Using escape to perform gene set enrichment analyses on single-cell RNA-seq data
Compiled: April 30, 2024
Package
escape 2.0.0
1 Loading Processed Single-Cell Data
For the demonstration of escape, we will use the example “pbmc_small” data from Seurat and also generate a SingleCellExperiment object from it.
suppressPackageStartupMessages(library(escape))
suppressPackageStartupMessages(library(SingleCellExperiment))
suppressPackageStartupMessages(library(scran))
suppressPackageStartupMessages(library(Seurat))
suppressPackageStartupMessages(library(SeuratObject))
suppressPackageStartupMessages(library(RColorBrewer))
suppressPackageStartupMessages(library(ggplot2))
pbmc_small <- get("pbmc_small")
sce.pbmc <- as.SingleCellExperiment(pbmc_small, assay = "RNA")2 Getting Gene Sets
2.1 Option 1: Molecular Signature Database
The first step in the process of performing gene set enrichment analysis is identifying the gene sets we would like to use. The function getGeneSets() allows users to isolate a whole or multiple libraries from a list of GSEABase GeneSetCollection objects.
We can do this for gene set collections from the built-in Molecular Signature Database by setting the parameter library equal to library/libraries of interest. For multiple libraries, just set library = c(“Library1”, “Library2”, etc).
Additional parameters include:
- subcategory Can be used to select subcategories in the larger database.
- gene.sets Can select individual pathways/gene sets to be isolated.
If the sequencing of the single-cell data is performed on a species other than “Homo sapiens”, make sure to use the species parameter in getGeneSets() in order to get the correct gene nomenclature.
GS.hallmark <- getGeneSets(library = "H")2.2 Option 2: Built-In gene sets
data("escape.gene.sets", package="escape")
gene.sets <- escape.gene.sets2.3 Option 3: Define personal gene sets
gene.sets <- list(Bcells = c("MS4A1","CD79B","CD79A","IGH1","IGH2")
Myeloid = c("SPI1","FCER1G","CSF1R"),
Tcells = c("CD3E", "CD3D", "CD3G", "CD7","CD8A"))3 Performing Enrichment Calculation
Several popular methods exist for Gene Set Enrichment Analysis (GSEA). These methods can vary in the underlying assumptions. escape incorporates several methods that are particularly advantageous for single-cell RNA values:
3.0.1 ssGSEA
This method calculates the enrichment score using a rank-normalized approach and generating an empirical cumulative distribution function for each individual cell. The enrichment score is defined for a gene set (G) using the number of genes in the gene set (NG) and total number of genes (N).
\[ ES(G,S) \sum_{i = 1}^{n} [P_W^G(G,S,i)-P_{NG}(G,S,i)] \] Please see the following citation for more information.
3.0.2 GSVA
GSVA varies slightly by estimating a Poisson-based kernel cumulative density function. But like ssGSEA, the ultimate enrichment score reported is based on the maximum value of the random walk statistic. GSVA appears to have better overall consistency and runs faster than ssGSEA.
\[ ES_{jk}^{max} = V_{jk} [max_{l=1,...,p}|v_{jk}(l)] \] Please see the following citation for more information.
3.0.3 AUCell
In contrast to ssGSEA and GSVA, AUCell takes the gene rankings for each cell and step-wise plots the position of each gene in the gene set along the y-axis. The output score is the area under the curve for this approach.
Please see the following citation for more information.
3.0.4 UCell
UCell calculates a Mann-Whitney U statistic based on the gene rank list. Importantly, UCell has a cut-off for ranked genes (\[r_{max}\]) at 1500 - this is per design as drop-out in single-cell can alter enrichment results. This also substantially speeds the calculations up.
The enrichment score output is then calculated using the complement of the U statistic scaled by the gene set size and cut-off.
\[ U_j^` = 1-\frac{U_j}{n \bullet r_{max}} \]
Please see the following citation for more information.
3.1 escape.matrix
escape has 2 major functions - the first being escape.matrix(), which serves as the backbone of enrichment calculations. Using count-level data supplied from a single-cell object or matrix, escape.matrix() will produce an enrichment score for the individual cells with the gene sets selected and output the values as a matrix.
method
- AUCell
- GSVA
- ssGSEA
- UCell
groups
- The number of cells to calculate at once.
min.size
- The minimum size of detectable genes in a gene set. Gene sets less than the min.size will be removed before the calculation.
normalize
- Use the number of genes from the gene sets in each cell to normalize the enrichment scores. The default value is FALSE.
make.positive
- During normalization, whether to shift the enrichment values to a positive range (TRUE) or not (FALSE). The default value is FALSE.
Cautionary note: make.positive was added to allow for differential analysis downstream of enrichment as some methods may produce negative values. It preserves log-fold change, but ultimately modifies the enrichment values and should be used with caution.
enrichment.scores <- escape.matrix(pbmc_small,
gene.sets = GS.hallmark,
groups = 1000,
min.size = 5)## [1] "Using sets of 1000 cells. Running 1 times."
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."ggplot(data = as.data.frame(enrichment.scores),
mapping = aes(enrichment.scores[,1], enrichment.scores[,2])) +
geom_point() +
theme_classic() +
theme(axis.title = element_blank())
Multi-core support is for all methods is available through BiocParallel. To add more cores, use the argument BPPARAM to escape.matrix(). Here we will use the SnowParam() for it’s support across platforms and explicitly call 2 workers (or cores).
enrichment.scores <- escape.matrix(pbmc_small,
gene.sets = GS.hallmark,
groups = 1000,
min.size = 5,
BPPARAM = SnowParam(workers = 2))3.2 runEscape
Alternatively, we can use runEscape() to calculate the enrichment score and directly attach the output to a single-cell object. The additional parameter for ``runEscape is new.assay.name, in order to save the enrichment scores as a custom assay in the single-cell object.
pbmc_small <- runEscape(pbmc_small,
method = "ssGSEA",
gene.sets = GS.hallmark,
groups = 1000,
min.size = 5,
new.assay.name = "escape.ssGSEA")## [1] "Using sets of 1000 cells. Running 1 times."
## [1] "Calculating ranks..."
## [1] "Calculating absolute values from ranks..."sce.pbmc <- runEscape(sce.pbmc,
method = "UCell",
gene.sets = GS.hallmark,
groups = 1000,
min.size = 5,
new.assay.name = "escape.UCell")## [1] "Using sets of 1000 cells. Running 1 times."We can quickly examine the attached enrichment scores using the visualization/workflow we prefer - here we will use just FeaturePlot() from the Seurat R package.
#Define color palette
colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE)
FeaturePlot(pbmc_small, "HALLMARK-APOPTOSIS") +
scale_color_gradientn(colors = colorblind_vector) +
theme(plot.title = element_blank())
3.3 performNormalization
Although we glossed over the normalization that can be used in escape.matrix() and runEscape(), it is worth mentioning here as normalization can affect all downstream analyses.
There can be inherent bias in enrichment values due to drop out in single-cell expression data. Cells with larger numbers of features and counts will likely have higher enrichment values. performNormalization() will normalize the enrichment values by calculating the number of genes expressed in each gene set and cell. This is similar to the normalization in classic GSEA and it will be stored in a new assay.
pbmc_small <- performNormalization(pbmc_small,
assay = "escape.ssGSEA",
gene.sets = GS.hallmark)## [1] "Calculating features per cell..."
## [1] "Normalizing enrichment scores per cell..."An alternative for scaling by expressed gene sets would be to use a scaling factor previously calculated during normal single-cell data processing and quality control. This can be done using the scale.factor argument and providing a vector.
pbmc_small <- performNormalization(pbmc_small,
assay = "escape.ssGSEA",
gene.sets = GS.hallmark,
scale.factor = pbmc_small$nFeature_RNA)## [1] "Normalizing enrichment scores per cell..."performNormalization() has an additional parameter make.positive. Across the individual gene sets, if negative normalized enrichment scores are seen, the minimum value is added to all values. For example if the normalized enrichment scores (after the above accounting for drop out) ranges from -50 to 50, make.positive will adjust the range to 0 to 100 (by adding 50). This allows for compatible log2-fold change downstream, but can alter the enrichment score interpretation.
4 Visualizations
There are a number of ways to look at the enrichment values downstream of runEscape() with the myriad plotting and visualizations functions/packages for single-cell data. escape include several additional plotting functions to assist in the analysis.
4.1 heatmapEnrichment
We can examine the enrichment values across our gene sets by using heatmapEnrichment(). This visualization will return the mean of the group.by variable. As a default - all visualizations of single-cell objects will use the cluster assignment or active identity as a default for visualizations.
heatmapEnrichment(pbmc_small,
group.by = "ident",
gene.set.use = "all",
assay = "escape.ssGSEA")
Most of the visualizations in escape have a defined set of parameters.
group.by
- The grouping variable for the comparison.
facet.by
- Using a variable to facet the graph into separate visualizations.
scale
- TRUE - z-transform the enrichment values.
- FALSE - leave raw values (DEFAULT).
In addition, heatmapEnrichment() allows for the reclustering of rows and columns using Euclidean distance of the enrichment scores and the Ward2 methods for clustering using cluster.rows and cluster.columns.
heatmapEnrichment(sce.pbmc,
group.by = "ident",
assay = "escape.UCell",
scale = TRUE,
cluster.rows = TRUE,
cluster.columns = TRUE)
Each visualization has an additional argument called **palette that supplies the coloring scheme to be used - available color palettes can be viewed with hcl.pals().
hcl.pals()## [1] "Pastel 1" "Dark 2" "Dark 3" "Set 2"
## [5] "Set 3" "Warm" "Cold" "Harmonic"
## [9] "Dynamic" "Grays" "Light Grays" "Blues 2"
## [13] "Blues 3" "Purples 2" "Purples 3" "Reds 2"
## [17] "Reds 3" "Greens 2" "Greens 3" "Oslo"
## [21] "Purple-Blue" "Red-Purple" "Red-Blue" "Purple-Orange"
## [25] "Purple-Yellow" "Blue-Yellow" "Green-Yellow" "Red-Yellow"
## [29] "Heat" "Heat 2" "Terrain" "Terrain 2"
## [33] "Viridis" "Plasma" "Inferno" "Rocket"
## [37] "Mako" "Dark Mint" "Mint" "BluGrn"
## [41] "Teal" "TealGrn" "Emrld" "BluYl"
## [45] "ag_GrnYl" "Peach" "PinkYl" "Burg"
## [49] "BurgYl" "RedOr" "OrYel" "Purp"
## [53] "PurpOr" "Sunset" "Magenta" "SunsetDark"
## [57] "ag_Sunset" "BrwnYl" "YlOrRd" "YlOrBr"
## [61] "OrRd" "Oranges" "YlGn" "YlGnBu"
## [65] "Reds" "RdPu" "PuRd" "Purples"
## [69] "PuBuGn" "PuBu" "Greens" "BuGn"
## [73] "GnBu" "BuPu" "Blues" "Lajolla"
## [77] "Turku" "Hawaii" "Batlow" "Blue-Red"
## [81] "Blue-Red 2" "Blue-Red 3" "Red-Green" "Purple-Green"
## [85] "Purple-Brown" "Green-Brown" "Blue-Yellow 2" "Blue-Yellow 3"
## [89] "Green-Orange" "Cyan-Magenta" "Tropic" "Broc"
## [93] "Cork" "Vik" "Berlin" "Lisbon"
## [97] "Tofino" "ArmyRose" "Earth" "Fall"
## [101] "Geyser" "TealRose" "Temps" "PuOr"
## [105] "RdBu" "RdGy" "PiYG" "PRGn"
## [109] "BrBG" "RdYlBu" "RdYlGn" "Spectral"
## [113] "Zissou 1" "Cividis" "Roma"heatmapEnrichment(pbmc_small,
assay = "escape.ssGSEA",
palette = "Spectral") 
Alternatively, we can add an additional layer to the ggplot object that is returned by the visualizations using something like scale_fill_gradientn() for continuous values or scale_fill_manual() for the categorical variables.
heatmapEnrichment(sce.pbmc,
group.by = "ident",
assay = "escape.UCell") +
scale_fill_gradientn(colors = rev(brewer.pal(11, "RdYlBu"))) 
4.2 geyserEnrichment
We can also focus on individual gene sets - one approach is to use geyserEnrichment(). Here individual cells are plotted along the Y-axis with graphical summary where the central dot refers to the median enrichment value and the thicker/thinner lines demonstrate the interval summaries referring to the 66% and 95%.
geyserEnrichment(pbmc_small,
assay = "escape.ssGSEA",
gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE")
To show the additional parameters that appear in visualizations of individual enrichment gene sets - we can reorder the groups by the mean of the gene set using order.by = “mean”.
geyserEnrichment(pbmc_small,
assay = "escape.ssGSEA",
gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
order.by = "mean")
What if we had 2 separate samples or groups within the data? Another parameter we can use is facet.by to allow for direct visualization of an additional variable.
geyserEnrichment(pbmc_small,
assay = "escape.ssGSEA",
gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
facet.by = "groups")
Lastly, we can select the way the color is applied to the plot using the color.by parameter. Here we can set it to the gene set of interest “HALLMARK-INTERFERON-GAMMA-RESPONSE”.
geyserEnrichment(pbmc_small,
assay = "escape.ssGSEA",
gene.set = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
color.by = "HALLMARK-INTERFERON-GAMMA-RESPONSE")
4.3 ridgeEnrichment
Similar to the geyserEnrichment() the ridgeEnrichment() can display the distribution of enrichment values across the selected gene set. The central line is at the median value for the respective grouping.
ridgeEnrichment(sce.pbmc,
assay = "escape.UCell",
gene.set = "HALLMARK-IL2-STAT5-SIGNALING")
We can get the relative position of individual cells along the x-axis using the add.rug parameter.
ridgeEnrichment(sce.pbmc,
assay = "escape.UCell",
gene.set = "HALLMARK-IL2-STAT5-SIGNALING",
add.rug = TRUE,
scale = TRUE)
4.4 splitEnrichment
Another distribution visualization is a violin plot, which we separate and directly compare using a binary classification. Like ridgeEnrichment(), this allows for greater use of categorical variables. For splitEnrichment(), the output will be two halves of a violin plot based on the split.by parameter with a central boxplot with the relative distribution across all samples.
splitEnrichment(pbmc_small,
assay = "escape.ssGSEA",
gene.set = "HALLMARK-IL2-STAT5-SIGNALING",
split.by = "groups")
4.5 densityEnrichment
densityEnrichment() is a method to visualize the mean rank position of the gene set features along the total feature space by group. This is similar to traditional GSEA analysis, but is not calculating the walk-based enrichment score.
gene.set.use
- The selected gene set to visualize
gene.sets
- The gene set library from either of the 3 options in the first section of the vignette.
densityEnrichment(pbmc_small,
gene.set.use = "HALLMARK-IL6-JAK-STAT3-SIGNALING",
gene.sets = GS.hallmark)
## scatterEnrichment
It may be advantageous to look at the distribution of multiple gene sets - here we can use scatterEnrichment() for a 2 gene set comparison. The color values are based on the density of points determined by the number of neighbors, similar to the Nebulosa R package. We just need to define which gene set to plot on the x.axis and which to plot on the y.axis.
scatterEnrichment(pbmc_small,
assay = "escape.ssGSEA",
x.axis = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
y.axis = "HALLMARK-IL6-JAK-STAT3-SIGNALING")
The scatter plot can also be converted into a hexbin, another method for summarizing the individual cell distributions along the x and y axis, by setting style = “hex”.
scatterEnrichment(sce.pbmc,
assay = "escape.UCell",
x.axis = "HALLMARK-INTERFERON-GAMMA-RESPONSE",
y.axis = "HALLMARK-IL6-JAK-STAT3-SIGNALING",
style = "hex")
5 Statistical Analysis
5.1 Principal Component Analysis (PCA)
escape has its own PCA function performPCA() which will work on a single-cell object or a matrix of enrichment values. This is specifically useful for downstream visualizations as it stores the eigenvalues and rotations. If we want to look at the relative contribution to overall variance of each component or a Biplot-like overlay of the individual features, use performPCA().
Alternatively, other PCA-based functions like Seurat’s RunPCA() or scater’s ``runPCA() can be used. These functions are likely faster and would be ideal if we have a larger number of cells and/or gene sets.
pbmc_small <- performPCA(pbmc_small,
assay = "escape.ssGSEA",
n.dim = 1:10)escape has a built in method for plotting PCA pcaEnrichment() that functions similarly to the scatterEnrichment() function where x.axis and y.axis are the components to plot.
pcaEnrichment(pbmc_small,
dimRed = "escape.PCA",
x.axis = "PC1",
y.axis = "PC2")
pcaEnrichment() can plot additional information on the principal component analysis.
add.percent.contribution will add the relative percent contribution of the x and y.axis to total variability observed in the PCA.
display.factors will overlay the magnitude and direction that the features/gene sets contribute to the selected components. The number of gene sets is determined by number.of.factors. This can assist in understanding the underlying differences in enrichment across different cells.
pcaEnrichment(pbmc_small,
dimRed = "escape.PCA",
x.axis = "PC1",
y.axis = "PC2",
add.percent.contribution = TRUE,
display.factors = TRUE,
number.of.factors = 10)
5.2 Differential Enrichment
Differential enrichment analysis can be performed similar to differential gene expression analysis. For the purposes of finding the differential enrichment values, we can first normalize the enrichment values for the ssGSEA calculations. Notice here, we are using make.positive = TRUE in order to adjust any negative values. This is a particular issue when it comes to ssGSEA and GSVA enrichment scores.
pbmc_small <- performNormalization(pbmc_small,
assay = "escape.ssGSEA",
gene.sets = GS.hallmark,
make.positive = TRUE)## [1] "Calculating features per cell..."
## [1] "Normalizing enrichment scores per cell..."all.markers <- FindAllMarkers(pbmc_small,
assay = "escape.ssGSEA_normalized",
min.pct = 0,
logfc.threshold = 0)
head(all.markers)## p_val avg_log2FC pct.1 pct.2
## HALLMARK-INTERFERON-GAMMA-RESPONSE 4.061261e-06 -1.5124819 0.972 1.000
## HALLMARK-ALLOGRAFT-REJECTION 6.519652e-04 2.3948709 1.000 0.977
## HALLMARK-IL2-STAT5-SIGNALING 5.934221e-03 3.2347238 1.000 0.977
## HALLMARK-ALLOGRAFT-REJECTION.1 2.932830e-07 -4.4124278 0.960 1.000
## HALLMARK-APOPTOSIS 2.932830e-07 2.0643196 1.000 0.982
## HALLMARK-P53-PATHWAY 9.505692e-05 0.8890429 1.000 0.982
## p_val_adj cluster
## HALLMARK-INTERFERON-GAMMA-RESPONSE 6.904145e-05 0
## HALLMARK-ALLOGRAFT-REJECTION 1.108341e-02 0
## HALLMARK-IL2-STAT5-SIGNALING 1.008818e-01 0
## HALLMARK-ALLOGRAFT-REJECTION.1 4.985811e-06 1
## HALLMARK-APOPTOSIS 4.985811e-06 1
## HALLMARK-P53-PATHWAY 1.615968e-03 1
## gene
## HALLMARK-INTERFERON-GAMMA-RESPONSE HALLMARK-INTERFERON-GAMMA-RESPONSE
## HALLMARK-ALLOGRAFT-REJECTION HALLMARK-ALLOGRAFT-REJECTION
## HALLMARK-IL2-STAT5-SIGNALING HALLMARK-IL2-STAT5-SIGNALING
## HALLMARK-ALLOGRAFT-REJECTION.1 HALLMARK-ALLOGRAFT-REJECTION
## HALLMARK-APOPTOSIS HALLMARK-APOPTOSIS
## HALLMARK-P53-PATHWAY HALLMARK-P53-PATHWAY6 Conclusions
If you have any questions, comments or suggestions, feel free to visit the github repository or email me.
sessionInfo()## R version 4.4.0 beta (2024-04-15 r86425)
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## [3] Seurat_5.0.3 SeuratObject_5.0.1
## [5] sp_2.1-4 scran_1.32.0
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## [129] munsell_0.5.1 Rcpp_1.0.12
## [131] babelgene_22.9 reticulate_1.36.1
## [133] stringi_1.8.3 zlibbioc_1.50.0
## [135] MASS_7.3-60.2 plyr_1.8.9
## [137] parallel_4.4.0 listenv_0.9.1
## [139] ggrepel_0.9.5 deldir_2.0-4
## [141] Biostrings_2.72.0 splines_4.4.0
## [143] tensor_1.5 locfit_1.5-9.9
## [145] igraph_2.0.3 spatstat.geom_3.2-9
## [147] RcppHNSW_0.6.0 reshape2_1.4.4
## [149] ScaledMatrix_1.12.0 XML_3.99-0.16.1
## [151] evaluate_0.23 BiocManager_1.30.22
## [153] httpuv_1.6.15 RANN_2.6.1
## [155] tidyr_1.3.1 purrr_1.0.2
## [157] polyclip_1.10-6 future_1.33.2
## [159] scattermore_1.2 rsvd_1.0.5
## [161] xtable_1.8-4 RSpectra_0.16-1
## [163] later_1.3.2 viridisLite_0.4.2
## [165] ggpointdensity_0.1.0 tibble_3.2.1
## [167] memoise_2.0.1 AnnotationDbi_1.66.0
## [169] cluster_2.1.6 globals_0.16.3
## [171] GSEABase_1.66.0