R/pca2go.R
In federicomarini/pcaExplorer: Interactive Visualization of RNA-seq Data Using a Principal Components Approach
Defines functions
limmaquickpca2go
rankedGeneLoadings
pca2go
topGOtable
Documented in
limmaquickpca2go
pca2go
topGOtable
#' Extract functional terms enriched in the DE genes, based on topGO
#'
#' A wrapper for extracting functional GO terms enriched in the DE genes, based on
#' the algorithm and the implementation in the topGO package
#'
#' Allowed values assumed by the `topGO_method2` parameter are one of the
#' following: `elim`, `weight`, `weight01`, `lea`,
#' `parentchild`. For more details on this, please refer to the original
#' documentation of the `topGO` package itself
#'
#' @param DEgenes A vector of (differentially expressed) genes
#' @param BGgenes A vector of background genes, e.g. all (expressed) genes in the assays
#' @param ontology Which Gene Ontology domain to analyze: `BP` (Biological Process), `MF` (Molecular Function), or `CC` (Cellular Component)
#' @param annot Which function to use for annotating genes to GO terms. Defaults to `annFUN.org`
#' @param mapping Which `org.XX.eg.db` to use for annotation - select according to the species
#' @param geneID Which format the genes are provided. Defaults to `symbol`, could also be
#' `entrez` or `ENSEMBL`
#' @param topTablerows How many rows to report before any filtering
#' @param fullNamesInRows Logical, whether to display or not the full names for the GO terms
#' @param addGeneToTerms Logical, whether to add a column with all genes annotated to each GO term
#' @param plotGraph Logical, if TRUE additionally plots a graph on the identified GO terms
#' @param plotNodes Number of nodes to plot
#' @param writeOutput Logical, if TRUE additionally writes out the result to a file
#' @param outputFile Name of the file the result should be written into
#' @param topGO_method2 Character, specifying which of the methods implemented by `topGO` should be used, in addition to the `classic` algorithm. Defaults to `elim`
#' @param do_padj Logical, whether to perform the adjustment on the p-values from the specific
#' topGO method, based on the FDR correction. Defaults to FALSE, since the assumption of
#' independent hypotheses is somewhat violated by the intrinsic DAG-structure of the Gene
#' Ontology Terms
#'
#' @import topGO
#'
#' @return A table containing the computed GO Terms and related enrichment scores
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design= ~ cell + dex)
#' # Example, performing extraction of enriched functional categories in
#' # detected significantly expressed genes
#' \dontrun{
#' dds_airway <- DESeq(dds_airway)
#' res_airway <- results(dds_airway)
#' library("AnnotationDbi")
#' library("org.Hs.eg.db")
#' res_airway$symbol <- mapIds(org.Hs.eg.db,
#' keys = row.names(res_airway),
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' res_airway$entrez <- mapIds(org.Hs.eg.db,
#' keys = row.names(res_airway),
#' column = "ENTREZID",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' resOrdered <- as.data.frame(res_airway[order(res_airway$padj),])
#' de_df <- resOrdered[resOrdered$padj < .05 & !is.na(resOrdered$padj),]
#' de_symbols <- de_df$symbol
#' bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
#' bg_symbols <- mapIds(org.Hs.eg.db,
#' keys = bg_ids,
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' library(topGO)
#' topgoDE_airway <- topGOtable(de_symbols, bg_symbols,
#' ontology = "BP",
#' mapping = "org.Hs.eg.db",
#' geneID = "symbol")
#' }
#'
#' @importFrom mosdef run_topGO
#'
#' @export
topGOtable <- function(DEgenes, # Differentially expressed genes
BGgenes, # background genes
ontology = "BP", # could use also "MF"
annot = annFUN.org, # parameters for creating topGO object
mapping = "org.Mm.eg.db",
geneID = "symbol", # could also beID = "entrez"
topTablerows = 200,
fullNamesInRows = TRUE,
addGeneToTerms = TRUE,
plotGraph = FALSE,
plotNodes = 10,
writeOutput = FALSE,
outputFile = "",
topGO_method2 = "elim",
do_padj = FALSE) {
.Deprecated(old = "topGOtable", new = "mosdef::run_topGO",
msg = paste0(
"Please use `mosdef::run_topGO()` in replacement of the `topGOtable()` function, ",
"originally located in the pcaExplorer package. \nCheck the manual page for ",
"`?mosdef::run_topGO()` to see the details on how to use it, e.g. ",
"refer to the new parameter definition and naming"))
res_enrich <- mosdef::run_topGO(
de_genes = DEgenes,
bg_genes = BGgenes,
annot = annot,
ontology = ontology,
mapping = mapping,
gene_id = geneID,
full_names_in_rows = fullNamesInRows,
add_gene_to_terms = addGeneToTerms,
topGO_method2 = topGO_method2,
do_padj = do_padj,
verbose = TRUE
)
return(res_enrich)
}
#' Functional interpretation of the principal components
#'
#' Extracts the genes with the highest loadings for each principal component, and
#' performs functional enrichment analysis on them using routines and algorithms from
#' the `topGO` package
#'
#' @param se A [DESeq2::DESeqTransform()] object, with data in `assay(se)`,
#' produced for example by either [DESeq2::rlog()] or
#' [DESeq2::varianceStabilizingTransformation()]
#' @param pca_ngenes Number of genes to use for the PCA
#' @param annotation A `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids)
#' and a column, `gene_name`, containing e.g. HGNC-based gene symbols
#' @param inputType Input format type of the gene identifiers. Will be used by the routines of `topGO`
#' @param organism Character abbreviation for the species, using `org.XX.eg.db` for annotation
#' @param ensToGeneSymbol Logical, whether to expect ENSEMBL gene identifiers, to convert to gene symbols
#' with the `annotation` provided
#' @param loadings_ngenes Number of genes to extract the loadings (in each direction)
#' @param background_genes Which genes to consider as background.
#' @param scale Logical, defaults to FALSE, scale values for the PCA
#' @param return_ranked_gene_loadings Logical, defaults to FALSE. If TRUE, simply returns
#' a list containing the top ranked genes with hi loadings in each PC and in each direction
#' @param annopkg String containing the name of the organism annotation package. Can be used to
#' override the `organism` parameter, e.g. in case of alternative identifiers used
#' in the annotation package (Arabidopsis with TAIR)
#' @param ... Further parameters to be passed to the topGO routine
#'
#' @return A nested list object containing for each principal component the terms enriched
#' in each direction. This object is to be thought in combination with the displaying feature
#' of the main [pcaExplorer()] function
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design= ~ cell + dex)
#' \dontrun{
#' rld_airway <- rlogTransformation(dds_airway)
#' # constructing the annotation object
#' anno_df <- data.frame(gene_id = rownames(dds_airway),
#' stringsAsFactors = FALSE)
#' library("AnnotationDbi")
#' library("org.Hs.eg.db")
#' anno_df$gene_name <- mapIds(org.Hs.eg.db,
#' keys = anno_df$gene_id,
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' rownames(anno_df) <- anno_df$gene_id
#' bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
#' library(topGO)
#' pca2go_airway <- pca2go(rld_airway,
#' annotation = anno_df,
#' organism = "Hs",
#' ensToGeneSymbol = TRUE,
#' background_genes = bg_ids)
#' }
#'
#' @export
pca2go <- function(se,
pca_ngenes = 10000,
annotation = NULL,
inputType = "geneSymbol",
organism = "Mm",
ensToGeneSymbol = FALSE,
loadings_ngenes = 500,
background_genes = NULL,
scale = FALSE,
return_ranked_gene_loadings = FALSE,
annopkg = NULL,
... # further parameters to be passed to the topgo routine
) {
# to force e.g. in case other identifiers are used, e.g. in Arabidopsis with TAIR
if (is.null(annopkg))
annopkg <- paste0("org.", organism, ".eg.db")
if (!require(annopkg, character.only = TRUE)) {
stop("The package", annopkg, "is not installed/available. Try installing it with BiocManager::install() ?")
}
exprsData <- assay(se)
if (is.null(background_genes)) {
if (is(se, "DESeqDataSet")) {
BGids <- rownames(se)[rowSums(counts(se)) > 0]
} else if (is(se, "DESeqTransform")) {
BGids <- rownames(se)[rowSums(assay(se)) != 0]
} else {
BGids <- rownames(se)
}
} else {
BGids <- background_genes
}
exprsData <- assay(se)[order(rowVars(assay(se)), decreasing = TRUE), ]
exprsData <- exprsData[1:pca_ngenes, ]
## convert ensembl to gene symbol ids
if (ensToGeneSymbol & !(is.null(annotation))) {
rownames(exprsData) <- annotation$gene_name[match(rownames(exprsData), rownames(annotation))]
BGids <- annotation$gene_name[match(BGids, rownames(annotation))]
}
rv <- rowVars(exprsData)
dropped <- sum(rv == 0)
if (dropped > 0)
message("Dropped ", dropped, " genes with 0 variance")
exprsData <- exprsData[rv > 0, ]
message("After subsetting/filtering for invariant genes, working on a ", nrow(exprsData), "x", ncol(exprsData), " expression matrix\n")
p <- prcomp(t(exprsData), scale = scale, center = TRUE)
message("Ranking genes by the loadings ...")
probesPC1pos <- rankedGeneLoadings(p, pc = 1, decreasing = TRUE)[1:loadings_ngenes]
probesPC1neg <- rankedGeneLoadings(p, pc = 1, decreasing = FALSE)[1:loadings_ngenes]
probesPC2pos <- rankedGeneLoadings(p, pc = 2, decreasing = TRUE)[1:loadings_ngenes]
probesPC2neg <- rankedGeneLoadings(p, pc = 2, decreasing = FALSE)[1:loadings_ngenes]
probesPC3pos <- rankedGeneLoadings(p, pc = 3, decreasing = TRUE)[1:loadings_ngenes]
probesPC3neg <- rankedGeneLoadings(p, pc = 3, decreasing = FALSE)[1:loadings_ngenes]
probesPC4pos <- rankedGeneLoadings(p, pc = 4, decreasing = TRUE)[1:loadings_ngenes]
probesPC4neg <- rankedGeneLoadings(p, pc = 4, decreasing = FALSE)[1:loadings_ngenes]
if (return_ranked_gene_loadings)
return(
list(PC1 = list(posLoad = probesPC1pos, negLoad = probesPC1neg),
PC2 = list(posLoad = probesPC2pos, negLoad = probesPC2neg),
PC3 = list(posLoad = probesPC3pos, negLoad = probesPC3neg),
PC4 = list(posLoad = probesPC4pos, negLoad = probesPC4neg)
)
)
message("Ranking genes by the loadings ... done!")
message("Extracting functional categories enriched in the gene subsets ...")
topGOpc1pos <- mosdef::run_topGO(de_genes = probesPC1pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc1neg <- mosdef::run_topGO(de_genes = probesPC1neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc2pos <- mosdef::run_topGO(de_genes = probesPC2pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc2neg <- mosdef::run_topGO(de_genes = probesPC2neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc3pos <- mosdef::run_topGO(de_genes = probesPC3pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc3neg <- mosdef::run_topGO(de_genes = probesPC3neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc4pos <- mosdef::run_topGO(de_genes = probesPC4pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc4neg <- mosdef::run_topGO(de_genes = probesPC4neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
goEnrichs <- list(PC1 = list(posLoad = topGOpc1pos, negLoad = topGOpc1neg),
PC2 = list(posLoad = topGOpc2pos, negLoad = topGOpc2neg),
PC3 = list(posLoad = topGOpc3pos, negLoad = topGOpc3neg),
PC4 = list(posLoad = topGOpc4pos, negLoad = topGOpc4neg)
)
message("Extracting functional categories enriched in the gene subsets ... done!")
attr(goEnrichs, "n_genesforpca") <- pca_ngenes
return(goEnrichs)
}
rankedGeneLoadings <- function(x, pc = 1, decreasing = TRUE) {
# works directly on the prcomp object
return(rownames(x$rotation)[order(x$rotation[, pc], decreasing = decreasing)])
}
#' Functional interpretation of the principal components, based on simple
#' overrepresentation analysis
#'
#' Extracts the genes with the highest loadings for each principal component, and
#' performs functional enrichment analysis on them using the simple and quick routine
#' provided by the `limma` package
#'
#' @param se A [DESeq2::DESeqTransform()] object, with data in `assay(se)`,
#' produced for example by either [DESeq2::rlog()] or
#' [DESeq2::varianceStabilizingTransformation()]
#' @param pca_ngenes Number of genes to use for the PCA
#' @param inputType Input format type of the gene identifiers. Deafults to `ENSEMBL`, that then will
#' be converted to ENTREZ ids. Can assume values such as `ENTREZID`,`GENENAME` or `SYMBOL`,
#' like it is normally used with the `select` function of `AnnotationDbi`
#' @param organism Character abbreviation for the species, using `org.XX.eg.db` for annotation
#' @param loadings_ngenes Number of genes to extract the loadings (in each direction)
#' @param background_genes Which genes to consider as background.
#' @param scale Logical, defaults to FALSE, scale values for the PCA
#' @param ... Further parameters to be passed to the goana routine
#'
#' @return A nested list object containing for each principal component the terms enriched
#' in each direction. This object is to be thought in combination with the displaying feature
#' of the main [pcaExplorer()] function
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' library("limma")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design = ~ cell + dex)
#' \dontrun{
#' rld_airway <- rlogTransformation(dds_airway)
#' goquick_airway <- limmaquickpca2go(rld_airway,
#' pca_ngenes = 10000,
#' inputType = "ENSEMBL",
#' organism = "Hs")
#' }
#'
#' @export
limmaquickpca2go <- function(se,
pca_ngenes = 10000,
inputType = "ENSEMBL",
organism = "Mm",
loadings_ngenes = 500,
background_genes = NULL,
scale = FALSE,
... # further parameters to be passed to the topgo routine
) {
annopkg <- paste0("org.", organism, ".eg.db")
if (!require(annopkg, character.only = TRUE)) {
stop("The package", annopkg, "is not installed/available. Try installing it with BiocManager::install() ?")
}
exprsData <- assay(se)
if (is.null(background_genes)) {
if (is(se, "DESeqDataSet")) {
BGids <- rownames(se)[rowSums(counts(se)) > 0]
} else if (is(se, "DESeqTransform")) {
BGids <- rownames(se)[rowSums(assay(se)) != 0]
} else {
BGids <- rownames(se)
}
} else {
BGids <- background_genes
}
exprsData <- assay(se)[order(rowVars(assay(se)), decreasing = TRUE), ]
exprsData <- exprsData[1:pca_ngenes, ]
rv <- rowVars(exprsData)
dropped <- sum(rv == 0)
if (dropped > 0)
message("Dropped ", dropped, " genes with 0 variance")
exprsData <- exprsData[rv > 0, ]
message("After subsetting/filtering for invariant genes, working on a ", nrow(exprsData), "x", ncol(exprsData), " expression matrix\n")
p <- prcomp(t(exprsData), scale = scale, center = TRUE)
message("Ranking genes by the loadings ...")
probesPC1pos <- rankedGeneLoadings(p, pc = 1, decreasing = TRUE)[1:loadings_ngenes]
probesPC1neg <- rankedGeneLoadings(p, pc = 1, decreasing = FALSE)[1:loadings_ngenes]
probesPC2pos <- rankedGeneLoadings(p, pc = 2, decreasing = TRUE)[1:loadings_ngenes]
probesPC2neg <- rankedGeneLoadings(p, pc = 2, decreasing = FALSE)[1:loadings_ngenes]
probesPC3pos <- rankedGeneLoadings(p, pc = 3, decreasing = TRUE)[1:loadings_ngenes]
probesPC3neg <- rankedGeneLoadings(p, pc = 3, decreasing = FALSE)[1:loadings_ngenes]
probesPC4pos <- rankedGeneLoadings(p, pc = 4, decreasing = TRUE)[1:loadings_ngenes]
probesPC4neg <- rankedGeneLoadings(p, pc = 4, decreasing = FALSE)[1:loadings_ngenes]
## convert ensembl to entrez ids
probesPC1pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC1pos, column = "ENTREZID", keytype = inputType)
probesPC1neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC1neg, column = "ENTREZID", keytype = inputType)
probesPC2pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC2pos, column = "ENTREZID", keytype = inputType)
probesPC2neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC2neg, column = "ENTREZID", keytype = inputType)
probesPC3pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC3pos, column = "ENTREZID", keytype = inputType)
probesPC3neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC3neg, column = "ENTREZID", keytype = inputType)
probesPC4pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC4pos, column = "ENTREZID", keytype = inputType)
probesPC4neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC4neg, column = "ENTREZID", keytype = inputType)
bg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = BGids, column = "ENTREZID", keytype = inputType)
message("Ranking genes by the loadings ... done!")
message("Extracting functional categories enriched in the gene subsets ...")
quickGOpc1pos <- limma::topGO(limma::goana(probesPC1pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("1")
quickGOpc1neg <- limma::topGO(limma::goana(probesPC1neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("2")
quickGOpc2pos <- limma::topGO(limma::goana(probesPC2pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("3")
quickGOpc2neg <- limma::topGO(limma::goana(probesPC2neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("4")
quickGOpc3pos <- limma::topGO(limma::goana(probesPC3pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("5")
quickGOpc3neg <- limma::topGO(limma::goana(probesPC3neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("6")
quickGOpc4pos <- limma::topGO(limma::goana(probesPC4pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("7")
quickGOpc4neg <- limma::topGO(limma::goana(probesPC4neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("8")
quickGOpc1pos <- quickGOpc1pos[order(quickGOpc1pos$P.DE), ]
quickGOpc1neg <- quickGOpc1neg[order(quickGOpc1neg$P.DE), ]
quickGOpc2pos <- quickGOpc2pos[order(quickGOpc2pos$P.DE), ]
quickGOpc2neg <- quickGOpc2neg[order(quickGOpc2neg$P.DE), ]
quickGOpc3pos <- quickGOpc3pos[order(quickGOpc3pos$P.DE), ]
quickGOpc3neg <- quickGOpc3neg[order(quickGOpc3neg$P.DE), ]
quickGOpc4pos <- quickGOpc4pos[order(quickGOpc4pos$P.DE), ]
quickGOpc4neg <- quickGOpc4neg[order(quickGOpc4neg$P.DE), ]
goEnrichs <- list(PC1 = list(posLoad = quickGOpc1pos, negLoad = quickGOpc1neg),
PC2 = list(posLoad = quickGOpc2pos, negLoad = quickGOpc2neg),
PC3 = list(posLoad = quickGOpc3pos, negLoad = quickGOpc3neg),
PC4 = list(posLoad = quickGOpc4pos, negLoad = quickGOpc4neg)
)
message("Extracting functional categories enriched in the gene subsets ... done!")
attr(goEnrichs, "n_genesforpca") <- pca_ngenes
return(goEnrichs)
}
federicomarini/pcaExplorer documentation built on June 12, 2025, 3:09 p.m.
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Defines functions limmaquickpca2go rankedGeneLoadings pca2go topGOtable
Documented in limmaquickpca2go pca2go topGOtable
#' Extract functional terms enriched in the DE genes, based on topGO
#'
#' A wrapper for extracting functional GO terms enriched in the DE genes, based on
#' the algorithm and the implementation in the topGO package
#'
#' Allowed values assumed by the `topGO_method2` parameter are one of the
#' following: `elim`, `weight`, `weight01`, `lea`,
#' `parentchild`. For more details on this, please refer to the original
#' documentation of the `topGO` package itself
#'
#' @param DEgenes A vector of (differentially expressed) genes
#' @param BGgenes A vector of background genes, e.g. all (expressed) genes in the assays
#' @param ontology Which Gene Ontology domain to analyze: `BP` (Biological Process), `MF` (Molecular Function), or `CC` (Cellular Component)
#' @param annot Which function to use for annotating genes to GO terms. Defaults to `annFUN.org`
#' @param mapping Which `org.XX.eg.db` to use for annotation - select according to the species
#' @param geneID Which format the genes are provided. Defaults to `symbol`, could also be
#' `entrez` or `ENSEMBL`
#' @param topTablerows How many rows to report before any filtering
#' @param fullNamesInRows Logical, whether to display or not the full names for the GO terms
#' @param addGeneToTerms Logical, whether to add a column with all genes annotated to each GO term
#' @param plotGraph Logical, if TRUE additionally plots a graph on the identified GO terms
#' @param plotNodes Number of nodes to plot
#' @param writeOutput Logical, if TRUE additionally writes out the result to a file
#' @param outputFile Name of the file the result should be written into
#' @param topGO_method2 Character, specifying which of the methods implemented by `topGO` should be used, in addition to the `classic` algorithm. Defaults to `elim`
#' @param do_padj Logical, whether to perform the adjustment on the p-values from the specific
#' topGO method, based on the FDR correction. Defaults to FALSE, since the assumption of
#' independent hypotheses is somewhat violated by the intrinsic DAG-structure of the Gene
#' Ontology Terms
#'
#' @import topGO
#'
#' @return A table containing the computed GO Terms and related enrichment scores
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design= ~ cell + dex)
#' # Example, performing extraction of enriched functional categories in
#' # detected significantly expressed genes
#' \dontrun{
#' dds_airway <- DESeq(dds_airway)
#' res_airway <- results(dds_airway)
#' library("AnnotationDbi")
#' library("org.Hs.eg.db")
#' res_airway$symbol <- mapIds(org.Hs.eg.db,
#' keys = row.names(res_airway),
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' res_airway$entrez <- mapIds(org.Hs.eg.db,
#' keys = row.names(res_airway),
#' column = "ENTREZID",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' resOrdered <- as.data.frame(res_airway[order(res_airway$padj),])
#' de_df <- resOrdered[resOrdered$padj < .05 & !is.na(resOrdered$padj),]
#' de_symbols <- de_df$symbol
#' bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
#' bg_symbols <- mapIds(org.Hs.eg.db,
#' keys = bg_ids,
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' library(topGO)
#' topgoDE_airway <- topGOtable(de_symbols, bg_symbols,
#' ontology = "BP",
#' mapping = "org.Hs.eg.db",
#' geneID = "symbol")
#' }
#'
#' @importFrom mosdef run_topGO
#'
#' @export
topGOtable <- function(DEgenes, # Differentially expressed genes
BGgenes, # background genes
ontology = "BP", # could use also "MF"
annot = annFUN.org, # parameters for creating topGO object
mapping = "org.Mm.eg.db",
geneID = "symbol", # could also beID = "entrez"
topTablerows = 200,
fullNamesInRows = TRUE,
addGeneToTerms = TRUE,
plotGraph = FALSE,
plotNodes = 10,
writeOutput = FALSE,
outputFile = "",
topGO_method2 = "elim",
do_padj = FALSE) {
.Deprecated(old = "topGOtable", new = "mosdef::run_topGO",
msg = paste0(
"Please use `mosdef::run_topGO()` in replacement of the `topGOtable()` function, ",
"originally located in the pcaExplorer package. \nCheck the manual page for ",
"`?mosdef::run_topGO()` to see the details on how to use it, e.g. ",
"refer to the new parameter definition and naming"))
res_enrich <- mosdef::run_topGO(
de_genes = DEgenes,
bg_genes = BGgenes,
annot = annot,
ontology = ontology,
mapping = mapping,
gene_id = geneID,
full_names_in_rows = fullNamesInRows,
add_gene_to_terms = addGeneToTerms,
topGO_method2 = topGO_method2,
do_padj = do_padj,
verbose = TRUE
)
return(res_enrich)
}
#' Functional interpretation of the principal components
#'
#' Extracts the genes with the highest loadings for each principal component, and
#' performs functional enrichment analysis on them using routines and algorithms from
#' the `topGO` package
#'
#' @param se A [DESeq2::DESeqTransform()] object, with data in `assay(se)`,
#' produced for example by either [DESeq2::rlog()] or
#' [DESeq2::varianceStabilizingTransformation()]
#' @param pca_ngenes Number of genes to use for the PCA
#' @param annotation A `data.frame` object, with row.names as gene identifiers (e.g. ENSEMBL ids)
#' and a column, `gene_name`, containing e.g. HGNC-based gene symbols
#' @param inputType Input format type of the gene identifiers. Will be used by the routines of `topGO`
#' @param organism Character abbreviation for the species, using `org.XX.eg.db` for annotation
#' @param ensToGeneSymbol Logical, whether to expect ENSEMBL gene identifiers, to convert to gene symbols
#' with the `annotation` provided
#' @param loadings_ngenes Number of genes to extract the loadings (in each direction)
#' @param background_genes Which genes to consider as background.
#' @param scale Logical, defaults to FALSE, scale values for the PCA
#' @param return_ranked_gene_loadings Logical, defaults to FALSE. If TRUE, simply returns
#' a list containing the top ranked genes with hi loadings in each PC and in each direction
#' @param annopkg String containing the name of the organism annotation package. Can be used to
#' override the `organism` parameter, e.g. in case of alternative identifiers used
#' in the annotation package (Arabidopsis with TAIR)
#' @param ... Further parameters to be passed to the topGO routine
#'
#' @return A nested list object containing for each principal component the terms enriched
#' in each direction. This object is to be thought in combination with the displaying feature
#' of the main [pcaExplorer()] function
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design= ~ cell + dex)
#' \dontrun{
#' rld_airway <- rlogTransformation(dds_airway)
#' # constructing the annotation object
#' anno_df <- data.frame(gene_id = rownames(dds_airway),
#' stringsAsFactors = FALSE)
#' library("AnnotationDbi")
#' library("org.Hs.eg.db")
#' anno_df$gene_name <- mapIds(org.Hs.eg.db,
#' keys = anno_df$gene_id,
#' column = "SYMBOL",
#' keytype = "ENSEMBL",
#' multiVals = "first")
#' rownames(anno_df) <- anno_df$gene_id
#' bg_ids <- rownames(dds_airway)[rowSums(counts(dds_airway)) > 0]
#' library(topGO)
#' pca2go_airway <- pca2go(rld_airway,
#' annotation = anno_df,
#' organism = "Hs",
#' ensToGeneSymbol = TRUE,
#' background_genes = bg_ids)
#' }
#'
#' @export
pca2go <- function(se,
pca_ngenes = 10000,
annotation = NULL,
inputType = "geneSymbol",
organism = "Mm",
ensToGeneSymbol = FALSE,
loadings_ngenes = 500,
background_genes = NULL,
scale = FALSE,
return_ranked_gene_loadings = FALSE,
annopkg = NULL,
... # further parameters to be passed to the topgo routine
) {
# to force e.g. in case other identifiers are used, e.g. in Arabidopsis with TAIR
if (is.null(annopkg))
annopkg <- paste0("org.", organism, ".eg.db")
if (!require(annopkg, character.only = TRUE)) {
stop("The package", annopkg, "is not installed/available. Try installing it with BiocManager::install() ?")
}
exprsData <- assay(se)
if (is.null(background_genes)) {
if (is(se, "DESeqDataSet")) {
BGids <- rownames(se)[rowSums(counts(se)) > 0]
} else if (is(se, "DESeqTransform")) {
BGids <- rownames(se)[rowSums(assay(se)) != 0]
} else {
BGids <- rownames(se)
}
} else {
BGids <- background_genes
}
exprsData <- assay(se)[order(rowVars(assay(se)), decreasing = TRUE), ]
exprsData <- exprsData[1:pca_ngenes, ]
## convert ensembl to gene symbol ids
if (ensToGeneSymbol & !(is.null(annotation))) {
rownames(exprsData) <- annotation$gene_name[match(rownames(exprsData), rownames(annotation))]
BGids <- annotation$gene_name[match(BGids, rownames(annotation))]
}
rv <- rowVars(exprsData)
dropped <- sum(rv == 0)
if (dropped > 0)
message("Dropped ", dropped, " genes with 0 variance")
exprsData <- exprsData[rv > 0, ]
message("After subsetting/filtering for invariant genes, working on a ", nrow(exprsData), "x", ncol(exprsData), " expression matrix\n")
p <- prcomp(t(exprsData), scale = scale, center = TRUE)
message("Ranking genes by the loadings ...")
probesPC1pos <- rankedGeneLoadings(p, pc = 1, decreasing = TRUE)[1:loadings_ngenes]
probesPC1neg <- rankedGeneLoadings(p, pc = 1, decreasing = FALSE)[1:loadings_ngenes]
probesPC2pos <- rankedGeneLoadings(p, pc = 2, decreasing = TRUE)[1:loadings_ngenes]
probesPC2neg <- rankedGeneLoadings(p, pc = 2, decreasing = FALSE)[1:loadings_ngenes]
probesPC3pos <- rankedGeneLoadings(p, pc = 3, decreasing = TRUE)[1:loadings_ngenes]
probesPC3neg <- rankedGeneLoadings(p, pc = 3, decreasing = FALSE)[1:loadings_ngenes]
probesPC4pos <- rankedGeneLoadings(p, pc = 4, decreasing = TRUE)[1:loadings_ngenes]
probesPC4neg <- rankedGeneLoadings(p, pc = 4, decreasing = FALSE)[1:loadings_ngenes]
if (return_ranked_gene_loadings)
return(
list(PC1 = list(posLoad = probesPC1pos, negLoad = probesPC1neg),
PC2 = list(posLoad = probesPC2pos, negLoad = probesPC2neg),
PC3 = list(posLoad = probesPC3pos, negLoad = probesPC3neg),
PC4 = list(posLoad = probesPC4pos, negLoad = probesPC4neg)
)
)
message("Ranking genes by the loadings ... done!")
message("Extracting functional categories enriched in the gene subsets ...")
topGOpc1pos <- mosdef::run_topGO(de_genes = probesPC1pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc1neg <- mosdef::run_topGO(de_genes = probesPC1neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc2pos <- mosdef::run_topGO(de_genes = probesPC2pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc2neg <- mosdef::run_topGO(de_genes = probesPC2neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc3pos <- mosdef::run_topGO(de_genes = probesPC3pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc3neg <- mosdef::run_topGO(de_genes = probesPC3neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc4pos <- mosdef::run_topGO(de_genes = probesPC4pos,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
topGOpc4neg <- mosdef::run_topGO(de_genes = probesPC4neg,
bg_genes = BGids, annot = annFUN.org, mapping = annopkg, ...)
goEnrichs <- list(PC1 = list(posLoad = topGOpc1pos, negLoad = topGOpc1neg),
PC2 = list(posLoad = topGOpc2pos, negLoad = topGOpc2neg),
PC3 = list(posLoad = topGOpc3pos, negLoad = topGOpc3neg),
PC4 = list(posLoad = topGOpc4pos, negLoad = topGOpc4neg)
)
message("Extracting functional categories enriched in the gene subsets ... done!")
attr(goEnrichs, "n_genesforpca") <- pca_ngenes
return(goEnrichs)
}
rankedGeneLoadings <- function(x, pc = 1, decreasing = TRUE) {
# works directly on the prcomp object
return(rownames(x$rotation)[order(x$rotation[, pc], decreasing = decreasing)])
}
#' Functional interpretation of the principal components, based on simple
#' overrepresentation analysis
#'
#' Extracts the genes with the highest loadings for each principal component, and
#' performs functional enrichment analysis on them using the simple and quick routine
#' provided by the `limma` package
#'
#' @param se A [DESeq2::DESeqTransform()] object, with data in `assay(se)`,
#' produced for example by either [DESeq2::rlog()] or
#' [DESeq2::varianceStabilizingTransformation()]
#' @param pca_ngenes Number of genes to use for the PCA
#' @param inputType Input format type of the gene identifiers. Deafults to `ENSEMBL`, that then will
#' be converted to ENTREZ ids. Can assume values such as `ENTREZID`,`GENENAME` or `SYMBOL`,
#' like it is normally used with the `select` function of `AnnotationDbi`
#' @param organism Character abbreviation for the species, using `org.XX.eg.db` for annotation
#' @param loadings_ngenes Number of genes to extract the loadings (in each direction)
#' @param background_genes Which genes to consider as background.
#' @param scale Logical, defaults to FALSE, scale values for the PCA
#' @param ... Further parameters to be passed to the goana routine
#'
#' @return A nested list object containing for each principal component the terms enriched
#' in each direction. This object is to be thought in combination with the displaying feature
#' of the main [pcaExplorer()] function
#'
#' @examples
#' library("airway")
#' library("DESeq2")
#' library("limma")
#' data("airway", package = "airway")
#' airway
#' dds_airway <- DESeqDataSet(airway, design = ~ cell + dex)
#' \dontrun{
#' rld_airway <- rlogTransformation(dds_airway)
#' goquick_airway <- limmaquickpca2go(rld_airway,
#' pca_ngenes = 10000,
#' inputType = "ENSEMBL",
#' organism = "Hs")
#' }
#'
#' @export
limmaquickpca2go <- function(se,
pca_ngenes = 10000,
inputType = "ENSEMBL",
organism = "Mm",
loadings_ngenes = 500,
background_genes = NULL,
scale = FALSE,
... # further parameters to be passed to the topgo routine
) {
annopkg <- paste0("org.", organism, ".eg.db")
if (!require(annopkg, character.only = TRUE)) {
stop("The package", annopkg, "is not installed/available. Try installing it with BiocManager::install() ?")
}
exprsData <- assay(se)
if (is.null(background_genes)) {
if (is(se, "DESeqDataSet")) {
BGids <- rownames(se)[rowSums(counts(se)) > 0]
} else if (is(se, "DESeqTransform")) {
BGids <- rownames(se)[rowSums(assay(se)) != 0]
} else {
BGids <- rownames(se)
}
} else {
BGids <- background_genes
}
exprsData <- assay(se)[order(rowVars(assay(se)), decreasing = TRUE), ]
exprsData <- exprsData[1:pca_ngenes, ]
rv <- rowVars(exprsData)
dropped <- sum(rv == 0)
if (dropped > 0)
message("Dropped ", dropped, " genes with 0 variance")
exprsData <- exprsData[rv > 0, ]
message("After subsetting/filtering for invariant genes, working on a ", nrow(exprsData), "x", ncol(exprsData), " expression matrix\n")
p <- prcomp(t(exprsData), scale = scale, center = TRUE)
message("Ranking genes by the loadings ...")
probesPC1pos <- rankedGeneLoadings(p, pc = 1, decreasing = TRUE)[1:loadings_ngenes]
probesPC1neg <- rankedGeneLoadings(p, pc = 1, decreasing = FALSE)[1:loadings_ngenes]
probesPC2pos <- rankedGeneLoadings(p, pc = 2, decreasing = TRUE)[1:loadings_ngenes]
probesPC2neg <- rankedGeneLoadings(p, pc = 2, decreasing = FALSE)[1:loadings_ngenes]
probesPC3pos <- rankedGeneLoadings(p, pc = 3, decreasing = TRUE)[1:loadings_ngenes]
probesPC3neg <- rankedGeneLoadings(p, pc = 3, decreasing = FALSE)[1:loadings_ngenes]
probesPC4pos <- rankedGeneLoadings(p, pc = 4, decreasing = TRUE)[1:loadings_ngenes]
probesPC4neg <- rankedGeneLoadings(p, pc = 4, decreasing = FALSE)[1:loadings_ngenes]
## convert ensembl to entrez ids
probesPC1pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC1pos, column = "ENTREZID", keytype = inputType)
probesPC1neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC1neg, column = "ENTREZID", keytype = inputType)
probesPC2pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC2pos, column = "ENTREZID", keytype = inputType)
probesPC2neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC2neg, column = "ENTREZID", keytype = inputType)
probesPC3pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC3pos, column = "ENTREZID", keytype = inputType)
probesPC3neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC3neg, column = "ENTREZID", keytype = inputType)
probesPC4pos_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC4pos, column = "ENTREZID", keytype = inputType)
probesPC4neg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = probesPC4neg, column = "ENTREZID", keytype = inputType)
bg_ENTREZ <- AnnotationDbi::mapIds(eval(parse(text = annopkg)), keys = BGids, column = "ENTREZID", keytype = inputType)
message("Ranking genes by the loadings ... done!")
message("Extracting functional categories enriched in the gene subsets ...")
quickGOpc1pos <- limma::topGO(limma::goana(probesPC1pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("1")
quickGOpc1neg <- limma::topGO(limma::goana(probesPC1neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("2")
quickGOpc2pos <- limma::topGO(limma::goana(probesPC2pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("3")
quickGOpc2neg <- limma::topGO(limma::goana(probesPC2neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("4")
quickGOpc3pos <- limma::topGO(limma::goana(probesPC3pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("5")
quickGOpc3neg <- limma::topGO(limma::goana(probesPC3neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("6")
quickGOpc4pos <- limma::topGO(limma::goana(probesPC4pos_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("7")
quickGOpc4neg <- limma::topGO(limma::goana(probesPC4neg_ENTREZ, bg_ENTREZ, species = organism, ...), ontology = "BP", number = 200); message("8")
quickGOpc1pos <- quickGOpc1pos[order(quickGOpc1pos$P.DE), ]
quickGOpc1neg <- quickGOpc1neg[order(quickGOpc1neg$P.DE), ]
quickGOpc2pos <- quickGOpc2pos[order(quickGOpc2pos$P.DE), ]
quickGOpc2neg <- quickGOpc2neg[order(quickGOpc2neg$P.DE), ]
quickGOpc3pos <- quickGOpc3pos[order(quickGOpc3pos$P.DE), ]
quickGOpc3neg <- quickGOpc3neg[order(quickGOpc3neg$P.DE), ]
quickGOpc4pos <- quickGOpc4pos[order(quickGOpc4pos$P.DE), ]
quickGOpc4neg <- quickGOpc4neg[order(quickGOpc4neg$P.DE), ]
goEnrichs <- list(PC1 = list(posLoad = quickGOpc1pos, negLoad = quickGOpc1neg),
PC2 = list(posLoad = quickGOpc2pos, negLoad = quickGOpc2neg),
PC3 = list(posLoad = quickGOpc3pos, negLoad = quickGOpc3neg),
PC4 = list(posLoad = quickGOpc4pos, negLoad = quickGOpc4neg)
)
message("Extracting functional categories enriched in the gene subsets ... done!")
attr(goEnrichs, "n_genesforpca") <- pca_ngenes
return(goEnrichs)
}
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