L. panamensis 20180822: Annotation data.

1 Annotation version: 20200220

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/U1103

lama_entry <- EuPathDB::get_eupath_entry(species="Leishmania amazonensis", metadata=meta)

## Found: Leishmania amazonensis MHOM/BR/71973/M2269

lb_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-Cl

testing_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(entry=lm_entry))

lp_org <- sm(EuPathDB::load_eupath_annotations(entry=lp_entry))

lb_org <- sm(EuPathDB::load_eupath_annotations(entry=lb_entry))

ld_org <- sm(EuPathDB::load_eupath_annotations(entry=ld_entry))

lmex_org <- sm(EuPathDB::load_eupath_annotations(entry=lmex_entry))

cf_ort <- sm(EuPathDB::load_eupath_annotations(entry=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     12

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))

## 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-            character

library(GSEABase)

## Loading required package: annotate

## Loading required package: XML

## Loading required package: graph

## 
## Attaching package: 'graph'

## The following object is masked from 'package:XML':
## 
##     addNode

details(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: Tregs

head(g2c_df)

##      found aDC Adipocytes Astrocytes B-cells Basophils CD4+ memory T-cells
## A1CF     0  NA         NA         NA      NA        NA                  NA
##      CD4+ naive T-cells CD4+ T-cells CD4+ Tcm CD4+ Tem CD8+ naive T-cells
## A1CF                 NA           NA       NA       NA                 NA
##      CD8+ T-cells CD8+ Tcm CD8+ Tem cDC Chondrocytes
## A1CF           NA       NA       NA  NA           NA
##      Class-switched memory B-cells CLP CMP DC Endothelial cells Eosinophils
## A1CF                            NA  NA  NA NA                NA          NA
##      Epithelial cells Erythrocytes Fibroblasts GMP Hepatocytes HSC iDC
## A1CF               NA           NA          NA  NA        TRUE  NA  NA
##      Keratinocytes ly Endothelial cells Macrophages Macrophages M1
## A1CF            NA                   NA          NA             NA
##      Macrophages M2 Mast cells Megakaryocytes Melanocytes Memory B-cells MEP
## A1CF             NA         NA             NA          NA             NA  NA
##      Mesangial cells Monocytes MPP MSC mv Endothelial cells Myocytes
## A1CF              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
##      Plasma cells Platelets Preadipocytes pro B-cells Sebocytes Skeletal muscle
## A1CF           NA        NA            NA          NA        NA              NA
##      Smooth muscle Tgd cells Th1 cells Th2 cells Tregs
## A1CF            NA        NA        NA        NA    NA
##  [ reached 'max' / getOption("max.print") -- omitted 5 rows ]

na_idx <- is.na(g2c_df)
g2c_df[na_idx] <- FALSE

1.4 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_biomart <- 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_expt <- sm(create_expt("sample_sheets/all_samples-combined.xlsx",
                          gene_info=hs_final_annotations,
                          file_column="humanfile",
                          notes=note))
hs_annotations <- fData(hs_expt)
undef_idx <- hs_annotations == "undefined"
hs_annotations[undef_idx] <- FALSE
fData(hs_expt[["expressionset"]]) <- hs_annotations

knitr::kable(head(hs_expt$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	sampleid.1	file
HPGL0241	HPGL0241	none	macrophage	TM130-Nil (Blue label)	Nil	uninf	a	a	undef	undef	undef	none	uninf_1	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	HPGL0241	null

cds_entries <- fData(hs_expt)
cds_entries <- cds_entries[["gene_biotype"]] == "protein_coding"
hs_cds_expt <- hs_expt
hs_cds_expt$expressionset <- hs_cds_expt$expressionset[cds_entries, ]
new_cds_entries <- fData(hs_cds_expt)

2.2 The parasite transcriptome mappings

parasite_expt <- sm(create_expt("sample_sheets/all_samples-combined.xlsx",
                                gene_info=lp_annotations, file_column="parasitefile"))
knitr::kable(head(parasite_expt$design, n=3),
             caption="The first three rows of the parasite experimental design.")

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	sampleid.1	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	0.0486111111111111	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	HPGL0242	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	0.0486111111111111	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	HPGL0243	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	0.0486111111111111	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	HPGL0244	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

Use the Tcruzi colors.

• A1 is a large title: “Macrophage Samples”
• Row 2 is the blue column headings
• 3-m contains Macrophage metadata
• m+1 is blank
• m+2 is a large title: “PBMC Samples”
• m+3-n contains PBMC metadata

4 End

At this point, we should have everything necessary to perform the various analyses of the 4 sub-experiments. So save the current data for reuse elsewhere.

The experimental design is available here.

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 2b76190adf865097c1c27606eeded8b57cef055d

## This is hpgltools commit: Fri Feb 14 11:21:34 2020 -0500: 2b76190adf865097c1c27606eeded8b57cef055d

this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))

## Saving to 01_annotation_20200220-v20200220.rda.xz

tmp <- saveme(filename=this_save)

## The savefile is: /mnt/sshfs/cbcbsub/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20200220-v20200220.rda.xz

## The file does not yet exist.

## The save string is: con <- pipe(paste0('pxz > /mnt/sshfs/cbcbsub/fs/cbcb-lab/nelsayed/scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20200220-v20200220.rda.xz'), 'wb'); save(list=ls(all.names=TRUE, envir=globalenv()),
##      envir=globalenv(), file=con, compress=FALSE); close(con)

pander::pander(sessionInfo())

R version 3.6.1 (2019-07-05)

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: stats4, parallel, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: Homo.sapiens(v.1.3.1), TxDb.Hsapiens.UCSC.hg19.knownGene(v.3.2.2), org.Hs.eg.db(v.3.10.0), GO.db(v.3.10.0), OrganismDbi(v.1.28.0), GenomicFeatures(v.1.38.2), GenomicRanges(v.1.38.0), GenomeInfoDb(v.1.22.0), GSEABase(v.1.48.0), graph(v.1.64.0), annotate(v.1.64.0), XML(v.3.99-0.3), xCell(v.1.1.0), org.Cfasciculata.Cf.Cl.v46.eg.db(v.2019.11), org.Lmexicana.MHOMGT2001U1103.v46.eg.db(v.2020.02), org.Ldonovani.BPK282A1.v46.eg.db(v.2019.11), org.Lbraziliensis.MHOMBR75M2904.v46.eg.db(v.2019.11), org.Lpanamensis.MHOMCOL81L13.v46.eg.db(v.2020.02), org.Lmajor.Friedlin.v46.eg.db(v.2020.02), AnnotationDbi(v.1.48.0), IRanges(v.2.20.2), S4Vectors(v.0.24.3), futile.logger(v.1.4.3), hpgltools(v.1.0), Biobase(v.2.46.0) and BiocGenerics(v.0.32.0)

loaded via a namespace (and not attached): rappdirs(v.0.3.1), rtracklayer(v.1.46.0), AnnotationForge(v.1.28.0), tidyr(v.1.0.2), ggplot2(v.3.2.1), bit64(v.0.9-7), knitr(v.1.28), DelayedArray(v.0.12.2), data.table(v.1.12.8), RCurl(v.1.98-1.1), doParallel(v.1.0.15), callr(v.3.4.2), cowplot(v.1.0.0), lambda.r(v.1.2.4), usethis(v.1.5.1), RSQLite(v.2.2.0), europepmc(v.0.3), rBiopaxParser(v.2.26.0), bit(v.1.1-15.2), enrichplot(v.1.6.1), xml2(v.1.2.2), httpuv(v.1.5.2), SummarizedExperiment(v.1.16.1), assertthat(v.0.2.1), viridis(v.0.5.1), xfun(v.0.12), hms(v.0.5.3), evaluate(v.0.14), promises(v.1.1.0), fansi(v.0.4.1), progress(v.1.2.2), caTools(v.1.18.0), dbplyr(v.1.4.2), igraph(v.1.2.4.2), DBI(v.1.1.0), geneplotter(v.1.64.0), purrr(v.0.3.3), ellipsis(v.0.3.0), dplyr(v.0.8.4), backports(v.1.1.5), biomaRt(v.2.42.0), vctrs(v.0.2.3), remotes(v.2.1.1), withr(v.2.1.2), ggforce(v.0.3.1), triebeard(v.0.3.0), AnnotationHubData(v.1.16.0), GenomicAlignments(v.1.22.1), prettyunits(v.1.1.1), DOSE(v.3.12.0), lazyeval(v.0.2.2), crayon(v.1.3.4), genefilter(v.1.68.0), pkgconfig(v.2.0.3), labeling(v.0.3), tweenr(v.1.0.1), nlme(v.3.1-144), pkgload(v.1.0.2), devtools(v.2.2.2), rlang(v.0.4.4), lifecycle(v.0.1.0), BiocFileCache(v.1.10.2), AnnotationHub(v.2.18.0), rprojroot(v.1.3-2), polyclip(v.1.10-0), GSVA(v.1.34.0), matrixStats(v.0.55.0), Matrix(v.1.2-18), urltools(v.1.7.3), boot(v.1.3-24), base64enc(v.0.1-3), ggridges(v.0.5.2), processx(v.3.4.2), viridisLite(v.0.3.0), bitops(v.1.0-6), KernSmooth(v.2.23-16), pander(v.0.6.3), Biostrings(v.2.54.0), blob(v.1.2.1), stringr(v.1.4.0), qvalue(v.2.18.0), gridGraphics(v.0.4-1), scales(v.1.1.0), memoise(v.1.1.0), magrittr(v.1.5), plyr(v.1.8.5), gplots(v.3.0.1.2), gdata(v.2.18.0), zlibbioc(v.1.32.0), compiler(v.3.6.1), RColorBrewer(v.1.1-2), lme4(v.1.1-21), Rsamtools(v.2.2.2), cli(v.2.0.1), XVector(v.0.26.0), ps(v.1.3.2), formatR(v.1.7), MASS(v.7.3-51.5), mgcv(v.1.8-31), tidyselect(v.1.0.0), stringi(v.1.4.6), EuPathDB(v.1.6.0), highr(v.0.8), yaml(v.2.2.1), GOSemSim(v.2.12.0), askpass(v.1.1), ggrepel(v.0.8.1), biocViews(v.1.54.0), grid(v.3.6.1), fastmatch(v.1.1-0), tools(v.3.6.1), rstudioapi(v.0.11), foreach(v.1.4.8), gridExtra(v.2.3), farver(v.2.0.3), ggraph(v.2.0.1), digest(v.0.6.24), rvcheck(v.0.1.7), BiocManager(v.1.30.10), shiny(v.1.4.0), pracma(v.2.2.9), Rcpp(v.1.0.3), BiocVersion(v.3.10.1), later(v.1.0.0), httr(v.1.4.1), colorspace(v.1.4-1), rvest(v.0.3.5), fs(v.1.3.1), splines(v.3.6.1), RBGL(v.1.62.1), graphlayouts(v.0.5.0), shinythemes(v.1.1.2), ggplotify(v.0.0.4), sessioninfo(v.1.1.1), xtable(v.1.8-4), jsonlite(v.1.6.1), nloptr(v.1.2.1), futile.options(v.1.0.1), tidygraph(v.1.1.2), testthat(v.2.3.1), R6(v.2.4.1), RUnit(v.0.4.32), pillar(v.1.4.3), htmltools(v.0.4.0), mime(v.0.9), glue(v.1.3.1), fastmap(v.1.0.1), minqa(v.1.2.4), clusterProfiler(v.3.14.3), BiocParallel(v.1.20.1), interactiveDisplayBase(v.1.24.0), codetools(v.0.2-16), fgsea(v.1.12.0), pkgbuild(v.1.0.6), lattice(v.0.20-40), tibble(v.2.1.3), sva(v.3.34.0), pbkrtest(v.0.4-7), curl(v.4.3), colorRamps(v.2.3), gtools(v.3.8.1), zip(v.2.0.4), openxlsx(v.4.1.4), openssl(v.1.4.1), survival(v.3.1-8), limma(v.3.42.2), rmarkdown(v.2.1), desc(v.1.2.0), munsell(v.0.5.0), DO.db(v.2.9), GenomeInfoDbData(v.1.2.2), iterators(v.1.0.12), variancePartition(v.1.16.1), reshape2(v.1.4.3) and gtable(v.0.3.0)