Extract functional terms enriched in the DE genes, based on clusterProfiler

A wrapper for extracting functional GO terms enriched in a list of (DE) genes, based on the algorithm and the implementation in the clusterProfiler package

Usage

run_cluPro(
  de_container = NULL,
  res_de = NULL,
  de_genes = NULL,
  bg_genes = NULL,
  top_de = NULL,
  FDR_threshold = 0.05,
  min_counts = 0,
  mapping = "org.Hs.eg.db",
  de_type = "up_and_down",
  keyType = "SYMBOL",
  verbose = TRUE,
  ...
)

Arguments

de_container

An object containing the data for a Differential Expression workflow (e.g. DESeq2, edgeR or limma). Currently, this can be a DESeqDataSet object, normally obtained after running your data through the DESeq2 framework.

res_de

An object containing the results of the Differential Expression analysis workflow (e.g. DESeq2, edgeR or limma). Currently, this can be a DESeqResults object created using the DESeq2 framework.

de_genes

A vector of (differentially expressed) genes

bg_genes

A vector of background genes, e.g. all (expressed) genes in the assays

top_de

numeric, how many of the top differentially expressed genes to use for the enrichment analysis. Attempts to reduce redundancy. Assumes the data is sorted by padj (default in DESeq2).

FDR_threshold

The pvalue threshold to us for counting genes as de. Default is 0.05

min_counts

numeric, min number of counts a gene needs to have to be included in the geneset that the de genes are compared to. Default is 0, recommended only for advanced users.

mapping

Which org.XX.eg.db package to use for annotation - select according to the species

de_type

One of: 'up', 'down', or 'up_and_down' Which genes to use for GOterm calculations

keyType

Gene format to input into enrichGO from clusterProfiler. If res_de and de_container are used use "SYMBOL" for more information check the enrichGO documentation

verbose

Logical, whether to add messages telling the user which steps were taken

...

Further parameters to use for the clusterProfiler::enrichGO() function from clusterProfiler.

Value

A table containing the computed GO Terms and related enrichment scores.

See also

clusterProfiler::enrichGO() for the underlying method

Other Enrichment functions: run_goseq(), run_topGO()

Examples

library("macrophage")
library("DESeq2")
data(gse, package = "macrophage")

dds_macrophage <- DESeqDataSet(gse, design = ~ line + condition)
#> using counts and average transcript lengths from tximeta
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rownames(dds_macrophage) <- substr(rownames(dds_macrophage), 1, 15)
keep <- rowSums(counts(dds_macrophage) >= 10) >= 6
dds_macrophage <- dds_macrophage[keep, ]
dds_macrophage <- DESeq(dds_macrophage)
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#> estimating size factors
#> using 'avgTxLength' from assays(dds), correcting for library size
#> estimating dispersions
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#> gene-wise dispersion estimates
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#> mean-dispersion relationship
#> final dispersion estimates
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#> fitting model and testing
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data(res_de_macrophage, package = "mosdef")

library("AnnotationDbi")
library("org.Hs.eg.db")
library("clusterProfiler")
#> clusterProfiler v4.11.1  For help: https://yulab-smu.top/biomedical-knowledge-mining-book/
#> 
#> If you use clusterProfiler in published research, please cite:
#> T Wu, E Hu, S Xu, M Chen, P Guo, Z Dai, T Feng, L Zhou, W Tang, L Zhan, X Fu, S Liu, X Bo, and G Yu. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. The Innovation. 2021, 2(3):100141
#> 
#> Attaching package: ‘clusterProfiler’
#> The following object is masked from ‘package:AnnotationDbi’:
#> 
#>     select
#> The following object is masked from ‘package:IRanges’:
#> 
#>     slice
#> The following object is masked from ‘package:S4Vectors’:
#> 
#>     rename
#> The following object is masked from ‘package:stats’:
#> 
#>     filter
CluProde_macrophage <- run_cluPro(
  res_de = res_macrophage_IFNg_vs_naive,
  de_container = dds_macrophage,
  mapping = "org.Hs.eg.db"
)
#> 'select()' returned 1:many mapping between keys and columns
#> 'select()' returned 1:many mapping between keys and columns
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#> 'select()' returned 1:many mapping between keys and columns
#> Your dataset has 1024 DE genes.
#> You selected 1024 (100.00%) genes for the enrichment analysis.
#> You are analyzing up_and_down-regulated genes in the `res_de` container