.3.2.2), org.Hs.eg.db(v.3.11.4), GO.db(v.3.11.4), OrganismDbi(v.1.30.0), GenomicFeatures(v.1.40.1), GenomicRanges(v.1.40.0), GenomeInfoDb(v.1.24.2), GSEABase(v.1.50.1), graph(v.1.66.0), annotate(v.1.66.0), XML(v.3.99-0.4), xCell(v.1.1.0), org.Cfasciculata.Cf.Cl.v46.eg.db(v.2020.07), org.Lmexicana.MHOMGT2001U1103.v46.eg.db(v.2020.07), org.Ldonovani.BPK282A1.v46.eg.db(v.2020.07), org.Lbraziliensis.MHOMBR75M2904.v46.eg.db(v.2020.07), org.Lpanamensis.MHOMCOL81L13.v46.eg.db(v.2020.07), org.Lmajor.Friedlin.v46.eg.db(v.2020.07), AnnotationDbi(v.1.50.1), IRanges(v.2.22.2), S4Vectors(v.0.26.1), futile.logger(v.1.4.3), hpgltools(v.1.0), testthat(v.2.3.2), Biobase(v.2.48.0) and BiocGenerics(v.0.34.0)
loaded via a namespace (and not attached): rappdirs(v.0.3.1), rtracklayer(v.1.48.0), AnnotationForge(v.1.30.1), R.methodsS3(v.1.8.0), tidyr(v.1.1.0), ggplot2(v.3.3.2), bit64(v.0.9-7), knitr(v.1.29), R.utils(v.2.9.2), DelayedArray(v.0.14.0), data.table(v.1.12.8), KEGGREST(v.1.28.0), RCurl(v.1.98-1.2), doParallel(v.1.0.15), generics(v.0.0.2), preprocessCore(v.1.50.0), callr(v.3.4.3), cowplot(v.1.0.0), lambda.r(v.1.2.4), usethis(v.1.6.1), RSQLite(v.2.2.0), europepmc(v.0.4), rBiopaxParser(v.2.28.0), bit(v.1.1-15.2), enrichplot(v.1.8.1), xml2(v.1.3.2), httpuv(v.1.5.4), SummarizedExperiment(v.1.18.2), assertthat(v.0.2.1), viridis(v.0.5.1), xfun(v.0.15), hms(v.0.5.3), evaluate(v.0.14), promises(v.1.1.1), DEoptimR(v.1.0-8), fansi(v.0.4.1), progress(v.1.2.2), caTools(v.1.18.0), dbplyr(v.1.4.4), geneplotter(v.1.66.0), igraph(v.1.2.5), DBI(v.1.1.0), purrr(v.0.3.4), ellipsis(v.0.3.1), dplyr(v.1.0.0), backports(v.1.1.8), biomaRt(v.2.44.1), vctrs(v.0.3.1), remotes(v.2.1.1), withr(v.2.2.0), ggforce(v.0.3.2), triebeard(v.0.3.0), robustbase(v.0.93-6), AnnotationHubData(v.1.18.0), GenomicAlignments(v.1.24.0), prettyunits(v.1.1.1), DOSE(v.3.14.0), crayon(v.1.3.4), genefilter(v.1.70.0), pkgconfig(v.2.0.3), labeling(v.0.3), tweenr(v.1.0.1), nlme(v.3.1-148), pkgload(v.1.1.0), devtools(v.2.3.0), rlang(v.0.4.7), lifecycle(v.0.2.0), downloader(v.0.4), BiocFileCache(v.1.12.0), directlabels(v.2020.6.17), AnnotationHub(v.2.20.0), rprojroot(v.1.3-2), polyclip(v.1.10-0), GSVA(v.1.36.2), matrixStats(v.0.56.0), Matrix(v.1.2-18), urltools(v.1.7.3), boot(v.1.3-25), base64enc(v.0.1-3), geneLenDataBase(v.1.24.0), ggridges(v.0.5.2), processx(v.3.4.3), png(v.0.1-7), viridisLite(v.0.3.0), bitops(v.1.0-6), R.oo(v.1.23.0), KernSmooth(v.2.23-17), pander(v.0.6.3), Biostrings(v.2.56.0), blob(v.1.2.1), stringr(v.1.4.0), qvalue(v.2.20.0), gProfileR(v.0.7.0), gridGraphics(v.0.5-0), scales(v.1.1.1), memoise(v.1.1.0), magrittr(v.1.5), plyr(v.1.8.6), gplots(v.3.0.4), gdata(v.2.18.0), zlibbioc(v.1.34.0), compiler(v.4.0.0), scatterpie(v.0.1.4), RColorBrewer(v.1.1-2), lme4(v.1.1-23), DESeq2(v.1.28.1), Rsamtools(v.2.4.0), cli(v.2.0.2), XVector(v.0.28.0), ps(v.1.3.3), formatR(v.1.7), MASS(v.7.3-51.6), mgcv(v.1.8-31), tidyselect(v.1.1.0), stringi(v.1.4.6), EuPathDB(v.1.6.0), highr(v.0.8), yaml(v.2.2.1), GOSemSim(v.2.14.0), askpass(v.1.1), locfit(v.1.5-9.4), ggrepel(v.0.8.2), biocViews(v.1.56.1), fastmatch(v.1.1-0), tools(v.4.0.0), rstudioapi(v.0.11), foreach(v.1.5.0), gridExtra(v.2.3), Rtsne(v.0.15), farver(v.2.0.3), ggraph(v.2.0.3), digest(v.0.6.25), rvcheck(v.0.1.8), BiocManager(v.1.30.10), pracma(v.2.2.9), shiny(v.1.5.0), quadprog(v.1.5-8), Rcpp(v.1.0.5), BiocVersion(v.3.11.1), later(v.1.1.0.1), httr(v.1.4.1), colorspace(v.1.4-1), rvest(v.0.3.5), fs(v.1.4.2), splines(v.4.0.0), RBGL(v.1.64.0), statmod(v.1.4.34), PROPER(v.1.20.0), graphlayouts(v.0.7.0), shinythemes(v.1.1.2), ggplotify(v.0.0.5), sessioninfo(v.1.1.1), xtable(v.1.8-4), jsonlite(v.1.7.0), nloptr(v.1.2.2.2), futile.options(v.1.0.1), tidygraph(v.1.2.0), corpcor(v.1.6.9), Vennerable(v.3.1.0.9000), R6(v.2.4.1), RUnit(v.0.4.32), pillar(v.1.4.6), htmltools(v.0.5.0), mime(v.0.9), glue(v.1.4.1), fastmap(v.1.0.1), minqa(v.1.2.4), clusterProfiler(v.3.16.0), BiocParallel(v.1.22.0), interactiveDisplayBase(v.1.26.3), codetools(v.0.2-16), fgsea(v.1.14.0), pkgbuild(v.1.1.0), lattice(v.0.20-41), tibble(v.3.0.3), sva(v.3.36.0), pbkrtest(v.0.4-8.6), BiasedUrn(v.1.07), curl(v.4.3), colorRamps(v.2.3), gtools(v.3.8.2), zip(v.2.0.4), openxlsx(v.4.1.5), openssl(v.1.4.2), survival(v.3.2-3), rmarkdown(v.2.3), desc(v.1.2.0), munsell(v.0.5.0), fastcluster(v.1.1.25), DO.db(v.2.9), GenomeInfoDbData(v.1.2.3), goseq(v.1.40.0), iterators(v.1.0.12), variancePartition(v.1.18.2), reshape2(v.1.4.4) and gtable(v.0.3.0)
message(paste0("This is hpgltools commit: ", get_git_commit()))## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 29c7dde0a24277fa95ac712188385a347c14c6f1## This is hpgltools commit: Thu Jul 9 10:37:52 2020 -0400: 29c7dde0a24277fa95ac712188385a347c14c6f1this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))## Saving to macrophage_all_analyses_202007-v20200706.rda.xztmp <- sm(saveme(filename=this_save))---
title: "L.panamensis 202007: Differential Expression in human macrophages."
author: "atb abelew@gmail.com"
date: "`r Sys.Date()`"
output:
  rmdformats::readthedown:
    code_download: true
    code_folding: show
    df_print: paged
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    width: 300
    keep_md: false
    mode: selfcontained
    toc_float: true
  BiocStyle::html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    toc_float: true
  html_document:
    code_download: true
    code_folding: show
    fig_caption: true
    fig_height: 7
    fig_width: 7
    highlight: tango
    keep_md: false
    mode: selfcontained
    number_sections: true
    self_contained: true
    theme: readable
    toc: true
    toc_float:
      collapsed: false
      smooth_scroll: false
---

<style type="text/css">
body, td {
  font-size: 16px;
}
code.r{
  font-size: 16px;
}
pre {
 font-size: 16px
}
</style>

```{r options, include=FALSE}
library("hpgltools")
tt <- devtools::load_all("/data/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=10))
rundate <- format(Sys.Date(), format="%Y%m%d")
previous_file <- "index.Rmd"
ver <- "20200706"

##tmp <- sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz")))
rmd_file <- "macrophage_all_analyses_202007.Rmd"
```

# Annotation version: `r ver`

## Genome annotation input

There are a few methods of importing annotation data into R.  The following are
two attempts, the second is currently being used in these analyses.

```{r make_orgdb}
meta <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")

lm_entry <- EuPathDB::get_eupath_entry(species="Leishmania major", metadata=meta)
lp_entry <- EuPathDB::get_eupath_entry(species="Leishmania panamensis", metadata=meta)
lmex_entry <- EuPathDB::get_eupath_entry(species="Leishmania mexicana", metadata=meta)
lama_entry <- EuPathDB::get_eupath_entry(species="Leishmania amazonensis", metadata=meta)
lb_entry <- EuPathDB::get_eupath_entry(species="2904", metadata=meta)
ld_entry <- EuPathDB::get_eupath_entry(species="donovani", metadata=meta)
crit_entry <- EuPathDB::get_eupath_entry(species="Crith", metadata=meta)

testing_panamensis <- EuPathDB::make_eupath_orgdb(entry=lp_entry)
testing_braziliensis <- EuPathDB::make_eupath_orgdb(entry=lb_entry)
testing_donovani <- EuPathDB::make_eupath_orgdb(entry=ld_entry)
testing_mexicana <- EuPathDB::make_eupath_orgdb(entry=lmex_entry)
testing_major <- EuPathDB::make_eupath_orgdb(entry=lm_entry)
testing_crith <- EuPathDB::make_eupath_orgdb(entry=crit_entry)
```

Assuming the above packages got created, we may load them and extract the
annotation data.

```{r lpanamensis_orgdb}
wanted_fields <- c("annot_cds_length", "annot_chromosome", "annot_gene_entrez_id",
                   "annot_gene_name", "annot_strand", "gid", "go_go_id",
                   "go_go_term_name", "go_ontology",
                   "interpro_description" ,"interpro_e_value", "type_gene_type")

lm_org <- sm(EuPathDB::load_eupath_annotations(query=lm_entry))
lp_org <- sm(EuPathDB::load_eupath_annotations(query=lp_entry))
lb_org <- sm(EuPathDB::load_eupath_annotations(query=lb_entry))
ld_org <- sm(EuPathDB::load_eupath_annotations(query=ld_entry))
lmex_org <- sm(EuPathDB::load_eupath_annotations(query=lmex_entry))
cf_ort <- sm(EuPathDB::load_eupath_annotations(query=crit_entry))
```

## Read a gff file

In contrast, it is possible to load most annotations of interest directly from
the gff files used in the alignments.  More in-depth information for the human
transcriptome may be extracted from biomart.

```{r genome_input}
## The old way of getting genome/annotation data
lp_gff <- "reference/lpanamensis.gff"
lb_gff <- "reference/lbraziliensis.gff"
hs_gff <- "reference/hsapiens.gtf"

lp_fasta <- "reference/lpanamensis.fasta.xz"
lb_fasta <- "reference/lbraziliensis.fasta.xz"
hs_fasta <- "reference/hsapiens.fasta.xz"

lp_annotations <- sm(load_gff_annotations(lp_gff, type="gene"))
rownames(lp_annotations) <- paste0("exon_", lp_annotations$web_id, ".1")

lb_annotations <- sm(load_gff_annotations(lb_gff, type="gene"))

hs_gff_annot <- sm(load_gff_annotations(hs_gff, id_col="gene_id"))
hs_annotations <- sm(load_biomart_annotations())$annotation
hs_annotations$ID <- hs_annotations$geneID
rownames(hs_annotations) <- make.names(hs_annotations[["ensembl_gene_id"]], unique=TRUE)
dim(hs_annotations)

lp_size_dist <- plot_histogram(lp_annotations[["width"]])
lp_size_dist

hs_size_dist <- plot_histogram(hs_annotations[["cds_length"]])
hs_size_dist +
  ggplot2::scale_x_continuous(limits=c(0, 20000))
```

## Extracting Cell Types

Maria Adelaida requested adding the xCell cell types to the data.

```{r xCell_data}
library(xCell)
data("xCell.data", package="xCell")
summary(xCell.data)
library(GSEABase)
details(xCell.data$signatures[[1]])

sigs <- xCell.data$signatures
head(names(sigs), n=10)
## Here we see that the signatures are encoded as 3 element lists, the first element is the
## cell type, followed by source, followed by replicate.txt.
cell_types <- unlist(lapply(strsplit(x=names(sigs), split="%"), function(x) { x[[1]] }))
cell_sources <- unlist(lapply(strsplit(x=names(sigs), split="%"), function(x) { x[[2]] }))
type_fact <- as.factor(cell_types)
types <- levels(type_fact)

celltypes_to_genes <- list()
for (c in 1:length(types)) {
  type <- types[c]
  idx <- cell_types == type
  set <- sigs[idx]
  genes <- set %>%
    geneIds() %>%
    unlist()
  celltypes_to_genes[[type]] <- as.character(genes)
}
genes_to_celltypes <- Biobase::reverseSplit(celltypes_to_genes)

g2c_df <- data.frame(row.names=unique(names(genes_to_celltypes)))
g2c_df[["found"]] <- 0
for (c in 1:length(celltypes_to_genes)) {
  celltype_name <- names(celltypes_to_genes)[[c]]
  message("Starting ", c, ": ", celltype_name)
  celltype_column <- as.data.frame(celltypes_to_genes[[c]])
  colnames(celltype_column) <- celltype_name
  rownames(celltype_column) <- make.names(celltype_column[[1]], unique=TRUE)
  celltype_column[[1]] <- TRUE
  g2c_df <- merge(g2c_df, celltype_column, by="row.names", all.x=TRUE)
  rownames(g2c_df) <- g2c_df[[1]]
  g2c_df <- g2c_df[, -1]
}
head(g2c_df)
na_idx <- is.na(g2c_df)
g2c_df[na_idx] <- FALSE
```

## Getting ontology data

Annotation for gene ontologies may be gathered from a similarly large number of
sources. The following are a couple.

```{r ontology1}
## Try using biomart
hs_go <- sm(load_biomart_go())
## or the org.Hs.eg.db sqlite database
tt <- sm(library("Homo.sapiens"))
hs <- Homo.sapiens
##hs_go_ensembl <- load_orgdb_go(hs, hs_annotations$geneID)
##dim(hs_go_biomart)
##dim(hs_go_ensembl)
##hs_goids <- hs_go_biomart

## While testing, I called this desc, that will need to change.
##lp_tooltips <- make_tooltips(lp_annotations)
##lb_tooltips <- make_tooltips(lb_annotations)

lp_lengths <- lp_annotations[, c("ID", "width")]
lb_lengths <- lb_annotations[, c("ID", "width")]
hs_lengths <- hs_annotations[, c("ensembl_gene_id", "cds_length")]
colnames(hs_lengths) <- c("ID", "width")

lp_goids <- read.csv(file="reference/lpan_go.txt.xz", sep="\t", header=FALSE)
lb_goids <- read.csv(file="reference/lbraz_go.txt.xz", sep="\t", header=FALSE)
colnames(lp_goids) <- c("ID","GO","ont","name","source","tag")
colnames(lb_goids) <- c("ID","GO","ont","name","source","tag")
```

# Putting the pieces together

The macrophage experiment has samples across 2 contexts, the host and parasite.
The following block sets up one experiment for each.  If you open the
all_samples-species.xlsx files, you will note immediately that a few different
attempts were made at ascertaining the most likely experimental factors that
contributed to the readily apparent batch effects.

## The human transcriptome mappings

Keep in mind that if I change the experimental design with new annotations, I
must therefore regenerate the following.

```{r hs_expt}
hs_final_annotations <- hs_annotations
hs_final_annotations <- hs_final_annotations[, c("ensembl_transcript_id", "ensembl_gene_id", "cds_length",
                                                 "hgnc_symbol", "description", "gene_biotype")]
hs_final_annotations$rn <- rownames(hs_final_annotations)
note <- "New experimental design factors by snp added 2016-09-20"
hs_final_annotations <- merge(hs_final_annotations, g2c_df,
                              by.x="hgnc_symbol", by.y="row.names", all.x=TRUE)
rownames(hs_final_annotations) <- hs_final_annotations$rn
hs_final_annotations$rn <- NULL
na_idx <- is.na(hs_final_annotations$xcell_types)
hs_final_annotations[na_idx, "xcell_types"] <- ""

hs_macr <- create_expt(
    metadata=glue::glue("sample_sheets/macrophage_samples_{ver}.xlsx"),
    gene_info=hs_final_annotations,
    file_column="humanfile",
    notes=note)
hs_annotations <- fData(hs_macr)
undef_idx <- hs_annotations == "undefined"
hs_annotations[undef_idx] <- FALSE
fData(hs_macr[["expressionset"]]) <- hs_annotations

knitr::kable(head(hs_macr$design, n=1))

##cds_entries <- fData(hs_macr)
##cds_entries <- cds_entries[["gene_biotype"]] == "protein_coding"
##hs_cds_macr <- hs_macr
##hs_cds_macr$expressionset <- hs_cds_macr$expressionset[cds_entries, ]
##new_cds_entries <- fData(hs_cds_macr)
hs_cds_macr <- exclude_genes_expt(hs_macr, method="keep",
                                  column="gene_biotype",
                                  patterns="protein_coding")
```

## The parasite transcriptome mappings

```{r parasite_expt}
lp_macr <- sm(create_expt(
    metadata=glue::glue("sample_sheets/macrophage_samples_{ver}.xlsx"),
    gene_info=lp_annotations, file_column="parasitefile"))
knitr::kable(head(lp_macr$design, n=3),
             caption="The first three rows of the parasite experimental design.")
```

# Supplemental Table 1

Table S1 is going to be a summary of the metadata in all_samples-combined
This may also include some of the numbers regarding mapping %, etc.

Wanted columns:

* Sample ID:  HPGLxxxx
* Donor Code: TM130 or PG1xx
* Cell Type:  Macrophage or PBMC
* Infection Status:  Infected or Uninfected
* Disease Outcome:  Chronic or Self-Healing or NA
* Batch: A or B (macrophage); NA for PBMC
* Number of reads that passed Illumina filter
* Number of reads after trimming
* Number of reads mapped - human
* % reads mapped - human
* Number of reads mapped - L.panamensis
* % reads mapped - L.panamensis

This table is maintained as an excel file in the sample_sheets/ directory.

# Sample Estimation, Macrophages: `r ver`

This document is concerned with analyzing RNAseq data of human and parasite during the infection of
human macrophages.  A few different strains of L. panamensis were used; the experiment is therefore
segregated into groups named 'self-healing', 'chronic', and 'uninfected'.  Two separate sets of
libraries were generated, one earlier set with greater coverage, and a later set including only 1
uninfected sample, and 2-3 chronic samples.

## Extract the macrophage experiment

The following subset operation extract the samples used for the macrophage experiment. The next
three lines then change the colors from the defaults.

```{r macrophages}
new_colors <- c("#009900", "#990000", "#000099")
names(new_colors) <- c("uninf", "chr", "sh")

hs_macr <- set_expt_colors(hs_macr, colors=new_colors)
labels <- as.character(pData(hs_macr)[["label"]])
hs_macr <- set_expt_samplenames(hs_macr, labels)

hs_cds_macr <- set_expt_colors(hs_cds_macr, colors=new_colors)
hs_cds_macr <- set_expt_samplenames(hs_cds_macr, labels)
```

# Compare methods

In our meeting this morning (20190708), Najib and Maria Adelaida asked for how
the data looks depending on batch methodology.

```{r batch_methods}
start <- normalize_expt(hs_cds_macr, filter=TRUE, convert="cpm", norm="quant", transform="log2")
pp(file="cpm_quant_filter_pca.png", image=plot_pca(start)$plot)

limma_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                              convert="cpm", norm="quant",
                              transform="log2", batch="limma")
pp(file="lcqf_limma.png", image=plot_pca(limma_batch)$plot)

pca_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                            convert="cpm", norm="quant",
                            transform="log2", batch="pca")
pp(file="lcqf_pca.png", image=plot_pca(pca_batch)$plot)

svaseq_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                               convert="cpm",
                               transform="log2", batch="svaseq")
pp(file="lcqf_svaseq.png", image=plot_pca(svaseq_batch)$plot)

sva_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                            convert="cpm",
                            transform="log2", batch="fsva")
pp(file="lcqf_sva.png", image=plot_pca(sva_batch)$plot)

combat_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                               convert="cpm",
                               transform="log2", batch="combat")
pp(file="lcqf_combat.png", image=plot_pca(combat_batch)$plot)

combatnp_batch <- normalize_expt(hs_cds_macr, filter=TRUE,
                                 convert="cpm",
                                 transform="log2", batch="combat_noprior")
pp(file="lcqf_combatnp.png", image=plot_pca(combatnp_batch)$plot)
```

# Figure S1

Figure S1 should include nice versions of the sample metrics.
The normalization chosen is batch-in-model.

First, however, we will make some plots of the raw data.

Sample names are going to be 'infectionstate_strainnumber' : chr_7721

* Panel A: Library sizes.
* Panel B: Heatmap distance raw.
* Panel C: PCA
* Panel D: TSNE

```{r fig_s1_write, fig.show="hide"}
fig_s1 <- sm(write_expt(
    hs_cds_macr, norm="raw", violin=FALSE, convert="cpm",
    transform="log2", batch="svaseq", filter=TRUE,
    excel=paste0("excel/figure_s1_sample_estimation-v", ver, ".xlsx")))

pp(file="fig_s1a_libsizes.tif", image=fig_s1$raw_libsize)
pp(file="fig_s1b_heatmap.tif", image=fig_s1$norm_disheat)
pp(file="fig_s1c_raw_pca.tif", image=fig_s1$raw_scaled_pca)
pp(file="fig_1a_norm_pca.tif", image=fig_s1$norm_pca)
```

# Differential Expression, Macrophage: `r ver`

# Differential expression analyses

The most likely batch method from the above is svaseq.  Let us perform
differential expression analyses with it along with a few other methods.

```{r setup_de_norm, fig.show="hide"}
hs_contrasts <- list(
    "macro_chr-sh" = c("chr","sh"),
    "macro_chr-nil" = c("chr","uninf"),
    "macro_sh-nil" = c("sh", "uninf"))
## Set up the data used in the comparative contrast sets.
```

## No batch in the model

### Set up no batch

Print a reminder of what we can expect when doing this with no batch information.

```{r nobatch_setup}
hs_macr_lowfilt <- sm(normalize_expt(hs_cds_macr, filter=TRUE))
hs_lowfilt_pca <- sm(plot_pca(hs_cds_macr, transform="log2"))
hs_lowfilt_pca$plot
```

```{r macro_nobatch1, fig.show="hide"}
hs_macr_nobatch <- all_pairwise(input=hs_cds_macr, model_batch=FALSE,
                                limma_method="robust")
## wow, all tools including basic agree almost completely
medians_by_condition <- hs_macr_nobatch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_contr-v{ver}.xlsx")
hs_macr_nobatch_tables <- sm(combine_de_tables(hs_macr_nobatch,
                                               excel=excel_file,
                                               keepers=hs_contrasts,
                                               extra_annot=medians_by_condition))
excel_file <- glue::glue("excel/{rundate}_hs_macr_nobatch_sig-v{ver}.xlsx")
hs_macr_nobatch_sig <- sm(extract_significant_genes(hs_macr_nobatch_tables,
                                                    excel=excel_file,
                                                    according_to="all"))
```

## Batch in the model

### Batch setup

```{r batch_setup}
hs_lowfilt_batch_pca <- sm(plot_pca(hs_cds_macr, transform="log2",
                                    convert="cpm", batch="limma", filter=TRUE))
hs_lowfilt_batch_pca$plot
```

In this  attempt, we add a batch factor in the experimental model and see how it does.

```{r macro_batch1, fig.show="hide"}
## Here just let all_pairwise run on filtered data and do its normal ~ 0 + condition + batch analyses
hs_macr_batch <- all_pairwise(input=hs_cds_macr, batch=TRUE, limma_method="robust")
medians_by_condition <- hs_macr_batch$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_contr-v{ver}.xlsx")
hs_macr_batch_tables <- combine_de_tables(
  hs_macr_batch,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition,
  excel=excel_file)
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_sig-v{ver}.xlsx")
hs_macr_batch_sig <- extract_significant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq")
excel_file <- glue::glue("excel/{rundate}_hs_macr_batchmodel_abund-v{ver}.xlsx")
hs_macr_batch_abun <- sm(extract_abundant_genes(
  hs_macr_batch_tables, excel=excel_file,
  according_to="deseq"))
```

## SVA

### sva setup

```{r sva_setup}
hs_lowfilt_sva_pca <- sm(plot_pca(hs_cds_macr, transform="log2",
                                  convert="cpm", batch="svaseq", filter=TRUE))
hs_lowfilt_sva_pca$plot
```

In this  attempt, we tell all pairwise to invoke svaseq.

```{r macro_sva, fig.show="hide"}
hs_macr_sva <- all_pairwise(input=hs_cds_macr, model_batch="svaseq", filter=TRUE, limma_method="robust")
medians_by_condition <- hs_macr_sva$basic$medians
excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_contr-v{ver}.xlsx")
hs_macr_sva_tables <- combine_de_tables(
  hs_macr_sva,
  keepers=hs_contrasts,
  extra_annot=medians_by_condition,
  excel=excel_file)
excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_sig-v{ver}.xlsx")
hs_macr_sva_sig <- extract_significant_genes(
    hs_macr_sva_tables, excel=excel_file,
    according_to="deseq")
excel_file <- glue::glue("excel/{rundate}_hs_macr_svamodel_abund-v{ver}.xlsx")
hs_macr_sva_abun <- sm(extract_abundant_genes(
    hs_macr_sva_tables, excel=excel_file,
    according_to="deseq"))
```

## Combat

As you know, combat actually changes the data, so it will require a slightly
different setup.

### combat setup

```{r combat_setup}
combatnp_input <- normalize_expt(hs_cds_macr, filter=TRUE,
                                 convert="cpm",
                                 batch="combat_noprior")
```

In this  attempt, we tell all pairwise to invoke svaseq.

```{r macro_combat, fig.show="hide"}
hs_macr_combat <- all_pairwise(input=combatnp_input, model_batch=FALSE,
                               force=TRUE, limma_method="robust")
excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_contr-v{ver}.xlsx")
hs_macr_combat_tables <- combine_de_tables(
    hs_macr_combat,
    keepers=hs_contrasts,
    extra_annot=medians_by_condition,
    excel=excel_file)
excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_sig-v{ver}.xlsx")
hs_macr_combat_sig <- extract_significant_genes(
  hs_macr_combat_tables, excel=excel_file,
  according_to="deseq")
excel_file <- glue::glue("excel/{rundate}_hs_macr_combat_abund-v{ver}.xlsx")
hs_macr_combat_abun <- sm(extract_abundant_genes(
  hs_macr_combat_tables, excel=excel_file,
  according_to="deseq"))
```

## Compare these three results

```{r compare_de_results}
nobatch_batch <- compare_de_results(first=hs_macr_nobatch_tables,
                                    second=hs_macr_batch_tables)
nobatch_batch$logfc

batch_sva <- compare_de_results(first=hs_macr_batch_tables,
                                second=hs_macr_sva_tables)
batch_sva$logfc

batch_combat <- compare_de_results(first=hs_macr_batch_tables,
                                   second=hs_macr_combat_tables)
batch_combat$logfc

sva_combat <- compare_de_results(first=hs_macr_sva_tables,
                                 second=hs_macr_combat_tables)
sva_combat$logfc
## Interesting how much sva and combat disagree.
```

## Two likely volcano plots

```{r table_s2}
batchmodel_volcano <- plot_volcano_de(
    table=hs_macr_batch_tables[["data"]][["macro_chr-sh"]],
    color_by="state",
    fc_col="deseq_logfc",
    p_col="deseq_adjp",
    shapes_by_state=FALSE,
    line_position="top")
batchmodel_volcano$plot

svamodel_volcano <- plot_volcano_de(
    table=hs_macr_sva_tables[["data"]][["macro_chr-sh"]],
    color_by="state",
    fc_col="deseq_logfc",
    p_col="deseq_adjp",
    shapes_by_state=FALSE,
    line_position="top")
svamodel_volcano$plot
```

## PROPER

```{r proper}
hs_proper <- simple_proper(de_tables=hs_macr_batch_tables)
```

## Ontology searching against the sva results.

Recall that I made the variables 'hs_macr_sva_sig' and 'hs_macr_sva_abun' to
hold the results of the most significantly changed and abundant genes.

gProfiler is my favorite tool for ontology searching, however they recently had
a big update and split their code.  The new version has all sorts of cool toys,
but as of the last time I tried it, did not work.  Thus the following is still
using the older methods.

```{r ontology2}
lfs_up <- hs_macr_sva_sig[["deseq"]][["ups"]][["macro_chr-sh"]]
lfs_down <- hs_macr_sva_sig[["deseq"]][["downs"]][["macro_chr-sh"]]

up_gp <- simple_gprofiler(sig_genes=lfs_up, species="hsapiens")
up_gp[["pvalue_plots"]][["bpp_plot_over"]]
down_gp <- simple_gprofiler(sig_genes=lfs_down, species="hsapiens")
down_gp[["pvalue_plots"]][["bpp_plot_over"]]

up_goseq <- simple_goseq(sig_genes=lfs_up, go_db=hs_go[["go"]], length_db=hs_lengths)
up_goseq[["pvalue_plots"]][["bpp_plot_over"]]
down_goseq <- simple_goseq(sig_genes=lfs_down, go_db=hs_go[["go"]], length_db=hs_lengths)
down_goseq[["pvalue_plots"]][["bpp_plot_over"]]

up_topgo <- simple_topgo(sig_genes=lfs_up, go_db=hs_go[["go"]])
up_topgo[["pvalue_plots"]][["bpp_plot_over"]]
down_topgo <- simple_topgo(sig_genes=lfs_down, go_db=hs_go[["go"]])
down_topgo[["pvalue_plots"]][["bpp_plot_over"]]

up_cp <- simple_clusterprofiler(sig_genes=lfs_up, do_david=FALSE, do_gsea=FALSE, orgdb="org.Hs.eg.db", fc_column="deseq_logfc")
up_cp[["pvalue_plots"]][["ego_all_bp"]]
up_cp[["plots"]][["dot_all_bp"]]
down_cp <- simple_clusterprofiler(sig_genes=lfs_down, do_david=FALSE, do_gsea=FALSE, orgdb="org.Hs.eg.db", fc_column="deseq_logfc")
down_cp[["pvalue_plots"]][["ego_all_bp"]]
down_cp[["plots"]][["dot_all_bp"]]
```


```{r saveme}
pander::pander(sessionInfo())
message(paste0("This is hpgltools commit: ", get_git_commit()))
this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))
tmp <- sm(saveme(filename=this_save))
```
