1 Annotation version: 20190205
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.
1.1.1 AnnotationHub
AnnotationHub is a newer service and has promise to be an excellent top-level resource for gathering annotation data.
tt <- sm(library(AnnotationHub))
ah <- sm(AnnotationHub())
orgdbs <- query(ah, "OrgDB")
annot_lm <- query(ah, c("OrgDB", "Friedlin"))
lm_name <- names(annot_lm)
annot_lm <- sm(annot_lm[[lm_name[[2]]]])
txtdbs <- query(ah, "TxDb")
## AH48429 appears to be panamensis
##annot_lp <- annot_lp[["AH48429"]]
annot_lp <- query(ah, c("OrgDB", "panamensis"))
lp_name <- names(annot_lp)
annot_lp <- sm(annot_lp[[lp_name[[2]]]])1.2 OrganismDb
Since this document was originally written, I have made substantial changes to how I create, load, and manipulate the eupathdb annotation data. As a result, this needs to be significantly reworked.
AnnotationHub is the new and fancier version of what OrganismDb does. Keith already made these for the parasites though, lets try and use one of those.
tritryp_metadata <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
testing_panamensis <- EuPathDB::make_eupath_organismdbi("panamensis")
testing_braziliensis <- EuPathDB::make_eupath_organismdbi("braziliensis")
testing_donovani <- EuPathDB::make_eupath_organismdbi("donovani")
testing_mexicana <- EuPathDB::make_eupath_organismdbi("mexicana")
testing_major <- EuPathDB::make_eupath_organismdbi("major")
testing_crith <- EuPathDB::make_eupath_organismdbi("Crit", metadata=tritryp_metadata)Assuming the above packages got created, we may load them and extract the annotation data.
tritryp_metadata <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
major_names <- EuPathDB::get_eupath_pkgnames("major")## Starting metadata download.## Finished metadata download.## Found the following hits: Leishmania major strain Friedlin, Leishmania major strain LV39c5, Leishmania major strain SD 75.1, choosing the first.## org.Lmajor.Friedlin.v41.eg.dbwanted_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")
old_name <- "org.Lmajor.Friedlin.v38.eg.db"
lm_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))
panamensis_names <- EuPathDB::get_eupath_pkgnames("panamensis")## Starting metadata download.## Finished metadata download.## Found the following hits: Leishmania panamensis MHOM/COL/81/L13, Leishmania panamensis strain MHOM/PA/94/PSC-1, choosing the first.## org.Lpanamensis.MHOMCOL81L13.v41.eg.dbold_name <- "org.Lpanamensis.MHOMCOL81L13.v38.eg.db"
lp_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))
braziliensis_names <- EuPathDB::get_eupath_pkgnames("braziliensis")## Starting metadata download.## Finished metadata download.## Found the following hits: Leishmania braziliensis MHOM/BR/75/M2903, Leishmania braziliensis MHOM/BR/75/M2904, choosing the first.## org.Lbraziliensis.MHOMBR75M2903.v41.eg.dbold_name <- "org.Lbraziliensis.MHOMBR75M2903.v38.eg.db"
lb_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))
##donovani_names <- get_eupath_pkgnames("donovani")
##donovani_names$orgdb
##ld_org <- load_orgdb_annotations(donovani_names$orgdb, keytype="gid", fields=wanted_fields)
##
##mexicana_names <- get_eupath_pkgnames("mexicana")
##mexicana_names$orgdb
##lmex_org <- load_orgdb_annotations(mexicana_names$orgdb, keytype="gid", fields=wanted_fields)
##
##fasciculata_names <- get_eupath_pkgnames("rithidia", metadata=tritryp_metadata)
##fasciculata_names$orgdb
##cf_org <- load_orgdb_annotations(fasciculata_names$orgdb, keytype="gid", fields=wanted_fields)1.3 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
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).
1.4 Extracting Cell Types
Maria Adelaida requested adding the xCell cell types to the data.
## Length Class Mode
## spill 3 -none- list
## spill.array 3 -none- list
## signatures 489 GeneSetCollection list
## genes 10808 -none- character## Loading required package: annotate## Loading required package: XML## Loading required package: graph##
## Attaching package: 'graph'## The following object is masked from 'package:XML':
##
## addNode## 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:## [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]]
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]
print(dim(g2c_df))
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## [1] 5079 65## 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
## A1CF NA NA NA NA NA
## Eosinophils Epithelial cells Erythrocytes Fibroblasts GMP Hepatocytes
## A1CF NA NA NA NA NA TRUE
## HSC iDC Keratinocytes ly Endothelial cells Macrophages Macrophages M1
## A1CF NA NA NA NA NA NA
## Macrophages M2 Mast cells Megakaryocytes Melanocytes Memory B-cells
## A1CF NA NA NA NA NA
## MEP Mesangial cells Monocytes MPP MSC mv Endothelial cells Myocytes
## A1CF NA NA NA NA NA NA NA
## naive B-cells Neurons Neutrophils NK cells NKT Osteoblast pDC
## A1CF NA NA NA NA NA NA NA
## Pericytes Plasma cells Platelets Preadipocytes pro B-cells Sebocytes
## A1CF NA NA NA NA NA NA
## Skeletal muscle Smooth muscle Tgd cells Th1 cells Th2 cells Tregs
## A1CF NA NA NA NA NA NA
## [ reached 'max' / getOption("max.print") -- omitted 5 rows ]1.5 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$geneID)
##dim(hs_go_biomart)
##dim(hs_go_ensembl)
##hs_goids <- hs_go_biomart
## While testing, I called this desc, that will need to change.
##lp_tooltips <- make_tooltips(lp_annotations)
##lb_tooltips <- make_tooltips(lb_annotations)
lp_lengths <- lp_annotations[, c("ID", "width")]
lb_lengths <- lb_annotations[, c("ID", "width")]
hs_lengths <- hs_annotations[, c("ensembl_gene_id", "cds_length")]
colnames(hs_lengths) <- c("ID", "width")
lp_goids <- read.csv(file="reference/lpan_go.txt.xz", sep="\t", header=FALSE)
lb_goids <- read.csv(file="reference/lbraz_go.txt.xz", sep="\t", header=FALSE)
colnames(lp_goids) <- c("ID","GO","ont","name","source","tag")
colnames(lb_goids) <- c("ID","GO","ont","name","source","tag")2 Putting the pieces together
The macrophage experiment has samples across 2 contexts, the host and parasite. The following block sets up one experiment for each. If you open the all_samples-species.xlsx files, you will note immediately that a few different attempts were made at ascertaining the most likely experimental factors that contributed to the readily apparent batch effects.
2.1 The human transcriptome mappings
Keep in mind that if I change the experimental design with new annotations, I must therefore regenerate the following.
hs_final_annotations <- hs_annotations
hs_final_annotations <- hs_final_annotations[, c("ensembl_transcript_id", "ensembl_gene_id",
"hgnc_symbol", "description", "gene_biotype")]
hs_final_annotations$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 | pathogenstrain.1 | 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 | 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 | null |
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.")| sampleid | pathogenstrain | experimentname | tubelabel | alias | condition | batch | anotherbatch | snpclade | snpcladev2 | snpcladev3 | pathogenstrain.1 | 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 | 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 | 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 | null |
| HPGL0245 | HPGL0245 | s1320 | macrophage | TM130-1320 | Chronic | chr | a | a | multicolor | yellowbrownmulti | right | s1320 | chr_1320 | d130 | undef | 40 | 4 | chronic | Ade | Adriana | Lp | Human | Human macs | Max 2 mill | 2h - 24h chase period | 0.0486111111111111 | unknown | unknown | 199 | Y | Wanderson | Y | 11 | Infected human macrophages | NA | hpgl0245 | preprocessing/hpgl0245/outputs/tophat_hsapiens/accepted_paired.count.xz | preprocessing/hpgl0245/outputs/tophat_lpanamensis/accepted_paired.count.xz | 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.
## If you wish to reproduce this exact build of hpgltools, invoke the following:## > git clone http://github.com/abelew/hpgltools.git## > git reset 7ec7a093f7ee8293e7a5326cb8f77479341502a7## This is hpgltools commit: Thu Feb 7 16:10:02 2019 -0500: 7ec7a093f7ee8293e7a5326cb8f77479341502a7this_save <- paste0(gsub(pattern="\\.Rmd", replace="", x=rmd_file), "-v", ver, ".rda.xz")
message(paste0("Saving to ", this_save))## Saving to 01_annotation_20190205-v20190205.rda.xz## The savefile is: /cbcb/nelsayed-scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20190205-v20190205.rda.xz## Renaming /cbcb/nelsayed-scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20190205-v20190205.rda.xz to /cbcb/nelsayed-scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20190205-v20190205.rda.xz.01.## The save string is: con <- pipe(paste0('pxz -T6 > /cbcb/nelsayed-scratch/atb/rnaseq/lpanamensis_2016/savefiles/01_annotation_20190205-v20190205.rda.xz'), 'wb'); save(list=ls(all.names=TRUE, envir=globalenv()),
## envir=globalenv(), file=con, compress=FALSE); close(con)R version 3.5.2 (2018-12-20)
Platform: x86_64-pc-linux-gnu (64-bit)
locale: LC_CTYPE=en_US.utf8, LC_NUMERIC=C, LC_TIME=en_US.utf8, LC_COLLATE=en_US.utf8, LC_MONETARY=en_US.utf8, LC_MESSAGES=en_US.utf8, LC_PAPER=en_US.utf8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.utf8 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.3.7.0), GO.db(v.3.7.0), OrganismDbi(v.1.24.0), GenomicFeatures(v.1.34.1), GenomicRanges(v.1.34.0), GenomeInfoDb(v.1.18.1), GSEABase(v.1.44.0), graph(v.1.60.0), annotate(v.1.60.0), XML(v.3.98-1.16), xCell(v.1.1.0), org.Lbraziliensis.MHOMBR75M2903.v38.eg.db(v.2018.08), org.Lpanamensis.MHOMCOL81L13.v38.eg.db(v.2018.08), org.Lmajor.Friedlin.v38.eg.db(v.2018.08), AnnotationDbi(v.1.44.0), IRanges(v.2.16.0), S4Vectors(v.0.20.1), AnnotationHub(v.2.14.2), hpgltools(v.2018.11), Biobase(v.2.42.0) and BiocGenerics(v.0.28.0)
loaded via a namespace (and not attached): tidyselect(v.0.2.5), lme4(v.1.1-19), RSQLite(v.2.1.1), grid(v.3.5.2), BiocParallel(v.1.16.5), devtools(v.2.0.1), munsell(v.0.5.0), codetools(v.0.2-16), units(v.0.6-2), withr(v.2.1.2), colorspace(v.1.4-0), GOSemSim(v.2.8.0), highr(v.0.7), knitr(v.1.21), rstudioapi(v.0.9.0), DOSE(v.3.8.2), labeling(v.0.3), urltools(v.1.7.1), GenomeInfoDbData(v.1.2.0), bit64(v.0.9-7), farver(v.1.1.0), rprojroot(v.1.3-2), xfun(v.0.4), R6(v.2.3.0), doParallel(v.1.0.14), bitops(v.1.0-6), fgsea(v.1.8.0), gridGraphics(v.0.3-0), DelayedArray(v.0.8.0), assertthat(v.0.2.0), promises(v.1.0.1), scales(v.1.0.0), ggraph(v.1.0.2), enrichplot(v.1.2.0), gtable(v.0.2.0), sva(v.3.30.1), processx(v.3.2.1), rlang(v.0.3.1), genefilter(v.1.64.0), splines(v.3.5.2), rtracklayer(v.1.42.1), lazyeval(v.0.2.1), europepmc(v.0.3), BiocManager(v.1.30.4), yaml(v.2.2.0), reshape2(v.1.4.3), backports(v.1.1.3), httpuv(v.1.4.5.1), qvalue(v.2.14.1), RBGL(v.1.58.1), clusterProfiler(v.3.10.1), tools(v.3.5.2), usethis(v.1.4.0), ggplotify(v.0.0.3), ggplot2(v.3.1.0), gplots(v.3.0.1), RColorBrewer(v.1.1-2), sessioninfo(v.1.1.1), ggridges(v.0.5.1), Rcpp(v.1.0.0), plyr(v.1.8.4), base64enc(v.0.1-3), progress(v.1.2.0), zlibbioc(v.1.28.0), purrr(v.0.2.5), RCurl(v.1.95-4.11), ps(v.1.3.0), prettyunits(v.1.0.2), viridis(v.0.5.1), cowplot(v.0.9.4), SummarizedExperiment(v.1.12.0), ggrepel(v.0.8.0), colorRamps(v.2.3), fs(v.1.2.6), variancePartition(v.1.12.1), magrittr(v.1.5), data.table(v.1.12.0), openxlsx(v.4.1.0), DO.db(v.2.9), triebeard(v.0.3.0), packrat(v.0.5.0), matrixStats(v.0.54.0), pkgload(v.1.0.2), hms(v.0.4.2), mime(v.0.6), evaluate(v.0.12), GSVA(v.1.30.0), xtable(v.1.8-3), pbkrtest(v.0.4-7), gridExtra(v.2.3), testthat(v.2.0.1), compiler(v.3.5.2), biomaRt(v.2.38.0), tibble(v.2.0.1), KernSmooth(v.2.23-15), crayon(v.1.3.4), minqa(v.1.2.4), htmltools(v.0.3.6), mgcv(v.1.8-26), later(v.0.7.5.9000), tidyr(v.0.8.2), geneplotter(v.1.60.0), DBI(v.1.0.0), tweenr(v.1.0.1), MASS(v.7.3-51.1), Matrix(v.1.2-15), cli(v.1.0.1), gdata(v.2.18.0), bindr(v.0.1.1), igraph(v.1.2.2), pkgconfig(v.2.0.2), rvcheck(v.0.1.3), GenomicAlignments(v.1.18.1), xml2(v.1.2.0), foreach(v.1.4.4), XVector(v.0.22.0), rvest(v.0.3.2), stringr(v.1.3.1), callr(v.3.1.1), digest(v.0.6.18), pracma(v.2.2.2), Biostrings(v.2.50.2), EuPathDB(v.1.0.1), rmarkdown(v.1.11), fastmatch(v.1.1-0), curl(v.3.3), shiny(v.1.2.0), Rsamtools(v.1.34.0), gtools(v.3.8.1), nloptr(v.1.2.1), nlme(v.3.1-137), jsonlite(v.1.6), bindrcpp(v.0.2.2), desc(v.1.2.0), viridisLite(v.0.3.0), limma(v.3.38.3), pillar(v.1.3.1), lattice(v.0.20-38), httr(v.1.4.0), pkgbuild(v.1.0.2), survival(v.2.43-3), interactiveDisplayBase(v.1.20.0), glue(v.1.3.0), remotes(v.2.0.2), zip(v.1.0.0), UpSetR(v.1.3.3), shinythemes(v.1.1.2), iterators(v.1.0.10), pander(v.0.6.3), bit(v.1.1-14), ggforce(v.0.1.3), stringi(v.1.2.4), blob(v.1.1.1), caTools(v.1.17.1.1), memoise(v.1.1.0) and dplyr(v.0.7.8)
---
title: "L. panamensis 20180822: Annotation data."
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("~/hpgltools")
knitr::opts_knit$set(width=120,
                     progress=TRUE,
                     verbose=TRUE,
                     echo=TRUE)
knitr::opts_chunk$set(error=TRUE,
                      dpi=96)
old_options <- options(digits=4,
                       max.print=120,
                       stringsAsFactors=FALSE,
                       knitr.duplicate.label="allow")
ggplot2::theme_set(ggplot2::theme_bw(base_size=10))
ver <- "20190205"
previous_file <- "index.Rmd"

tmp <- try(sm(loadme(filename=paste0(gsub(pattern="\\.Rmd", replace="", x=previous_file), "-v", ver, ".rda.xz"))))
rmd_file <- "01_annotation_20190205.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.

### AnnotationHub

AnnotationHub is a newer service and has promise to be an excellent top-level
resource for gathering annotation data.

```{r data_input_genome}
tt <- sm(library(AnnotationHub))
ah <- sm(AnnotationHub())
orgdbs <- query(ah, "OrgDB")
annot_lm <- query(ah, c("OrgDB", "Friedlin"))
lm_name <- names(annot_lm)
annot_lm <- sm(annot_lm[[lm_name[[2]]]])
txtdbs <- query(ah, "TxDb")

## AH48429 appears to be panamensis
##annot_lp <- annot_lp[["AH48429"]]
annot_lp <- query(ah, c("OrgDB", "panamensis"))
lp_name <- names(annot_lp)
annot_lp <- sm(annot_lp[[lp_name[[2]]]])
```

## OrganismDb

Since this document was originally written, I have made substantial changes to
how I create, load, and manipulate the eupathdb annotation data.  As a result,
this needs to be significantly reworked.

AnnotationHub is the new and fancier version of what OrganismDb does.  Keith
already made these for the parasites though, lets try and use one of those.

```{r make_orgdb, eval=FALSE}
tritryp_metadata <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
testing_panamensis <- EuPathDB::make_eupath_organismdbi("panamensis")
testing_braziliensis <- EuPathDB::make_eupath_organismdbi("braziliensis")
testing_donovani <- EuPathDB::make_eupath_organismdbi("donovani")
testing_mexicana <- EuPathDB::make_eupath_organismdbi("mexicana")
testing_major <- EuPathDB::make_eupath_organismdbi("major")
testing_crith <- EuPathDB::make_eupath_organismdbi("Crit", metadata=tritryp_metadata)
```

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

```{r lpanamensis_orgdb}
tritryp_metadata <- EuPathDB::download_eupath_metadata(webservice="tritrypdb")
major_names <- EuPathDB::get_eupath_pkgnames("major")
major_names$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")

old_name <- "org.Lmajor.Friedlin.v38.eg.db"
lm_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))

panamensis_names <- EuPathDB::get_eupath_pkgnames("panamensis")
panamensis_names$orgdb
old_name <- "org.Lpanamensis.MHOMCOL81L13.v38.eg.db"
lp_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))

braziliensis_names <- EuPathDB::get_eupath_pkgnames("braziliensis")
braziliensis_names$orgdb
old_name <- "org.Lbraziliensis.MHOMBR75M2903.v38.eg.db"
lb_org <- sm(EuPathDB::load_orgdb_annotations(old_name, keytype="gid", fields=wanted_fields))

##donovani_names <- get_eupath_pkgnames("donovani")
##donovani_names$orgdb
##ld_org <- load_orgdb_annotations(donovani_names$orgdb, keytype="gid", fields=wanted_fields)
##
##mexicana_names <- get_eupath_pkgnames("mexicana")
##mexicana_names$orgdb
##lmex_org <- load_orgdb_annotations(mexicana_names$orgdb, keytype="gid", fields=wanted_fields)
##
##fasciculata_names <- get_eupath_pkgnames("rithidia", metadata=tritryp_metadata)
##fasciculata_names$orgdb
##cf_org <- load_orgdb_annotations(fasciculata_names$orgdb, keytype="gid", fields=wanted_fields)
```

## 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]]
  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]
  print(dim(g2c_df))
}
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 ontology}
## 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")
```

# 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",
                                                 "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))

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)
```

## The parasite transcriptome mappings

```{r parasite_expt}
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.")
```

# 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

# 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](sample_sheets/all_samples-combined.xlsx").

```{r saveme}
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 <- saveme(filename=this_save)
pander::pander(sessionInfo())
```
