sbea: Set-based enrichment analysis (SBEA)
In lgeistlinger/EnrichmentBrowser: Seamless navigation through combined results of set-based and network-based enrichment analysis
sbeaMethods R Documentation
Set-based enrichment analysis (SBEA)
Description
This is the main function for the enrichment analysis of gene sets. It
implements and wraps existing implementations of several frequently used
methods and allows a flexible inspection of resulting gene set rankings.
Usage
sbeaMethods()
sbea(
method = EnrichmentBrowser::sbeaMethods(),
se,
gs,
alpha = 0.05,
perm = 1000,
padj.method = "none",
out.file = NULL,
browse = FALSE,
assay = "auto",
...
)
Arguments
method
Set-based enrichment analysis method. Currently, the
following set-based enrichment analysis methods are supported: ‘ora’,
‘safe’, ‘gsea’, ‘padog’, ‘roast’,
‘camera’, ‘gsa’, ‘gsva’, ‘globaltest’,
‘samgs’, ‘ebm’, and ‘mgsa’. For basic ora also set
'perm=0'. Default is ‘ora’. This can also be a
user-defined function implementing a set-based enrichment method. See Details.
se
Expression dataset. An object of class
SummarizedExperiment. Mandatory minimal annotations:
colData column storing binary group assignment (named
"GROUP")
rowData column storing (log2) fold changes of differential
expression between sample groups (named "FC")
rowData column storing
adjusted (corrected for multiple testing) p-values of differential
expression between sample groups (named "ADJ.PVAL")
Additional optional
annotations:
colData column defining paired samples or
sample blocks (named "BLOCK")
metadata slot named "annotation" giving
the organism under investigation in KEGG three letter code (e.g. "hsa" for
Homo sapiens)
metadata slot named "dataType" indicating the expression
data type ("ma" for microarray, "rseq" for RNA-seq)
gs
Gene sets. Either a list of gene sets (character vectors of gene
IDs) or a text file in GMT format storing all gene sets under investigation.
alpha
Statistical significance level. Defaults to 0.05.
perm
Number of permutations of the sample group assignments.
Defaults to 1000. For basic ora set 'perm=0'. Using
method="gsea" and 'perm=0' invokes the permutation approximation from the
npGSEA package.
padj.method
Method for adjusting nominal gene set p-values to
multiple testing. For available methods see the man page of the stats
function p.adjust. Defaults to'none', i.e. leaves the nominal
gene set p-values unadjusted.
out.file
Optional output file the gene set ranking will be written
to.
browse
Logical. Should results be displayed in the browser for
interactive exploration? Defaults to FALSE.
assay
Character. The name of the assay for enrichment
analysis if se is a SummarizedExperiment with
*multiple assays*. Defaults to "auto", which automatically determines
the appropriate assay based on data type provided and enrichment method selected.
See details.
...
Additional arguments passed to individual sbea methods. This
includes currently for ORA and MGSA:
beta: Log2 fold change
significance level. Defaults to 1 (2-fold).
sig.stat: decides which
statistic is used for determining significant DE genes. Options are:
'p' (Default): genes with adjusted p-value below alpha.
'fc': genes with abs(log2(fold change)) above beta
'&': p & fc
(logical AND)
'|': p | fc (logical OR)
'xxp': top xx % of genes
sorted by adjusted p-value
'xxfc' top xx % of genes sorted by absolute
log2 fold change.
Details
'ora': overrepresentation analysis, simple and frequently used test based on
the hypergeometric distribution (see Goeman and Buhlmann, 2007, for a
critical review).
'safe': significance analysis of function and expression, generalization of
ORA, includes other test statistics, e.g. Wilcoxon's rank sum, and allows to
estimate the significance of gene sets by sample permutation; implemented in
the safe package (Barry et al., 2005).
'gsea': gene set enrichment analysis, frequently used and widely accepted,
uses a Kolmogorov-Smirnov statistic to test whether the ranks of the
p-values of genes in a gene set resemble a uniform distribution (Subramanian
et al., 2005).
'padog': pathway analysis with down-weighting of overlapping genes,
incorporates gene weights to favor genes appearing in few pathways versus
genes that appear in many pathways; implemented in the PADOG package.
'roast': rotation gene set test, uses rotation instead of permutation for
assessment of gene set significance; implemented in the limma and edgeR
packages for microarray and RNA-seq data, respectively.
'camera': correlation adjusted mean rank gene set test, accounts for
inter-gene correlations as implemented in the limma and edgeR packages for
microarray and RNA-seq data, respectively.
'gsa': gene set analysis, differs from GSEA by using the maxmean statistic,
i.e. the mean of the positive or negative part of gene scores in the gene
set; implemented in the GSA package.
'gsva': gene set variation analysis, transforms the data from a gene by
sample matrix to a gene set by sample matrix, thereby allowing the
evaluation of gene set enrichment for each sample; implemented in the GSVA
package.
'globaltest': global testing of groups of genes, general test of groups of
genes for association with a response variable; implemented in the
globaltest package.
'samgs': significance analysis of microarrays on gene sets, extends the SAM
method for single genes to gene set analysis (Dinu et al., 2007).
'ebm': empirical Brown's method, combines p-values of genes in a gene set
using Brown's method to combine p-values from dependent tests; implemented
in the EmpiricalBrownsMethod package.
'mgsa': model-based gene set analysis, Bayesian modeling approach taking set
overlap into account by working on all sets simultaneously, thereby reducing
the number of redundant sets; implemented in the mgsa package.
It is also possible to use additional set-based enrichment methods. This
requires to implement a function that takes 'se' and 'gs'
as arguments and returns a numeric vector 'ps' storing the resulting p-value
for each gene set in 'gs'. This vector must be named accordingly (i.e.
names(ps) == names(gs)). See examples.
Using a SummarizedExperiment with *multiple assays*:
For the typical use case within the EnrichmentBrowser workflow this will
be a SummarizedExperiment with two assays: (i) an assay
storing the *raw* expression values, and (ii) an assay storing the *norm*alized
expression values as obtained with the normalize function.
In this case, assay = "auto" will *auto*matically determine the assay
based on the data type provided and the enrichment method selected.
For usage outside of the typical workflow, the assay argument can be
used to provide the name of the assay for the enrichment analysis.
Value
sbeaMethods: a character vector of currently supported methods;
sbea: if(is.null(out.file)): an enrichment analysis result object that can
be detailedly explored by calling eaBrowse and from which a
flat gene set ranking can be extracted by calling gsRanking.
If 'out.file' is given, the ranking is written to the specified file.
Author(s)
Ludwig Geistlinger
References
Geistlinger at al. (2020) Towards a gold standard for benchmarking
gene set enrichment analysis. Briefings in Bioinformatics.
Goeman and Buhlmann (2007) Analyzing gene expression data in
terms of gene sets: methodological issues. Bioinformatics, 23:980-7.
Subramanian et al. (2005) Gene Set Enrichment Analysis: a knowledge-based
approach for interpreting genome-wide expression profiles. PNAS, 102:15545-50.
See Also
Input: readSE, probe2gene
getGenesets to retrieve gene sets from databases such as GO
and KEGG.
Output: gsRanking to retrieve the ranked list of gene sets.
eaBrowse for exploration of resulting gene sets.
Other: nbea to perform network-based enrichment analysis.
combResults to combine results from different methods.
Examples
# currently supported methods
sbeaMethods()
# (1) expression data:
# simulated expression values of 100 genes
# in two sample groups of 6 samples each
se <- makeExampleData(what="SE")
se <- deAna(se)
# (2) gene sets:
# draw 10 gene sets with 15-25 genes
gs <- makeExampleData(what="gs", gnames=names(se))
# (3) make 2 artificially enriched sets:
sig.genes <- names(se)[rowData(se)$ADJ.PVAL < 0.1]
gs[[1]] <- sample(sig.genes, length(gs[[1]]))
gs[[2]] <- sample(sig.genes, length(gs[[2]]))
# (4) performing the enrichment analysis
ea.res <- sbea(method="ora", se=se, gs=gs, perm=0)
# (5) result visualization and exploration
gsRanking(ea.res)
# using your own tailored function as enrichment method
dummySBEA <- function(se, gs)
{
sig.ps <- sample(seq(0, 0.05, length=1000), 5)
nsig.ps <- sample(seq(0.1, 1, length=1000), length(gs)-5)
ps <- sample(c(sig.ps, nsig.ps), length(gs))
names(ps) <- names(gs)
return(ps)
}
ea.res2 <- sbea(method=dummySBEA, se=se, gs=gs)
gsRanking(ea.res2)
lgeistlinger/EnrichmentBrowser documentation built on May 9, 2024, 7:22 p.m.
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| sbeaMethods | R Documentation |
Set-based enrichment analysis (SBEA)
Description
This is the main function for the enrichment analysis of gene sets. It implements and wraps existing implementations of several frequently used methods and allows a flexible inspection of resulting gene set rankings.
Usage
sbeaMethods()
sbea(
method = EnrichmentBrowser::sbeaMethods(),
se,
gs,
alpha = 0.05,
perm = 1000,
padj.method = "none",
out.file = NULL,
browse = FALSE,
assay = "auto",
...
)
Arguments
method |
Set-based enrichment analysis method. Currently, the following set-based enrichment analysis methods are supported: ‘ora’, ‘safe’, ‘gsea’, ‘padog’, ‘roast’, ‘camera’, ‘gsa’, ‘gsva’, ‘globaltest’, ‘samgs’, ‘ebm’, and ‘mgsa’. For basic ora also set 'perm=0'. Default is ‘ora’. This can also be a user-defined function implementing a set-based enrichment method. See Details. |
se |
Expression dataset. An object of class
Additional optional annotations:
|
gs |
Gene sets. Either a list of gene sets (character vectors of gene IDs) or a text file in GMT format storing all gene sets under investigation. |
alpha |
Statistical significance level. Defaults to 0.05. |
perm |
Number of permutations of the sample group assignments. Defaults to 1000. For basic ora set 'perm=0'. Using method="gsea" and 'perm=0' invokes the permutation approximation from the npGSEA package. |
padj.method |
Method for adjusting nominal gene set p-values to
multiple testing. For available methods see the man page of the stats
function |
out.file |
Optional output file the gene set ranking will be written to. |
browse |
Logical. Should results be displayed in the browser for interactive exploration? Defaults to FALSE. |
assay |
Character. The name of the assay for enrichment
analysis if |
... |
Additional arguments passed to individual sbea methods. This includes currently for ORA and MGSA:
|
Details
'ora': overrepresentation analysis, simple and frequently used test based on the hypergeometric distribution (see Goeman and Buhlmann, 2007, for a critical review).
'safe': significance analysis of function and expression, generalization of ORA, includes other test statistics, e.g. Wilcoxon's rank sum, and allows to estimate the significance of gene sets by sample permutation; implemented in the safe package (Barry et al., 2005).
'gsea': gene set enrichment analysis, frequently used and widely accepted, uses a Kolmogorov-Smirnov statistic to test whether the ranks of the p-values of genes in a gene set resemble a uniform distribution (Subramanian et al., 2005).
'padog': pathway analysis with down-weighting of overlapping genes, incorporates gene weights to favor genes appearing in few pathways versus genes that appear in many pathways; implemented in the PADOG package.
'roast': rotation gene set test, uses rotation instead of permutation for assessment of gene set significance; implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively.
'camera': correlation adjusted mean rank gene set test, accounts for inter-gene correlations as implemented in the limma and edgeR packages for microarray and RNA-seq data, respectively.
'gsa': gene set analysis, differs from GSEA by using the maxmean statistic, i.e. the mean of the positive or negative part of gene scores in the gene set; implemented in the GSA package.
'gsva': gene set variation analysis, transforms the data from a gene by sample matrix to a gene set by sample matrix, thereby allowing the evaluation of gene set enrichment for each sample; implemented in the GSVA package.
'globaltest': global testing of groups of genes, general test of groups of genes for association with a response variable; implemented in the globaltest package.
'samgs': significance analysis of microarrays on gene sets, extends the SAM method for single genes to gene set analysis (Dinu et al., 2007).
'ebm': empirical Brown's method, combines p-values of genes in a gene set using Brown's method to combine p-values from dependent tests; implemented in the EmpiricalBrownsMethod package.
'mgsa': model-based gene set analysis, Bayesian modeling approach taking set overlap into account by working on all sets simultaneously, thereby reducing the number of redundant sets; implemented in the mgsa package.
It is also possible to use additional set-based enrichment methods. This requires to implement a function that takes 'se' and 'gs' as arguments and returns a numeric vector 'ps' storing the resulting p-value for each gene set in 'gs'. This vector must be named accordingly (i.e. names(ps) == names(gs)). See examples.
Using a SummarizedExperiment with *multiple assays*:
For the typical use case within the EnrichmentBrowser workflow this will
be a SummarizedExperiment with two assays: (i) an assay
storing the *raw* expression values, and (ii) an assay storing the *norm*alized
expression values as obtained with the normalize function.
In this case, assay = "auto" will *auto*matically determine the assay
based on the data type provided and the enrichment method selected.
For usage outside of the typical workflow, the assay argument can be
used to provide the name of the assay for the enrichment analysis.
Value
sbeaMethods: a character vector of currently supported methods;
sbea: if(is.null(out.file)): an enrichment analysis result object that can
be detailedly explored by calling eaBrowse and from which a
flat gene set ranking can be extracted by calling gsRanking.
If 'out.file' is given, the ranking is written to the specified file.
Author(s)
Ludwig Geistlinger
References
Geistlinger at al. (2020) Towards a gold standard for benchmarking gene set enrichment analysis. Briefings in Bioinformatics.
Goeman and Buhlmann (2007) Analyzing gene expression data in terms of gene sets: methodological issues. Bioinformatics, 23:980-7.
Subramanian et al. (2005) Gene Set Enrichment Analysis: a knowledge-based approach for interpreting genome-wide expression profiles. PNAS, 102:15545-50.
See Also
Input: readSE, probe2gene
getGenesets to retrieve gene sets from databases such as GO
and KEGG.
Output: gsRanking to retrieve the ranked list of gene sets.
eaBrowse for exploration of resulting gene sets.
Other: nbea to perform network-based enrichment analysis.
combResults to combine results from different methods.
Examples
# currently supported methods
sbeaMethods()
# (1) expression data:
# simulated expression values of 100 genes
# in two sample groups of 6 samples each
se <- makeExampleData(what="SE")
se <- deAna(se)
# (2) gene sets:
# draw 10 gene sets with 15-25 genes
gs <- makeExampleData(what="gs", gnames=names(se))
# (3) make 2 artificially enriched sets:
sig.genes <- names(se)[rowData(se)$ADJ.PVAL < 0.1]
gs[[1]] <- sample(sig.genes, length(gs[[1]]))
gs[[2]] <- sample(sig.genes, length(gs[[2]]))
# (4) performing the enrichment analysis
ea.res <- sbea(method="ora", se=se, gs=gs, perm=0)
# (5) result visualization and exploration
gsRanking(ea.res)
# using your own tailored function as enrichment method
dummySBEA <- function(se, gs)
{
sig.ps <- sample(seq(0, 0.05, length=1000), 5)
nsig.ps <- sample(seq(0.1, 1, length=1000), length(gs)-5)
ps <- sample(c(sig.ps, nsig.ps), length(gs))
names(ps) <- names(gs)
return(ps)
}
ea.res2 <- sbea(method=dummySBEA, se=se, gs=gs)
gsRanking(ea.res2)
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