Performing gene set enrichment analyses with sparrow
29 October 2024
Abstract
The sparrow package facilitates the use of gene sets in the analysis of high throughput genomics data. It provides simple execution and comparison of several GSEA approaches through a unified interface within the user’s workspace or interactively via a shiny application provided by the sparrow.shiny package. This package also provides an easy wrapper to single sample gene set scoring and geneset-centric heatmaps for visualization. sparrow package version: 1.12.0
Package
sparrow 1.12.0
Contents
1 Overview
The {sparrow} package was built to facilitate the use of gene sets in the
analysis of high throughput genomics data (primarily RNA-seq). It does so
by providing these top-line functionalities:
- The
seasfunction is a wrapper that orchestrates the execution of any number of user-specified gene set enrichment analyses (GSEA) over a particular experimental contrast of interest. This will create aSparrowResultobject which stores the results of each GSEA method internally, allowing for easy query and retrieval. - A sister
{sparrow.shiny}package provides anexplorefunction, which is invoked onSparrowResultobjects returned from a call toseas. The shiny application facilitates interactive exploration of these GSEA results. This application can also be deployed to a shiny server and can be initialized by uploading a serializedSparrowResult*.rdsfile. - An “over representation analysis” method
ora()which wraps the biased enrichment functionality found withinlimma::keggaand generalizes it to work against data.frame inputs with arbitrary genesets. - The
scoreSingleSamplesfunction is a wrapper that enables the user to generate single sample gene set scores using a variety of different single sample gene set scoring methods. - Convenience gene set collection retrieval functions that return
BiocSets from widely used databases, likegetMSigCollection()(MSigDB),getKeggCollection()(KEGG),getPantherCollection()(PANTHER database), andgetReactomeCollection()(Reactome) with support for different organisms and identifier types (partially).
The initial GSEA methods that sparrow wrapped were the ones provided by limma and edgeR. As such, many analyses using sparrow expect you to re-use the same data objects used for differential expression analysis, namely:
- Expression data (an
EList,DGEList, or expression matrix) - A design matrix
- A contrast vector/matrix (if your design and comparison require it)
Other methods only require the user to provide a ranked vector of statistics that represent some differential expression statistic per gene, and the GSEA is performed by analyzing the ranks of genes within this vector.
The user can invoke one seas() call that can orchestrate multiple analyses
of any type.
Currently supported gene set enrichment methods include:
## method test_type package
## 1 camera preranked limma
## 2 cameraPR preranked limma
## 3 fgsea preranked fgsea
## 4 ora ora ora
## 5 fry preranked limma
## 6 roast preranked limma
## 7 romer preranked limma
## 8 goseq ora goseq
## 9 geneSetTest preranked limma
## 10 logFC preranked limma
## 11 svdGeneSetTest meta sparrowWhen using these methods in analyses that lead to publication, please cite the original papers that developed these methods and cite sparrow when its functionality assisted in your interpretation and analysis.
The sparrow package provides a small example expression dataset extracted from
the TCGA BRCA dataset, which is available via the exampleExpressionSet
function. In this vignette we will explore differential expression and gene
set enrichment analysis by examining differences between basal and her2 PAM50
subtypes.
2 Standard Workflow
Let’s begin by setting up our work environment for exploratory analysis using the sparrow package.
library(sparrow)
library(magrittr)
library(dplyr)
library(ggplot2)
library(ComplexHeatmap)
library(circlize)
library(edgeR)
library(data.table)
theme_set(theme_bw())Internally, sparrow leverages the
data.table package for fast
indexing and manipulation over data.frames. All functions that return
data.frame looking objects back have converted it from an data.table prior
to return. All such functions take an as.dt argument, which is set to FALSE
by default that controls this behavior. If you want {sparrow} to return a
data.table back to you from some function, try adding an as.dt = TRUE argument
to the end of the function call.
2.1 Data Setup
sparrow is most straightforward to use when our data objects and analysis are
performed with either the edgeR or voom/limma pipelines and when we use standard
gene identifiers (like esnemble) as rownames() to these objects.
The exampleExpressionSet function gives us just such an object. We call it
below in a manner that gives us an object that allows us to explore expression
differences between different subtypes of breast cancer.
Below you’ll find the $targets data.frame of the voomed EList
## Patient_ID Cancer_Status PAM50subtype
## TCGA-A2-A0CM-01A-31R-A034-07 TCGA-A2-A0CM tumor Basal
## TCGA-BH-A0RX-01A-21R-A084-07 TCGA-BH-A0RX tumor Basal
## TCGA-BH-A18Q-01A-12R-A12D-07 TCGA-BH-A18Q tumor Basal
## TCGA-B6-A0RU-01A-11R-A084-07 TCGA-B6-A0RU tumor Basal
## TCGA-BH-A18P-01A-11R-A12D-07 TCGA-BH-A18P tumor Her2
## TCGA-C8-A275-01A-21R-A16F-07 TCGA-C8-A275 tumor Her2
## TCGA-C8-A12Z-01A-11R-A115-07 TCGA-C8-A12Z tumor Her2
## TCGA-A2-A0T1-01A-21R-A084-07 TCGA-A2-A0T1 tumor Her2
## TCGA-AC-A3OD-01A-11R-A21T-07 TCGA-AC-A3OD tumor LumA
## TCGA-AN-A0XS-01A-22R-A109-07 TCGA-AN-A0XS tumor LumA
## TCGA-A2-A0EM-01A-11R-A034-07 TCGA-A2-A0EM tumor LumA
## TCGA-AR-A24O-01A-11R-A169-07 TCGA-AR-A24O tumor LumA
## TCGA-D8-A4Z1-01A-21R-A266-07 TCGA-D8-A4Z1 tumor LumANote that there are many tutorials online that outline how to generate expression matrices
for use with differential expression and analysis, such as the one that is returned from
the exampleExpressionSet function. Summarizing assay data into such a format is out
of scope for this vignette, but you can reference the
airway vignette
for full details (among others).
2.2 Data Analysis
We will identify the genes and genesets that are differentially expressed
between the basal and her2 subtypes. The vm object has already been voomd
using this design:
## Basal Her2 LumA
## TCGA-A2-A0CM-01A-31R-A034-07 1 0 0
## TCGA-BH-A0RX-01A-21R-A084-07 1 0 0
## TCGA-BH-A18Q-01A-12R-A12D-07 1 0 0
## TCGA-B6-A0RU-01A-11R-A084-07 1 0 0
## TCGA-BH-A18P-01A-11R-A12D-07 0 1 0
## TCGA-C8-A275-01A-21R-A16F-07 0 1 0
## TCGA-C8-A12Z-01A-11R-A115-07 0 1 0
## TCGA-A2-A0T1-01A-21R-A084-07 0 1 0
## TCGA-AC-A3OD-01A-11R-A21T-07 0 0 1
## TCGA-AN-A0XS-01A-22R-A109-07 0 0 1
## TCGA-A2-A0EM-01A-11R-A034-07 0 0 1
## TCGA-AR-A24O-01A-11R-A169-07 0 0 1
## TCGA-D8-A4Z1-01A-21R-A266-07 0 0 1
## attr(,"assign")
## [1] 1 1 1
## attr(,"contrasts")
## attr(,"contrasts")$PAM50subtype
## [1] "contr.treatment"We can test for differences between basla and her2 subtypes using the following contrast:
## Contrasts
## Levels BvH
## Basal 1
## Her2 -1
## LumA 02.2.1 Differential Gene Expression
In this section, we first show you the straightforward analysis you would do if you were only testing for differential gene expression.
With the data we have at hand, you would simply do the following:
2.2.2 Gene Set Enrichment Analysis
Given that we now have all of the pieces of data required for a differential
expression analysis, performing GSEA is trivial using the seas wrapper
function. We simply need to now define (1) the battery of gene sets we want to
test against, and (2) the GSEA methods we want to explore.
2.2.3 Gene Sets to Test
The sparrow package provides a GeneSetDb class to store collections of
gene sets. The GeneSetDb object is used heavily for the internal functionality
of {sparrow}, however you can provide sparrow with collections of gene sets
using other containers from the bioconductor universe, like a BiocSet::BiocSet
or a GSEABase::GeneSetCollection. This package provides convenience methods
to convert between these different types of gene set containers. Please refer
to The GeneSetDb Class section for more details.
The {sparrow} package also provides convenience methods to retrieve gene set
collections from different sourckes, like MSigDB,
PANTHER, KEGG, etc. These methods are named using the following
pattern: get<CollectionName>Collection() to return a BiocSet with the
gene sets from the collection, or get<CollectionName>GeneSetDb() to get
a GeneSetDb of the same.
We’ll use the getMSigGeneSetDb convenience function provided by the
sparrow package to load the hallmark ("h") and
c2 (curated) ("c2") gene set collections from MSigDB.
To retrieve a BiocSet of these same collections, you could do:
You can view a table of the gene sets defined inside a GeneSetDb (gdb)object
via its geneSets(gdb) accessor:
## collection name active N
## 1 C2 ABBUD_LIF_SIGNALING_1_DN FALSE 28
## 2 C2 ABBUD_LIF_SIGNALING_1_UP FALSE 43
## 3 C2 ABBUD_LIF_SIGNALING_2_DN FALSE 7
## 4 C2 ABBUD_LIF_SIGNALING_2_UP FALSE 13
## 5 C2 ABDELMOHSEN_ELAVL4_TARGETS FALSE 16
## 6 C2 ABDULRAHMAN_KIDNEY_CANCER_VHL_DN FALSE 132.3 Running sparrow
Performing multiple gene set enrichment analyses over your contrast of interest
simply requires you to provide a GeneSetDb (or BiocSet) object along with
your data and an enumeration of the methods you want to use in your analysis.
The call to seas() will perform these analyses and return a
SparrowResult object which you can then use for downstream analysis.
mg <- seas(
vm, gdb, c('camera', 'fry', 'ora'),
design = vm$design, contrast = cm[, 'BvH'],
# these parameters define which genes are differentially expressed
feature.max.padj = 0.05, feature.min.logFC = 1,
# for camera:
inter.gene.cor = 0.01,
# specifies the numeric covariate to bias-correct for
# "size" is found in the vm$genes data.frame, which makes its way to the
# internal DGE statistics table ... more on that later
feature.bias = "size")We will unpack the details of the seas() call shortly …
2.4 Implicit Differential Expression
First, let’s note that in addition to running a plethora of GSEA’s over our data
we’ve also run a standard differential expression analysis. If you’ve passed
a matrix, ExpressionSet or EList into seas(), a limma-based
lmFit %>% (eBayes|treat) %>% (topTable|topTreat) pipeline was run. If a
DGEList was passed, then seas utilizes the edgeR-based
glmQLFit %>% (glmQLFTest | glmTreat) %>% topTags pipeline.
The result of the internally run differential expression analysis is accessible
via a call to logFC function on the SparrowResult object:
## symbol entrez_id logFC t pval padj
## 1 A1BG 1 0.67012895 1.07951394 0.2982819 0.6858344
## 2 ADA 100 0.53844094 0.92401125 0.3708544 0.7415607
## 3 CDH2 1000 -0.08180996 -0.09901074 0.9225083 0.9795974
## 4 AKT3 10000 0.58338138 1.29502525 0.2158892 0.6125318
## 5 LOC100009676 100009676 -0.09581391 -0.26985709 0.7911366 0.9398579
## 6 MED6 10001 0.04505155 0.15082239 0.8822288 0.9701384We can confirm that the statistics generated internally in seas() mimic our
explicit analysis above by verifying that the t-statistics generated by both
approaches are identical.
comp <- tt %>%
select(entrez_id, logFC, t, pval=P.Value, padj=adj.P.Val) %>%
inner_join(lfc, by='entrez_id', suffix=c('.tt', '.mg'))
all.equal(comp$t.tt, comp$t.mg)## [1] TRUEThe internally performed differential expression analysis within the seas()
call can be customized almost as extensively as an explicitly performed analysis
that you would run using limma or edgeR by sending more parameters through
seas()’s ... argument.
See the
Custom Differential Expression
section further in the vignette as well as the help available in
?calculateIndividualLogFC (which is called inside the seas() function)
for more information.
2.5 Explicit GSEA
We also have the results of all the GSEA analyses that we specified to our
seas call via the methods parameter.
## SparrowResult (max FDR by collection set to 0.20%)
## ---------------------------------------------------
## collection method geneset_count sig_count sig_up sig_down
## 1 C2 camera 6150 349 206 143
## 2 H camera 50 6 5 1
## 3 C2 fry 6150 95 33 62
## 4 H fry 50 0 0 0
## 5 C2 ora 6150 96 33 63
## 6 H ora 50 3 1 2
## 7 C2 ora.down 6150 73 6 67
## 8 H ora.down 50 2 0 2
## 9 C2 ora.up 6150 24 21 3
## 10 H ora.up 50 0 0 0The table above enumerates the different GSEA methods run over each geneset
collection in the rows. The columns enumerate the number of genesets that the
collection has in total (geneset_count), and how many were found significant
at a given FDR, which is set to 20% by default. The show command for the
SparrowResult object simply calls the tabulateResults() function, which
you can call directly with the value of max.p that you might find more
appropriate.
2.6 Exploring Results
GSEA results can be examined interactively via the command line, or via a shiny
application. You can use the resultNames function to find out what GSEA
methods were run, and therefore available to you, within the the
SparrowResult object:
## [1] "camera" "fry" "ora" "ora.down" "ora.up"Note that when running an “over representation analysis” "ora" (or "goseq"),
it will be run three different ways. The tests will be run first by testing
all differentially expressed genes that meet a given set of min logFC and
max FDR thresholds, then separately for only genes that go up in your contrast,
and a third time for only the genes that go down.
The individual gene set statistics generated by each method are available via
the result function (or several can be returned with results):
You can identify genesets with the strongest enrichment by filtering and sorting against the appropriate columns. We can, for instance, identify which hallmark gene sets show the strongest enrichment as follows:
cam.res %>%
filter(padj < 0.1, collection == 'H') %>%
arrange(desc(mean.logFC)) %>%
select(name, n, mean.logFC, padj) %>%
head## name n mean.logFC padj
## 1 HALLMARK_MYC_TARGETS_V2 58 0.4461105 0.0002612790
## 2 HALLMARK_INTERFERON_ALPHA_RESPONSE 96 0.3916716 0.0874709010
## 3 HALLMARK_E2F_TARGETS 200 0.3465703 0.0001892151
## 4 HALLMARK_MYC_TARGETS_V1 200 0.2092836 0.0234431144You can also list the members of a geneset and their individual differential
expression statistics for the contrast under test using the geneSet function.
geneSet(mg, name = 'HALLMARK_WNT_BETA_CATENIN_SIGNALING') %>%
select(symbol, entrez_id, logFC, pval, padj) %>%
head()## symbol entrez_id logFC pval padj
## 1 HDAC5 10014 0.8984691 0.02253974 0.2522754
## 2 CSNK1E 1454 -0.1793725 0.52104817 0.8317753
## 3 CTNNB1 1499 0.2577554 0.54741640 0.8467181
## 4 JAG1 182 0.7293432 0.02496690 0.2625306
## 5 DVL2 1856 0.4921509 0.24186744 0.6362028
## 6 DKK1 22943 0.6567652 0.66735589 0.8982828The results provided in the table generated from a call to geneSet are
independant of GSEA method. The statistics appended to the gene set members
are simply the ones generated from a differential expression analysis.
2.7 Plotting
{sparrow} provides a number of interactive plotting facilities to explore the
enrichment of a single geneset under the given contrast. In the boxplots and
density plots shown below, the log fold changes (logFCs) (or t-statistics) for
all genes under the contrast are visualized in the “background” set, and these
same values are shown for the desired geneset under the “geneset” group.
The logFC (or t-statistics) of the genes in the gene set are plotted as points, which allow you to hover to identify the identity of the genes that land in the regions of the distributions you care about.
Including interactive plots increases the size of the vignette’s by a lot and
will be rejected by the bioconductor build servers, so all plots included in
this vignette are static snapshots of the javascript enabled plots you would
normally get from iplot().
Boxplot
boxplot of geneset log2FC’s
Density
density plot of geneset log2FC’s
GSEA plot
gsea plot of geneset log2FC’s
2.7.1 Interactive Exploration
A sister {sparrow.shiny} package is available that can be used
to interactively explore SparrowResult objects to help you try to make sense
of the enrichment hits you get (or not!). The application can be invoked as
follows:
Screen shot of interactive sparrow exploration
Please refer to the "sparrow-shiny" vignette in the
{sparrow.shiny} package for documentation on the application’s
use.
The {sparrow.shiny} package is currently only available to install from
GitHub, but will be available through Bioconductor soon.
3 Singe Sample Gene Set Scoring
It can be both convenient and effective to transform a gene-by-sample expression matrix to a geneset-by-sample expression matrix. By doing so, so we can quickly identify biological processes that are up/down regulated (loosely speaking) in each sample.
We can generate single sample gene set scores using the gene sets defined in a
GeneSetDb using the scoreSingleSamples function. This function takes a
GeneSetDb, an expression container, and a methods argument, which is
analagous to the methods argument in the seas() call: it defines
all of the scoring methos the user wants to apply to each sample.
Let’s pick a few gene sets to score our samples with for this exercise. We’ll take the significant hallmark gene sets, or any other significant gene set that has a large (on average) log fold change between conditions.
sig.res <- cam.res %>%
filter(padj < 0.05 & (grepl("HALLMARK", name) | abs(mean.logFC) >= 2))
gdb.sub <- gdb[geneSets(gdb)$name %in% sig.res$name]Refer to the Subsetting a GeneSetDb section to
learn how to subset a GeneSetDb object to create a derivative object with
fewer gene sets.
Recall that the GSEA analysis we performed was perfomed between the Basal and Her2 subtypes, so we will use an expression matrix that only has the samples from those two groups.
3.1 Generating Single Sample Gene Set Scores
Once we have a GeneSetDb object that contains all of the gene sets we wish
to use to create single sample gene set scores, we can use the
scoreSingleSamples function to produce these scores using a variety of
algorithmes, which the user species using the methods parameter.
The scoreSingleSamples function will return a long data.frame with
length(methods) * ncol(exprs) rows. Each row represents the score for the
given sample using the specified method. You can subset against the method
column to extract all of the single sample scores for a given method.
scores <- scoreSingleSamples(gdb.sub, vm.bh,
methods = c('ewm', 'ssgsea', 'zscore'),
ssgsea.norm = TRUE, unscale=FALSE, uncenter=FALSE,
as.dt = TRUE)We can see how the scores from different methods compare to each other:
# We miss you, reshape2::acast
sw <- dcast(scores, name + sample_id ~ method, value.var="score")
corplot(sw[, -(1:2), with = FALSE], cluster=TRUE)
It is, perhaps, interesting to compare how the ewm method scores change when
we choose not to “uncenter” and “unscale” them:
ewmu <- scoreSingleSamples(gdb.sub, vm.bh,methods = "ewm",
unscale = TRUE, uncenter = TRUE, as.dt = TRUE)
ewmu[, method := "ewm_unscale"]
scores.all <- rbind(scores, ewmu)
swa <- dcast(scores.all, name + sample_id ~ method, value.var="score")
corplot(swa[, -(1:2), with = FALSE], cluster=TRUE)
Further exposition on the “ewm” (eigenWeightedMean) scoring method can be
found in the ?eigenWeightedMean function.
3.2 Visualizing Single Sample Gene Set Scores
The “long” data.frame nature of the results produced by scoreSingleSamples
makes it convenient to use with graphing libraries like ggplot2 so that we can
create arbitrary visualizations. Creating boxplots for gene sets per subtype
is an easy way to explore these results.
Let’s annotate each row in scores.all with the subtype annotation and observe
how these methods score each sample for a few gene sets.
all.scores <- scores.all %>%
inner_join(select(vm.bh$targets, sample_id=Sample_ID, subtype=PAM50subtype),
by = "sample_id")
some.scores <- all.scores %>%
filter(name %in% head(unique(all.scores$name), 5))
ggplot(some.scores, aes(subtype, score)) +
geom_boxplot(outlier.shape=NA) +
geom_jitter(width=0.25) +
facet_grid(name ~ method)
4 Gene Set Based Heatmap with mgheatmap
We often want to create expression based heatmaps that highlight the behavior of
gene sets across our samples. The mgheatmap function uses the
ComplexHeatmap package to create two different types of heatmaps:
- Gene based heatmaps, that split the genes (rows) based on their genesets
- Single sample gene set based heatmaps, optionally split by gene set collection.
The mgheatmap function has a set of arguments that customize how the heatmap
is to be created (gene level vs. gene set level, whether to split it, etcv) and
will also use the ... argument to pass any parameters down to the inner
ComplexHeatmap::Heatmap function call and customize its behavior. The
mgheatmap function returns a ComplexHeatmap,Heatmap object for plotting
or combining with other ComplexHeatmap heatmaps or annotations in order to
create arbitrarily complex/informative heatmap figures.
4.1 Gene level based heatmap (from genesets)
You can plot a heatmap of the genes from a predefined set of gene sets by
providing the gene sets you want to visualize in a GeneSetDb object.
We’ll create a new GeneSetDb object using the first two gene sets in gdb.sub
and draw a heatmap of their expression.
gs.sub <- geneSets(gdb.sub)
gdb.2 <- gdb.sub[geneSets(gdb.sub)$name %in% head(gs.sub$name, 2)]
col.anno <- HeatmapAnnotation(
df = vm.bh$targets[, 'PAM50subtype', drop = FALSE],
col = list(PAM50subtype = c(Basal = "gray", Her2 = "black")))
mgheatmap(vm.bh, gdb.2, aggregate.by = "none", split = TRUE,
show_row_names = FALSE, show_column_names = FALSE,
recenter = TRUE, top_annotation = col.anno, zlim = c(-3, 3))
4.2 Gene set-based heatmap
You can often get a higher information:ink ratio by plotting heatmaps based on single sample gene set scores as opposed to the genes that make up a geneset.
Let’s see what the simple 2-geneset version of the heatmap above looks like:
mgheatmap(vm.bh, gdb.2, aggregate.by = "ewm", split = FALSE,
show_row_names = TRUE, show_column_names = FALSE,
top_annotation = col.anno)
Plotted in this way, we can now show the activity of a greater number of genesets
mgheatmap(vm.bh, gdb.sub,
aggregate.by = 'ewm', split=TRUE, recenter = TRUE,
show_row_names=TRUE, show_column_names=FALSE,
top_annotation=col.anno, zlim = c(-2.5, 2.5))
5 The GeneSetDb Class
The GeneSetDb class was developed to address the internal needs of the sparrow
package for fast look up, subsetting, cross reference, etc. of a collection of
gene sets. At the time (~2015), it was developed because the classes used for
this purpose in the bioconductor ecosystem (a GSEABase::GeneSetCollection,
or a simple list of gene vectors) didn’t cut the mustard.
More recently, bioc-core has developed a new class called a BiocSet that is
feature-rich and shares significant overlap with the features in the
sparrow::GeneSetDb class. Although we can’t quite replace the internals of
{sparrow} to use the BiocSet just yet, users are encouraged to provide
collections of gene sets in the form of a BiocSet everywhere {sparrow}
functions require gene set collections, like seas() and
scoreSingleSamples(). You can also convert a sparrow::GeneSetDb() to a
BiocSet via a simple call: as(gdb, "BiocSet").
The remainder of this section provides a quick overview of the GeneSetDb
class.
The GeneSetDb object uses the data.table package internally for fast lookup.
Internally the collection of gene set information is minimally stored as a
three-column data.table in “long form”, which has the following columns:
- collection
- name
- feature_id
More columns can be added to the internal data.table (a “symbol” column,
for instance), but those are the only three you need.
To see what we are talking about, exactly, you can call the as.data.frame
function on a GeneSetDb object:
## collection name feature_id symbol
## 1 C2 ABBUD_LIF_SIGNALING_1_DN 100133941 CD24
## 2 C2 ABBUD_LIF_SIGNALING_1_DN 10753 CAPN9
## 3 C2 ABBUD_LIF_SIGNALING_1_DN 146556 C16orf89
## 4 C2 ABBUD_LIF_SIGNALING_1_DN 1644 DDC
## 5 C2 ABBUD_LIF_SIGNALING_1_DN 1943 EFNA2
## 201 C2 ABE_VEGFA_TARGETS_2HR 3949 LDLR
## 202 C2 ABE_VEGFA_TARGETS_2HR 4171 MCM2
## 203 C2 ABE_VEGFA_TARGETS_2HR 5055 SERPINB2
## 204 C2 ABE_VEGFA_TARGETS_2HR 5133 PDCD1
## 205 C2 ABE_VEGFA_TARGETS_2HR 5493 PPLThe (collection,name) tuple is the primary key of a gene set. The feature_id
column stores gene identifiers. For the time being, it will be most natural
for these IDs to simply be ensembl gene identifiers (or entrez ids) as many of
the annotation databases use these identifiers, as well. In reality, you will
want the values in the feature_id columns to match with the feature id’s
you have in your data container (ie. the rownames() of a
SummarizedExperiment, for instance).
5.1 Building a GeneSetDb
The sparrow package provides convenience functions to fetch genesets from many sources and convert them into a GeneSetDb object. The two most useful sources may be:
- MSigDB via
getMSigGeneSetDb(...). Although the coresparrowpackage provides the getter function for these genesets, the main data retrieval functionality is provided through the msigdbr package. - PANTHER (pathways and GOSLIM) via
getPantherGeneSetDb() - KEGG via
getKeggGeneSetDb(...)
We also provide similarly named methos to retrieve these gene set collections
as a BiocSet, just substitute "Collection" for "GeneSetDb", ie.
getMsigCollection(...), getPantherCollection(...), and
getKeggCollection(...).
You can create a custom GeneSetDb via the GeneSetDb() constructor, which
accepts the following types of inputs.
- A
BiocSet - A
GeneSetCollection - A data.frame of geneset membership. This requires
collection,name, andfeature_idcolumns. Reference the output ofas.data.frame(gdb)shown above. - A named list of gene identifier vectors that represent genesets for a single collection
- A named list of (2)-like lists. The top level names are the names of the different collections, and each sublist represents the genesets in that collection.
Two GeneSetDb objects can be combined using the cobine() function. For now
it is your responsibility to ensure that the two GeneSetDb objects are
“reasonably conformable”, ie. they use the same types of gene identifiers, and
are referencing the same species, etc.
msigdb <- getMSigGeneSetDb('H', 'human')
goslimdb <- getPantherGeneSetDb('goslim', 'human')
gdb.uber <- combine(msigdb, goslimdb)See the help and examples in ?GeneSetDb for more information.
For some reason the PANTHER.db package needs to be installed in a
user-writable package location for this to work properly. If you see an error
that speaks to using “rsqlite to write to a readonly database”, you will have to
re-install PANTHER.db in a user-writable directory using
BiocManager::install("PANTHER.db")
5.2 Subsetting a GeneSetDb
The subsetting functionality for a GeneSetDb is a bit clunky. We assume
you want to subset a GeneSetDb to include a subset of, well, gene sets.
One way you can do that is to provide a logical vector that is as long as there are gene sets in the GeneSetDb as an index.
For instance, if we want to include only the genesets in CP:PID,
you can do that. This subcatory information is stored in the "subcategory"
column from geneSets(gdb)
## collection name active N n subcategory gs_id
## 1 C2 PID_A6B1_A6B4_INTEGRIN_PATHWAY FALSE 46 NA CP:PID M239
## 2 C2 PID_AJDISS_2PATHWAY FALSE 48 NA CP:PID M142
## 3 C2 PID_ALK1_PATHWAY FALSE 26 NA CP:PID M185
## 4 C2 PID_ALK2_PATHWAY FALSE 11 NA CP:PID M203
## 5 C2 PID_ALPHA_SYNUCLEIN_PATHWAY FALSE 32 NA CP:PID M275
## 6 C2 PID_AMB2_NEUTROPHILS_PATHWAY FALSE 41 NA CP:PID M159You can also subset a GeneSetDb to only include gene sets that contain
certain features:
## [1] 6230## [1] 1205.3 Active vs Inactive Gene Sets
A GeneSetDb is used to hold “the universe” of genes that belong to different
gene sets across different collections. Depending on the assay performed to
measure these genes, the set of genes you observe in your study will likely
be a subset of the genes in the GeneSetDb. As such, prior to using a
GeneSetDb for GSEA, it must be “conformed” to a target object that will be
used for the input to the GESA (either a matrix of expression, or a pre ranked
vector of statistics). This step will index into the target expression object
and identify which rows of the object correspond to which genes in the
GeneSetDb.
“Conformation” happens automatically within the seas() call, but we call it
explicitly below to outline its functionality. The command below conforms
the GeneSetDb to our target “voomed” EList, and deactivates gene sets
(i.e. removes them from downstream GSEA) that have less than 10 or more than 100
genes that were found in vm:
## collection name active N n subcategory gs_id
## 1 C2 ABBUD_LIF_SIGNALING_1_DN TRUE 28 25 CGP M1423
## 2 C2 ABBUD_LIF_SIGNALING_1_UP TRUE 43 37 CGP M1458
## 3 C2 ABBUD_LIF_SIGNALING_2_DN FALSE 7 5 CGP M1481
## 4 C2 ABBUD_LIF_SIGNALING_2_UP TRUE 13 12 CGP M1439
## 5 C2 ABDELMOHSEN_ELAVL4_TARGETS TRUE 16 15 CGP M2509
## 6 C2 ABDULRAHMAN_KIDNEY_CANCER_VHL_DN TRUE 13 12 CGP M2096We can see that, only 23 of the 26 genes in the
(C2,ABBUD_LIF_SIGNALING_1_DN) were found in the rows of vm, and the (C2,ABBUD_LIF_SIGNALING_2_DN) was “deactivated.” Deactivated
(active == FALSE) gene sets will be ignored during downstream analyses. This
gene set was deactivated because it only has five “conformed” genes, but the
minimum geneset size we wanted to consider (min.gs.size) was set to ten in
our call to conform.
5.4 Accessing members of a gene set
The geneSet and featureIds functions allow the user to identify the genes
found in a gene set. Both of these functions take an active.only argument,
which is TRUE by default. This specifies that only the genes that have been
successfully conformed to a gene set should be the ones that are returned.
For instance, we can identify which genes belong to the
(C2,ABBUD_LIF_SIGNALING_1_DN), and which three were not found in vm like so:
missed <- setdiff(
featureIds(gdbc, 'C2', 'ABBUD_LIF_SIGNALING_1_DN', active.only=FALSE),
featureIds(gdbc, 'C2', 'ABBUD_LIF_SIGNALING_1_DN', active.only=TRUE))
missed## [1] "1644" "1943" "3170"or we can use the geneSet function to return a data.frame of these results:
gdbc %>%
geneSet('C2', 'ABBUD_LIF_SIGNALING_1_DN', active.only = FALSE) %>%
subset(feature_id %in% missed)## collection name active N n feature_id symbol
## 4 C2 ABBUD_LIF_SIGNALING_1_DN TRUE 28 25 1644 DDC
## 5 C2 ABBUD_LIF_SIGNALING_1_DN TRUE 28 25 1943 EFNA2
## 12 C2 ABBUD_LIF_SIGNALING_1_DN TRUE 28 25 3170 FOXA25.5 Mapping of gene set featureIds to target expression containers
It may be that the IDs used in a gene set collection are different from the
ones used as the rownames of your expression container. For instance, the IDs
used for a given gene set collection in the GeneSetDb might be
Ensembl gene identifiers, but the rownames of the expression object might
be Entrez ID. This is where the mapping parameter becomes useful.
The GeneSetDb class has a concept of an internal featureIdMap to accommodate
these scenarios, which would allow for a non-destructive mapping of the original
IDs to a new “ID space” (entrez to ensembl, for instance).
This functionality is not ready for this release, but it’s just a note to keep
the user aware of some future development of the package. For the
time being, the user is required to manually map the feautreIds in their
expression matrix to be concordant with the ones found in the GeneSetDb.
In the meantime, a renameRows convenience function is provided here
to easily rename the rows of our expression container to different values.
For instance, to rename this is how you might rename the rows of your assay
container to use symbols:
vm <- exampleExpressionSet()
vms <- renameRows(vm, "symbol")
head(cbind(rownames(vm), rownames(vms)))## [,1] [,2]
## [1,] "1" "A1BG"
## [2,] "100" "ADA"
## [3,] "1000" "CDH2"
## [4,] "10000" "AKT3"
## [5,] "100009676" "LOC100009676"
## [6,] "10001" "MED6"We grabbed the symbol column from vm$genes and “smartly” renamed the rows
of vm with the values there. Refer to the ?renameRows man page for more
details. This, of course, still requires you to manually fetch and map
identifiers, but still …
6 Customizing Analyses
The internal differential expression analysis as well the gene set enrichment
analyses can be customized by passing parameters through the ... in the
seas() function.
6.1 Custom Differential Expression
The internal differential expression pipeline, exported via the
calculateIndividualLogFC function allows the end user to configure an
“arbitrarily complex” differential expression analysis using either edgeR’s
quasilikelihood framework (if the input is a DGEList) or a direct limma
analysis (with a pre-voomed EList, expression matrix, or whatever).
User’s should refer to the ?calculateIndividualLogFC help page to see
which parameters are exposed for a differential expression analysis and
configure them accordingly. When calling seas() use these same parameters
in the call and they will be provided to calculateIndividualLogFC.
For instance, if you wanted to use limma’s “treat” functionality to specify a minimal log fold change threshold for statistical significance, you would do so as follows:
mg <- seas(vm, gdb, "goseq", design = vm$design, cm[, 'BvH'],
treat.lfc=log2(1.5),
## feature length vector required for goseq
feature.bias=setNames(vm$genes$size, rownames(vm)))Using the internal treat functionality would really only affect enrichment
tests that first threshold the genes in your experiment as “significant” or not,
like goseq and not tests like camera.
6.2 Custom GSEA
The GSEA methods that are wrapped by seas() all take the same parameters
that are defined by their implementation. Simply pass these parameters down
via the ... in the seas() call.
For instance, you can read ?camera to find that the camera method accepts an
inter.gene.cor parameter, and ?roast will tell you that you can specify
the number of rotations used via the nrot parameter.
7 Reproducibility
Session Information
## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] grid stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] data.table_1.16.2 edgeR_4.4.0 limma_3.62.0
## [4] circlize_0.4.16 ComplexHeatmap_2.22.0 ggplot2_3.5.1
## [7] dplyr_1.1.4 magrittr_2.0.3 sparrow_1.12.0
## [10] BiocStyle_2.34.0
##
## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.9
## [3] shape_1.4.6.1 magick_2.8.5
## [5] farver_2.1.2 rmarkdown_2.28
## [7] BiocSet_1.20.0 GlobalOptions_0.1.2
## [9] BiocIO_1.16.0 zlibbioc_1.52.0
## [11] vctrs_0.6.5 Cairo_1.6-2
## [13] memoise_2.0.1 DelayedMatrixStats_1.28.0
## [15] tinytex_0.53 htmltools_0.5.8.1
## [17] S4Arrays_1.6.0 Rhdf5lib_1.28.0
## [19] SparseArray_1.6.0 rhdf5_2.50.0
## [21] sass_0.4.9 bslib_0.8.0
## [23] htmlwidgets_1.6.4 plyr_1.8.9
## [25] plotly_4.10.4 cachem_1.1.0
## [27] lifecycle_1.0.4 iterators_1.0.14
## [29] pkgconfig_2.0.3 rsvd_1.0.5
## [31] Matrix_1.7-1 R6_2.5.1
## [33] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [35] MatrixGenerics_1.18.0 clue_0.3-65
## [37] digest_0.6.37 colorspace_2.1-1
## [39] AnnotationDbi_1.68.0 S4Vectors_0.44.0
## [41] irlba_2.3.5.1 GenomicRanges_1.58.0
## [43] RSQLite_2.3.7 beachmat_2.22.0
## [45] labeling_0.4.3 fansi_1.0.6
## [47] httr_1.4.7 abind_1.4-8
## [49] compiler_4.4.1 bit64_4.5.2
## [51] withr_3.0.2 doParallel_1.0.17
## [53] backports_1.5.0 BiocParallel_1.40.0
## [55] viridis_0.6.5 DBI_1.2.3
## [57] highr_0.11 BiasedUrn_2.0.12
## [59] HDF5Array_1.34.0 DelayedArray_0.32.0
## [61] rjson_0.2.23 tools_4.4.1
## [63] msigdbr_7.5.1 glue_1.8.0
## [65] rhdf5filters_1.18.0 checkmate_2.3.2
## [67] cluster_2.1.6 generics_0.1.3
## [69] gtable_0.3.6 tidyr_1.3.1
## [71] BiocSingular_1.22.0 ScaledMatrix_1.14.0
## [73] utf8_1.2.4 XVector_0.46.0
## [75] BiocGenerics_0.52.0 foreach_1.5.2
## [77] pillar_1.9.0 babelgene_22.9
## [79] GSVA_2.0.0 lattice_0.22-6
## [81] bit_4.5.0 annotate_1.84.0
## [83] tidyselect_1.2.1 SingleCellExperiment_1.28.0
## [85] locfit_1.5-9.10 Biostrings_2.74.0
## [87] knitr_1.48 gridExtra_2.3
## [89] bookdown_0.41 IRanges_2.40.0
## [91] SummarizedExperiment_1.36.0 stats4_4.4.1
## [93] xfun_0.48 Biobase_2.66.0
## [95] statmod_1.5.0 matrixStats_1.4.1
## [97] UCSC.utils_1.2.0 lazyeval_0.2.2
## [99] yaml_2.3.10 evaluate_1.0.1
## [101] codetools_0.2-20 tibble_3.2.1
## [103] BiocManager_1.30.25 graph_1.84.0
## [105] cli_3.6.3 ontologyIndex_2.12
## [107] xtable_1.8-4 munsell_0.5.1
## [109] jquerylib_0.1.4 Rcpp_1.0.13
## [111] GenomeInfoDb_1.42.0 png_0.1-8
## [113] XML_3.99-0.17 parallel_4.4.1
## [115] blob_1.2.4 sparseMatrixStats_1.18.0
## [117] SpatialExperiment_1.16.0 viridisLite_0.4.2
## [119] GSEABase_1.68.0 scales_1.3.0
## [121] purrr_1.0.2 crayon_1.5.3
## [123] GetoptLong_1.0.5 rlang_1.1.4
## [125] KEGGREST_1.46.0