Differential state analysis with muscat
October 24, 2023
Abstract
Single-cell RNA sequencing (scRNA-seq) has quickly become an empowering technology to profile the transcriptomes of individual cells on a large scale. Many early analyses of differential expression have aimed at identifying differences between subpopulations, and thus are focused on finding subpopulation markers either in a single sample or across multiple samples. More generally, such methods can compare expression levels in multiple sets of cells, thus leading to cross-condition analyses.
However, given the emergence of replicated multi-condition scRNA-seq datasets, an area of increasing focus is making sample-level inferences, termed here as differential state (DS) analysis. For example, one could investigate the condition-specific responses of cell subpopulations measured from patients in each condition.
muscat: multi-sample multi-group scRNA-seq analysis tools (Crowell et al. 2020) provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data.
Package
muscat 1.16.0
Contents
For details on the concept and technicalities of DS analysis, and the methods presented here, consider having a look at our publication:
Crowell HL, Soneson C*, Germain P-L*, Calini D,
Collin L, Raposo C, Malhotra D, and Robinson MD:
muscat detects subpopulation-specific state transitions from
multi-sample multi-condition single-cell transcriptomics data.
Nature Communications 11, 6077 (2020).
DOI: 10.1038/s41467-020-19894-4
Load packages
library(dplyr)
library(ggplot2)
library(limma)
library(muscat)
library(purrr)1 Introduction
1.1 What is DS analysis?
A fundamental task in the analysis of single-cell RNA-sequencing (scRNA-seq) data is the identification of systematic transcriptional changes (Stegle, Teichmann, and Marioni 2015). Such analyses are a critical step in the understanding of molecular responses, and have applications in development, in perturbation studies or in disease.
Most of the current scRNA-seq differential expression (DE) analysis methods are designed to test one set of cells against another (or more generally, multiple sets together), and can be used to compare cell clusters (e.g., for identifying marker genes) or across conditions (cells from one condition versus another) (Soneson and Robinson 2018). In such statistical models, the cells are the experimental units and thus represent the population that inferences will extrapolate to.
Using established terminology, we refer to cell identity as the combination of cell type, a stable molecular signature, and cell state, a transient snapshot of a cell’s molecular events (Wagner, Regev, and Yosef 2016; Trapnell 2015). This classification is inherently arbitrary, but still provides a basis for biological interpretation and a framework for discovering interesting expression patterns from scRNA-seq datasets. For example, T cells could be defined as a single (albeit diverse) cell type or could be divided into discrete subtypes, if relevant information to categorize each cell at this level were available. In either case, the framework presented here would be able to focus on the cell type of interest and look for changes (in expression) across samples.
Given the emergence of multi-sample multi-group scRNA-seq datasets, the goal becomes making sample-level inferences (i.e., experimental units are samples). Thus, differential state (DS) analysis is defined as following a given cell type across a set of samples (e.g., individuals) and experimental conditions (e.g., treatments), in order to identify cell-type-specific responses, i.e., changes in cell state. DS analysis: i) should be able to detect diluted changes that only affect a single cell type, a subset of cell types or even a subset of a single subpopulation; and, ii) is intended to be orthogonal to clustering or cell type assignment.
1.2 Starting point
The starting point for a DS analysis is a (sparse) matrix of gene expression, either as counts or some kind of normalized data, where rows = genes and columns = cells. Each cell additionally has a cluster (subpopulation) label as well as a sample label; metadata should accompany the list of samples, such that they can be organized into comparable groups with sample-level replicates (e.g., via a design matrix).
The approach presented here is modular and thus subpopulation labels could originate from an earlier step in the analysis, such as clustering (Duò, Robinson, and Soneson 2018; Freytag et al. 2018), perhaps after integration (Butler et al. 2018; Stuart et al. 2019) or after labeling of clusters (Diaz-Mejia et al. 2019) or after cell-level type assignment (Zhang et al. 2019).
2 Getting started
2.1 Data description
For this vignette, we will use a SingleCellExperiment (SCE) containing 10x droplet-based scRNA-seq PBCM data from 8 Lupus patients obtained before and after 6h-treatment with IFN-\(\beta\) (Kang et al. 2018). The complete raw data, as well as gene and cell metadata is available through the NCBI GEO, accession number GSE96583.
2.2 Loading the data
The Kang et al. (2018) dataset has been made available through Bioconductor’s ExperimentHub and can be loaded into R as follows: We first initialize a Hub instance to search for and load available data with the ExperimentHub function, and store the complete list of records in the variable eh. Using query, we then retrieve any records that match our keyword(s) of interest, as well as their corresponding accession ID (EH1234).
library(ExperimentHub)
eh <- ExperimentHub()
query(eh, "Kang")## ExperimentHub with 3 records
## # snapshotDate(): 2023-10-24
## # $dataprovider: NCI_GDC, GEO
## # $species: Homo sapiens
## # $rdataclass: character, SingleCellExperiment, BSseq
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH1661"]]'
##
## title
## EH1661 | Whole Genome Bisulfit Sequencing Data for 47 samples
## EH1662 | Whole Genome Bisulfit Sequencing Data for 47 samples
## EH2259 | Kang18_8vs8Finally, we load the data of interest into R via [[ and the corresponding accession ID. The dataset contains >35,000 genes and ~29,000 cells:
(sce <- eh[["EH2259"]])## class: SingleCellExperiment
## dim: 35635 29065
## metadata(0):
## assays(1): counts
## rownames(35635): MIR1302-10 FAM138A ... MT-ND6 MT-CYB
## rowData names(2): ENSEMBL SYMBOL
## colnames(29065): AAACATACAATGCC-1 AAACATACATTTCC-1 ... TTTGCATGGTTTGG-1
## TTTGCATGTCTTAC-1
## colData names(5): ind stim cluster cell multiplets
## reducedDimNames(1): TSNE
## mainExpName: NULL
## altExpNames(0):2.3 Preprocessing
The scater package (McCarthy et al. 2017) provides a variety of tools for preprocessing and quality control of single-cell transcriptomic data. For completeness, we will apply some minimal filtering steps to
- remove undetected genes
- remove cells with very few or many detected genes
- remove very lowly expressed genes
- compute normalized expression values for visualization
For more thorough preprocessing, we refer to the Quality control with scater vignette.
# remove undetected genes
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
dim(sce)## [1] 18890 29065We use perCellQCMetrics to compute various per-cell quality control metrics, and proceed with filtering cells and genes as noted above:
# calculate per-cell quality control (QC) metrics
library(scater)
qc <- perCellQCMetrics(sce)
# remove cells with few or many detected genes
ol <- isOutlier(metric = qc$detected, nmads = 2, log = TRUE)
sce <- sce[, !ol]
dim(sce)## [1] 18890 26820# remove lowly expressed genes
sce <- sce[rowSums(counts(sce) > 1) >= 10, ]
dim(sce)## [1] 7118 26820Finally, we use logNormCounts to calculate log\(_2\)-transformed normalized expression values by dividing each count by its size factor, adding a pseudo-count of 1, and log-transforming1 Note that, in this workflow, expression values are used for visualization only, and that differential analyses are performed on pseudobulks (section 3.3) or the count data directly (section 3.4)..
# compute sum-factors & normalize
sce <- computeLibraryFactors(sce)
sce <- logNormCounts(sce)Alternatively, expression values could be obtained via vst (variance stabilizing transformation) from the sctransform package (Hafemeister and Satija 2019), which returns Pearson residuals from a regularized negative binomial regression model that can be interpreted as normalized expression values:
library(sctransform)
assays(sce)$vstresiduals <- vst(counts(sce), verbosity = FALSE)$yBy default, scater’s functions will try to access the assay data specified via argument exprs_values (default logcounts) for e.g. visualization and dimension reduction. When an alternative assay such as the vstresiduals above should be used, it is thus necessary to explicitly specify this, for example, via runUMAP(sce, exprs_values = "vstresiduals") to compute UMAP cell embeddings on the assay data compute above.
2.4 Data preparation
muscat expects a certain format of the input SCE. Specifically, the following cell metadata (colData) columns have to be provided:
"sample_id": unique sample identifiers (e.g., PeterPan_ref1, Nautilus_trt3, …)"cluster_id": subpopulation (cluster) assignments (e.g., T cells, monocytes, …)"group_id": experimental group/condition (e.g., control/treatment, healthy/diseased, …)
sce$id <- paste0(sce$stim, sce$ind)
(sce <- prepSCE(sce,
kid = "cell", # subpopulation assignments
gid = "stim", # group IDs (ctrl/stim)
sid = "id", # sample IDs (ctrl/stim.1234)
drop = TRUE)) # drop all other colData columns## class: SingleCellExperiment
## dim: 7118 26820
## metadata(1): experiment_info
## assays(2): counts logcounts
## rownames(7118): NOC2L HES4 ... S100B PRMT2
## rowData names(2): ENSEMBL SYMBOL
## colnames(26820): AAACATACAATGCC-1 AAACATACATTTCC-1 ... TTTGCATGGTTTGG-1
## TTTGCATGTCTTAC-1
## colData names(3): cluster_id sample_id group_id
## reducedDimNames(1): TSNE
## mainExpName: NULL
## altExpNames(0):For consistency and easy accession throughout this vignette, we will store cluster and sample IDs, as well as the number of clusters and samples into the following simple variables:
nk <- length(kids <- levels(sce$cluster_id))
ns <- length(sids <- levels(sce$sample_id))
names(kids) <- kids; names(sids) <- sids2.5 Data overview
2.5.1 Cluster-sample sizes
As we will be aggregating measurements at the cluster-sample level, it is of particular importance to check the number of cells captured for each such instance. While aggregateData (see Section 3.1) allows excluding cluster-sample combinations with less than a threshold number of cells, clusters or samples with overall very low cell-counts may be excluded from further analysis at this point already.
For the Kang et al. (2018) dataset, for example, one might consider removing the Dendritic cells and Megakaryocytes clusters, as these contain less than 50 cells across all samples.
# nb. of cells per cluster-sample
t(table(sce$cluster_id, sce$sample_id))##
## B cells CD14+ Monocytes CD4 T cells CD8 T cells Dendritic cells
## ctrl101 113 186 336 95 5
## ctrl1015 476 783 919 226 11
## ctrl1016 144 419 526 671 10
## ctrl1039 30 116 202 30 5
## ctrl107 51 222 197 31 2
## ctrl1244 134 429 1215 82 28
## ctrl1256 240 383 1136 156 12
## ctrl1488 234 317 1343 78 17
## stim101 144 222 437 121 20
## stim1015 357 683 814 153 17
## stim1016 129 361 426 600 10
## stim1039 39 154 318 40 7
## stim107 56 185 217 22 7
## stim1244 94 318 980 46 19
## stim1256 211 369 1047 133 16
## stim1488 283 370 1658 73 34
##
## FCGR3A+ Monocytes Megakaryocytes NK cells
## ctrl101 81 12 84
## ctrl1015 232 25 208
## ctrl1016 126 15 151
## ctrl1039 28 5 20
## ctrl107 29 5 49
## ctrl1244 53 19 131
## ctrl1256 50 15 275
## ctrl1488 99 21 120
## stim101 126 7 120
## stim1015 222 24 224
## stim1016 124 12 239
## stim1039 36 13 32
## stim107 36 4 51
## stim1244 35 14 136
## stim1256 73 21 257
## stim1488 139 35 1872.5.2 Dimension reduction
The dimension reductions (DR) available within the SCE can be accessed via reducedDims from the scater package. The data provided by Kang et al. (2018) already contains t-SNE coordinates; however, we can of course compute additional dimension reductions using one of scater’s runX functions:
# compute UMAP using 1st 20 PCs
sce <- runUMAP(sce, pca = 20)Using scater’s plotReducedDim function, we can plot t-SNE and UMAP representations colored by cluster and group IDs, respectively. We additionally create a small wrapper function, .plot_dr(), to improve the readability of color legends and simplify the plotting theme:
# wrapper to prettify reduced dimension plots
.plot_dr <- function(sce, dr, col)
plotReducedDim(sce, dimred = dr, colour_by = col) +
guides(fill = guide_legend(override.aes = list(alpha = 1, size = 3))) +
theme_minimal() + theme(aspect.ratio = 1)For our dataset, the t-SNE and UMAP colored by cluster_ids show that cell-populations are well-separated from one another. IFN-\(\beta\) stimulation manifests as a severe shift in the low-dimensional projection of cells when coloring by group_ids, indicating widespread, genome-scale transcriptional changes.
# downsample to max. 100 cells per cluster
cs_by_k <- split(colnames(sce), sce$cluster_id)
cs100 <- unlist(sapply(cs_by_k, function(u)
sample(u, min(length(u), 100))))
# plot t-SNE & UMAP colored by cluster & group ID
for (dr in c("TSNE", "UMAP"))
for (col in c("cluster_id", "group_id"))
.plot_dr(sce[, cs100], dr, col)TSNE
Figure 1: Dimension reduction plots
Cells are colored by cluster ID (A) and group ID (B), respectively. For each cluster, at most 100 cells were sampled for plotting.
UMAP
Figure 2: Dimension reduction plots
Cells are colored by cluster ID (A) and group ID (B), respectively. For each cluster, at most 100 cells were sampled for plotting.
3 Differential State (DS) analysis
To test for state changes across conditions, we will consider two types of approaches: i) mixed models that act directly on cell-level measurements; and ii) aggregation-based methods that act on pseudobulk data. For both approaches, each gene is tested for state changes in each cluster. Thus, a total of \(\#(genes) \times \#(clusters)\) tests will be performed per comparison of interest. The following schematic summarizes the data representation considered by cell- and sample-level approaches, respectively:
Figure 3: Schematic overview of cell- and sample-level approaches for DS analysis
Top panels show a schematic of the data distributions or aggregates across samples (each violin is a group or sample; each dot is a sample) and conditions (blue or orange). The bottom panels highlight the data organization in sub-matrix slices of the original count table.
3.1 Aggregation of single-cell to pseudobulk data
In order to leverage existing robust bulk RNA-seq DE frameworks, such as edgeR (Robinson, McCarthy, and Smyth 2010), DESeq2 (Love, Huber, and Anders 2014), and limma (Ritchie et al. 2015), we first aggregate measurements for each sample (in each cluster) to obtain pseudobulk data.
In general, aggregateData() will aggregate the data by the colData variables specified with argument by, and return a SingleCellExperiment containing pseudobulk data.
For DS analysis, measurements must be aggregated at the cluster-sample level (default by = c("cluster_id", "sample_id"). In this case, the returned SingleCellExperiment will contain one assay per cluster, where rows = genes and columns = samples. Arguments assay and fun specify the input data and summary statistic, respectively, to use for aggregation.
While, in principle, various combinations of input data (raw/(log-)normalized counts, CPM ect.) and summary statistics (sum, mean, median) could be applied, we here default to the sum of raw counts:
pb <- aggregateData(sce,
assay = "counts", fun = "sum",
by = c("cluster_id", "sample_id"))
# one sheet per subpopulation
assayNames(pb)## [1] "B cells" "CD14+ Monocytes" "CD4 T cells"
## [4] "CD8 T cells" "Dendritic cells" "FCGR3A+ Monocytes"
## [7] "Megakaryocytes" "NK cells"# pseudobulks for 1st subpopulation
t(head(assay(pb)))## NOC2L HES4 ISG15 TNFRSF18 TNFRSF4 SDF4
## ctrl101 12 0 13 8 1 13
## ctrl1015 37 4 136 55 16 42
## ctrl1016 13 3 42 10 0 15
## ctrl1039 2 1 16 3 1 1
## ctrl107 7 0 6 3 4 5
## ctrl1244 24 7 25 30 11 7
## ctrl1256 22 1 65 30 11 17
## ctrl1488 19 1 34 27 6 23
## stim101 12 8 1362 3 1 9
## stim1015 34 27 4022 21 2 19
## stim1016 7 8 1271 2 4 6
## stim1039 3 3 393 1 1 2
## stim107 4 2 556 1 0 3
## stim1244 13 10 830 8 2 5
## stim1256 14 10 2235 10 2 6
## stim1488 18 5 2927 13 1 193.2 Pseudobulk-level MDS plot
Prior to conducting any formal testing, we can compute a multi-dimensional scaling (MDS) plot of aggregated signal to explore overall sample similarities.
pbMDS takes as input any SCE containg PB data as returned by aggregateData, and computes MDS dimensions using edgeR. Ideally, such a representation of the data should separate both clusters and groups from one another. Vice versa, samples from the same cluster or group should cluster together.
In our MDS plot on pseudo-bulk counts (Fig. 4), we can observe that the first dimension (MDS1) clearly separates cell populations (clusters), while the second (MDS2) separates control and stimulated samples (groups). Furthermore, the two T-cell clusters fall close to each other.
(pb_mds <- pbMDS(pb))
Figure 4: Pseudobulk-level multidimensional scaling (MDS) plot
Each point represents a cluster-sample instance; points are colored by cluster ID and shaped by group ID.
If you’re not satisfied with how the plot looks, here’s an example of how to modify the ggplot-object from above in various ways:
# use very distinctive shaping of groups & change cluster colors
pb_mds <- pb_mds +
scale_shape_manual(values = c(17, 4)) +
scale_color_manual(values = RColorBrewer::brewer.pal(8, "Set2"))
# change point size & alpha
pb_mds$layers[[1]]$aes_params$size <- 5
pb_mds$layers[[1]]$aes_params$alpha <- 0.6
pb_mds
Figure 5: Pseudobulk-level MDS plot v2
Default plotting aesthetics were modified to change shaping of groups, coloring of clusters, as well as point size and transparency.
3.3 Sample-level analysis: Pseudobulk methods
Once we have assembled the pseudobulk data, we can test for DS using pbDS. By default, a \(\sim group\_id\) model is fit, and the last coefficient of the linear model is tested to be equal to zero.
# run DS analysis
res <- pbDS(pb, verbose = FALSE)
# access results table for 1st comparison
tbl <- res$table[[1]]
# one data.frame per cluster
names(tbl)## [1] "B cells" "CD14+ Monocytes" "CD4 T cells"
## [4] "CD8 T cells" "Dendritic cells" "FCGR3A+ Monocytes"
## [7] "Megakaryocytes" "NK cells"# view results for 1st cluster
k1 <- tbl[[1]]
head(format(k1[, -ncol(k1)], digits = 2))## gene cluster_id logFC logCPM F p_val p_adj.loc p_adj.glb
## 1 NOC2L B cells -0.27278 6.5 1.7e+00 2.2e-01 3.8e-01 3.5e-01
## 2 ISG15 B cells 5.47348 12.2 9.1e+02 2.4e-21 9.3e-19 3.2e-18
## 3 TNFRSF18 B cells -1.34598 6.4 2.6e+01 2.7e-05 1.9e-04 1.7e-04
## 4 CPSF3L B cells -0.13505 6.1 3.6e-01 5.9e-01 7.4e-01 7.2e-01
## 5 AURKAIP1 B cells -0.01435 7.8 1.6e-02 9.5e-01 9.8e-01 9.8e-01
## 6 MRPL20 B cells 0.23892 7.4 2.7e+00 1.1e-01 2.3e-01 2.1e-01Depening on the complexity of the experimental design (e.g., when there are more than two groups present), comparison(s) of interest may need to be specified explicitly. We can provide pbDS with a design matrix capturing the experimental design using model.matrix (package stats), and a contrast matrix that specifies our comparison of interesting using makeContrasts from the limma package. Alternatively, the comparison(s) of interest (or a list thereof) can be specified with via coefs (see ?glmQLFTest for details). For the Kang et al. (2018) dataset, we want to carry out a single comparison of stimulated against control samples, thus placing "ctrl" on the right-hand side as the reference condition:
# construct design & contrast matrix
ei <- metadata(sce)$experiment_info
mm <- model.matrix(~ 0 + ei$group_id)
dimnames(mm) <- list(ei$sample_id, levels(ei$group_id))
contrast <- makeContrasts("stim-ctrl", levels = mm)
# run DS analysis
pbDS(pb, design = mm, contrast = contrast)3.4 Cell-level analysis: Mixed models
Alternative to the above sample-level approach, we fit (for each gene) a mixed model (MM) to the cell-level measurement data. muscat provides implementations of MM that use 3 main approaches:
- fitting linear mixed models (LMMs) on log-normalized data with observational weights,
- fitting LMMs on variance-stabilized data; and,
- fitting generalized linear mixed models (GLMMs) directly on counts
In each case, a \(\sim 1 + \text{group_id} + (1\,|\,\text{sample_id})\) model is fit for each gene, optimizing the log-likelihood (i.e., REML = FALSE). P-values are calculated using the estimates of degrees of freedom specifying by argument df (default "Satterthwaite"). Fitting, testing and moderation are applied subpopulation-wise. For differential testing, mmDS will only consider:
- subpopulations with at least
n_cellscells (default 10) in at leastn_samplessamples (default 2) - genes with a count >=
min_count(default 1) in at leastmin_cells(default 20)
Mixed model based approaches can be run directly on cell-level measurements, and do not require prior aggregation:
# 1st approach
mm <- mmDS(sce, method = "dream",
n_cells = 10, n_samples = 2,
min_counts = 1, min_cells = 20)
# 2nd & 3rd approach
mm <- mmDS(sce, method = "vst", vst = "sctransform")
mm <- mmDS(sce, method = "nbinom")4 Handling results
4.1 Results filtering & overview
To get a general overview of the differential testing results, we first filter them to retain hits FDR < 5% and abs(logFC) > 1, and count the number and frequency of differential findings by cluster. Finally, we can view the top hits (lowest adj. p-value) in each cluster.
# filter FDR < 5%, abs(logFC) > 1 & sort by adj. p-value
tbl_fil <- lapply(tbl, function(u) {
u <- dplyr::filter(u, p_adj.loc < 0.05, abs(logFC) > 1)
dplyr::arrange(u, p_adj.loc)
})
# nb. of DS genes & % of total by cluster
n_de <- vapply(tbl_fil, nrow, numeric(1))
p_de <- format(n_de / nrow(sce) * 100, digits = 3)
data.frame("#DS" = n_de, "%DS" = p_de, check.names = FALSE)## #DS %DS
## B cells 238 3.344
## CD14+ Monocytes 1042 14.639
## CD4 T cells 322 4.524
## CD8 T cells 94 1.321
## Dendritic cells 117 1.644
## FCGR3A+ Monocytes 384 5.395
## Megakaryocytes 28 0.393
## NK cells 168 2.360# view top 2 hits in each cluster
top2 <- bind_rows(lapply(tbl_fil, top_n, 2, p_adj.loc))
format(top2[, -ncol(top2)], digits = 2)## gene cluster_id logFC logCPM F p_val p_adj.loc p_adj.glb
## 1 DNAJC15 B cells 1.1 7.1 12.4 0.00168 0.0074 0.0065
## 2 MCM5 B cells -1.1 6.4 9.9 0.00413 0.0162 0.0141
## 3 CCL3L1 CD14+ Monocytes 1.1 5.8 8.7 0.00750 0.0138 0.0233
## 4 SERPINB2 CD14+ Monocytes -1.1 7.2 8.4 0.00855 0.0155 0.0260
## 5 HOPX CD4 T cells -1.0 4.1 8.0 0.00940 0.0442 0.0282
## 6 CPNE2 CD4 T cells -1.1 4.1 7.9 0.00957 0.0447 0.0287
## 7 TUBA4A CD8 T cells -1.1 7.3 19.7 0.00017 0.0015 0.0009
## 8 ITM2C CD8 T cells -1.0 7.3 14.8 0.00078 0.0057 0.0034
## 9 RALA Dendritic cells -1.0 9.1 8.1 0.01227 0.0352 0.0351
## 10 H2AFZ Dendritic cells -1.0 10.2 7.2 0.01675 0.0454 0.0457
## 11 C1QB FCGR3A+ Monocytes 1.6 6.5 8.8 0.00730 0.0165 0.0228
## 12 CBWD1 FCGR3A+ Monocytes 1.0 6.6 6.9 0.01571 0.0316 0.0434
## 13 TMEM123 Megakaryocytes 1.1 9.7 18.3 0.00065 0.0065 0.0029
## 14 NT5C3A Megakaryocytes 1.3 9.7 13.8 0.00208 0.0194 0.0079
## 15 FOXP1 NK cells -1.1 6.7 11.4 0.00231 0.0120 0.0086
## 16 ITM2C NK cells -1.1 7.4 10.4 0.00344 0.0165 0.01204.2 Calculating expression frequencies
Besides filter DS results based on magnitude (logFCs) and significance (FDR), it is often worthwhile to also consider the expression frequencies of each gene, i.e., the fraction of cells that express a given gene in each sample and/or group.
muscat provides wrapper, calcExprFreqs to compute cluster-sample/-group wise expression frequencies. Here, a gene is considered to be expressed when the specified measurement value (argument assay) falls above a certain threshold (argument th). Note that, assay = "counts" and th = 0 (default) amounts to the fraction of cells for which a respective gene has been detected.
calcExprFreqs will return a SingleCellExperiment object, where sheets (assays) = clusters, rows = genes, and columns = samples (and groups, if group_ids are present in the colData of the input SCE).
frq <- calcExprFreqs(sce, assay = "counts", th = 0)
# one sheet per cluster
assayNames(frq)## [1] "B cells" "CD14+ Monocytes" "CD4 T cells"
## [4] "CD8 T cells" "Dendritic cells" "FCGR3A+ Monocytes"
## [7] "Megakaryocytes" "NK cells"# expression frequencies in each
# sample & group; 1st cluster
t(head(assay(frq), 5))## NOC2L HES4 ISG15 TNFRSF18 TNFRSF4
## ctrl101 0.09734513 0.000000000 0.08849558 0.061946903 0.008849558
## ctrl1015 0.07773109 0.008403361 0.18067227 0.090336134 0.021008403
## ctrl1016 0.08333333 0.013888889 0.20833333 0.048611111 0.000000000
## ctrl1039 0.06666667 0.033333333 0.33333333 0.100000000 0.033333333
## ctrl107 0.09803922 0.000000000 0.07843137 0.058823529 0.019607843
## ctrl1244 0.12686567 0.037313433 0.10447761 0.126865672 0.029850746
## ctrl1256 0.08750000 0.004166667 0.18750000 0.091666667 0.025000000
## ctrl1488 0.06837607 0.004273504 0.11965812 0.076923077 0.012820513
## stim101 0.08333333 0.055555556 0.98611111 0.020833333 0.006944444
## stim1015 0.08963585 0.072829132 0.99719888 0.044817927 0.005602241
## stim1016 0.03875969 0.054263566 1.00000000 0.007751938 0.023255814
## stim1039 0.05128205 0.051282051 1.00000000 0.025641026 0.025641026
## stim107 0.05357143 0.035714286 1.00000000 0.017857143 0.000000000
## stim1244 0.11702128 0.095744681 0.98936170 0.053191489 0.021276596
## stim1256 0.06635071 0.037914692 0.99526066 0.037914692 0.004739336
## stim1488 0.05653710 0.014134276 0.99646643 0.031802120 0.003533569
## ctrl 0.08509142 0.009845288 0.15963432 0.084388186 0.018284107
## stim 0.07235339 0.050266565 0.99543031 0.033511043 0.008377761We can use the obtained frequencies to, for instance, only retain genes that are expressed in an average of 10% of cells in at least 1 group:
gids <- levels(sce$group_id)
frq10 <- vapply(as.list(assays(frq)),
function(u) apply(u[, gids] > 0.1, 1, any),
logical(nrow(sce)))
t(head(frq10))## NOC2L HES4 ISG15 TNFRSF18 TNFRSF4 SDF4
## B cells FALSE FALSE TRUE FALSE FALSE FALSE
## CD14+ Monocytes FALSE TRUE TRUE FALSE FALSE TRUE
## CD4 T cells FALSE FALSE TRUE FALSE FALSE FALSE
## CD8 T cells FALSE FALSE TRUE FALSE FALSE FALSE
## Dendritic cells FALSE TRUE TRUE FALSE TRUE TRUE
## FCGR3A+ Monocytes FALSE TRUE TRUE FALSE FALSE FALSE
## Megakaryocytes FALSE FALSE TRUE FALSE FALSE FALSE
## NK cells FALSE FALSE TRUE TRUE FALSE TRUEtbl_fil2 <- lapply(kids, function(k)
dplyr::filter(tbl_fil[[k]],
gene %in% names(which(frq10[, k]))))
# nb. of DS genes & % of total by cluster
n_de <- vapply(tbl_fil2, nrow, numeric(1))
p_de <- format(n_de / nrow(sce) * 100, digits = 3)
data.frame("#DS" = n_de, "%DS" = p_de, check.names = FALSE)## #DS %DS
## B cells 226 3.175
## CD14+ Monocytes 622 8.738
## CD4 T cells 154 2.164
## CD8 T cells 94 1.321
## Dendritic cells 117 1.644
## FCGR3A+ Monocytes 382 5.367
## Megakaryocytes 28 0.393
## NK cells 160 2.2484.3 Formatting results
Especially when testing multiple contrasts or coefficients, the results returned by runDS may become very complex and unhandy for exploration or exporting. Results can be formatted using resDS, which provides two alternative modes for formatting: bind = "row"/"col".
When bind = "row", results from all comparisons will be merged vertically (analogous to do.call("rbind", ...)) into a tidy format table, with column contrast/coef specifying the comparison.
Otherwise, bind = "col", results will be merged horizontally into a single wide table where all results for a given gene and cluster are kept in one row. An identifier of the respective contrast of coefficient is then appended to the column names. This format is useful when wanting to view a specific gene’s behavior across, for example, multiple treatments, but will become messy when many comparisons are included.
Expression frequencies computed with calcExprFreqs, as well as cluster-sample level avg. CPM, can be included in the results by setting frq/cpm = TRUE. Alternatively, if the former have been pre-computed, they can be supplied directly as an input to resDS (see example below).
# tidy format; attach pre-computed expression frequencies
resDS(sce, res, bind = "row", frq = frq)
# big-table (wide) format; attach CPMs
resDS(sce, res, bind = "col", cpm = TRUE)Alternatively, if expression frequencies have not been pre-computed with calcExprFreqs, they may be added to the results table directly by specifying frq = TRUE:
# compute expression frequencies on the fly
resDS(sce, res, frq = TRUE)5 Visualizing results
5.1 Between-cluster concordance
DS analysis aims at identifying population-specific changes in state (or expression) across conditions. In this setting, key questions of interest arise, e.g., which genes are DE in only a single (or very few) clusters? How many DE genes are shared between clusters? In summary, what is the general concordance in differential findings between clusters?
To gain an impression of the between-cluster (dis-)agreement on DE genes, we generate an UpSet-plot that visualizes the number of DE genes that are shared across or unique to certain clusters:
library(UpSetR)
de_gs_by_k <- map(tbl_fil, "gene")
upset(fromList(de_gs_by_k))
Upset plot. Included are DS findings (FDR < 0.05, |logFC| > 1) across all clusters; shown are the 50 most frequent interactions.
An UpSet plot as the one above tells us, for instance, that 185 genes are differential for all subpopulations; 387 across both Monocytes clusters; and 159 only in the B cells cluster.
5.2 DR colored by expression
The code chunk generates a set of t-SNEs colored by gene expression for the top-8 DS genes. To match the affected cells to their cluster and experimental group, see the t-SNEs colored by cluster and group ID from above.
# pull top-8 DS genes across all clusters
top8 <- bind_rows(tbl_fil) %>%
slice_min(p_adj.loc, n = 8,
with_ties = FALSE) %>%
pull("gene")
# for ea. gene in 'top8', plot t-SNE colored by its expression
ps <- lapply(top8, function(g)
.plot_dr(sce[, cs100], "TSNE", g) +
ggtitle(g) + theme(legend.position = "none"))
# arrange plots
plot_grid(plotlist = ps, ncol = 4, align = "vh")
Figure 7: t-SNE colored by gene expression
Show are t-SNE projections with cells colored by the expression of the top-8 DS genes. For each cluster, at most 100 cells were sampled for plotting.
5.3 Cell-level viz.: Violin plots
For changes of high interest, we can view the cell-level expression profiles of a specific gene across samples or groups using plotExpression (scater package). Here, we generate violin plots for the top-6 DS genes (lowest adj. p-value) in the B cells cluster2 Note that, as DS testing is done at the cluster-level, we need to subset the cells that have been assigned to the corresponding cluster for plotting..
plotExpression(sce[, sce$cluster_id == "B cells"],
features = tbl_fil$`B cells`$gene[seq_len(6)],
x = "sample_id", colour_by = "group_id", ncol = 3) +
guides(fill = guide_legend(override.aes = list(size = 5, alpha = 1))) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
Figure 8: Violin plots
Show are the top 6 hits (lowest adj. p-value) for the B cells cluster. Each violin is a sample; points are colored by group ID.
5.4 Sample-level viz.: Pseudobulk heatmaps
Especially when wanting to gain an overview of numerous DE testing results for many clusters, both dimension reduction and cell-level visualizations require a lot of space can become cumbersome to interpret. In this setting, it is thus recommended to visualize aggregated measures, e.g., mean expressions by cluster sample.
# top-5 DS genes per cluster
pbHeatmap(sce, res, top_n = 5)
Alternatively, pbHeatmap provides a set of options regarding which cluster(s), gene(s), and comparison to include (arguments k, g and c, respectively). For example, the following options render a heatmap visualizing the top 20 DS genes for the B cells cluster:
# top-20 DS genes for single cluster
pbHeatmap(sce, res, k = "B cells")
Similarly, we can visualize the cluster-sample means of a single gene of interest across all clusters in order to identify cell-types that are affected similarly by different experimental conditions:
# single gene across all clusters
pbHeatmap(sce, res, g = "ISG20")
Session info
sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] UpSetR_1.4.0 scater_1.30.0
## [3] scuttle_1.12.0 Matrix_1.6-1.1
## [5] muscData_1.15.0 SingleCellExperiment_1.24.0
## [7] SummarizedExperiment_1.32.0 Biobase_2.62.0
## [9] GenomicRanges_1.54.0 GenomeInfoDb_1.38.0
## [11] IRanges_2.36.0 S4Vectors_0.40.0
## [13] MatrixGenerics_1.14.0 matrixStats_1.0.0
## [15] ExperimentHub_2.10.0 AnnotationHub_3.10.0
## [17] BiocFileCache_2.10.0 dbplyr_2.3.4
## [19] BiocGenerics_0.48.0 purrr_1.0.2
## [21] muscat_1.16.0 limma_3.58.0
## [23] ggplot2_3.4.4 dplyr_1.1.3
## [25] cowplot_1.1.1 BiocStyle_2.30.0
##
## loaded via a namespace (and not attached):
## [1] RcppAnnoy_0.0.21 later_1.3.1
## [3] splines_4.3.1 filelock_1.0.2
## [5] bitops_1.0-7 tibble_3.2.1
## [7] lifecycle_1.0.3 Rdpack_2.5
## [9] edgeR_4.0.0 doParallel_1.0.17
## [11] globals_0.16.2 lattice_0.22-5
## [13] MASS_7.3-60 backports_1.4.1
## [15] magrittr_2.0.3 sass_0.4.7
## [17] rmarkdown_2.25 jquerylib_0.1.4
## [19] yaml_2.3.7 httpuv_1.6.12
## [21] sctransform_0.4.1 DBI_1.1.3
## [23] minqa_1.2.6 RColorBrewer_1.1-3
## [25] multcomp_1.4-25 abind_1.4-5
## [27] zlibbioc_1.48.0 EnvStats_2.8.1
## [29] glmmTMB_1.1.8 RCurl_1.98-1.12
## [31] TH.data_1.1-2 rappdirs_0.3.3
## [33] sandwich_3.0-2 circlize_0.4.15
## [35] GenomeInfoDbData_1.2.11 ggrepel_0.9.4
## [37] pbkrtest_0.5.2 irlba_2.3.5.1
## [39] listenv_0.9.0 parallelly_1.36.0
## [41] DelayedMatrixStats_1.24.0 codetools_0.2-19
## [43] DelayedArray_0.28.0 tidyselect_1.2.0
## [45] shape_1.4.6 farver_2.1.1
## [47] lme4_1.1-34 ScaledMatrix_1.10.0
## [49] viridis_0.6.4 jsonlite_1.8.7
## [51] GetoptLong_1.0.5 BiocNeighbors_1.20.0
## [53] ellipsis_0.3.2 survival_3.5-7
## [55] iterators_1.0.14 emmeans_1.8.9
## [57] foreach_1.5.2 tools_4.3.1
## [59] progress_1.2.2 Rcpp_1.0.11
## [61] blme_1.0-5 glue_1.6.2
## [63] gridExtra_2.3 SparseArray_1.2.0
## [65] xfun_0.40 mgcv_1.9-0
## [67] DESeq2_1.42.0 withr_2.5.1
## [69] numDeriv_2016.8-1.1 BiocManager_1.30.22
## [71] fastmap_1.1.1 boot_1.3-28.1
## [73] fansi_1.0.5 caTools_1.18.2
## [75] digest_0.6.33 rsvd_1.0.5
## [77] mime_0.12 R6_2.5.1
## [79] estimability_1.4.1 colorspace_2.1-0
## [81] Cairo_1.6-1 gtools_3.9.4
## [83] RSQLite_2.3.1 RhpcBLASctl_0.23-42
## [85] utf8_1.2.4 tidyr_1.3.0
## [87] generics_0.1.3 variancePartition_1.32.0
## [89] data.table_1.14.8 corpcor_1.6.10
## [91] httr_1.4.7 prettyunits_1.2.0
## [93] S4Arrays_1.2.0 uwot_0.1.16
## [95] pkgconfig_2.0.3 gtable_0.3.4
## [97] blob_1.2.4 ComplexHeatmap_2.18.0
## [99] XVector_0.42.0 remaCor_0.0.16
## [101] htmltools_0.5.6.1 bookdown_0.36
## [103] TMB_1.9.6 clue_0.3-65
## [105] scales_1.2.1 png_0.1-8
## [107] fANCOVA_0.6-1 knitr_1.44
## [109] reshape2_1.4.4 rjson_0.2.21
## [111] curl_5.1.0 coda_0.19-4
## [113] nlme_3.1-163 nloptr_2.0.3
## [115] cachem_1.0.8 zoo_1.8-12
## [117] GlobalOptions_0.1.2 stringr_1.5.0
## [119] BiocVersion_3.18.0 KernSmooth_2.23-22
## [121] parallel_4.3.1 vipor_0.4.5
## [123] AnnotationDbi_1.64.0 pillar_1.9.0
## [125] grid_4.3.1 vctrs_0.6.4
## [127] gplots_3.1.3 promises_1.2.1
## [129] BiocSingular_1.18.0 beachmat_2.18.0
## [131] xtable_1.8-4 cluster_2.1.4
## [133] beeswarm_0.4.0 evaluate_0.22
## [135] magick_2.8.1 mvtnorm_1.2-3
## [137] cli_3.6.1 locfit_1.5-9.8
## [139] compiler_4.3.1 rlang_1.1.1
## [141] crayon_1.5.2 future.apply_1.11.0
## [143] labeling_0.4.3 plyr_1.8.9
## [145] ggbeeswarm_0.7.2 stringi_1.7.12
## [147] viridisLite_0.4.2 BiocParallel_1.36.0
## [149] Biostrings_2.70.0 lmerTest_3.1-3
## [151] munsell_0.5.0 aod_1.3.2
## [153] hms_1.1.3 bit64_4.0.5
## [155] sparseMatrixStats_1.14.0 future_1.33.0
## [157] KEGGREST_1.42.0 shiny_1.7.5.1
## [159] statmod_1.5.0 interactiveDisplayBase_1.40.0
## [161] rbibutils_2.2.15 memoise_2.0.1
## [163] broom_1.0.5 bslib_0.5.1
## [165] bit_4.0.5References
Butler, Andrew, Paul Hoffman, Peter Smibert, Efthymia Papalexi, and Rahul Satija. 2018. “Integrating Single-Cell Transcriptomic Data Across Different Conditions, Technologies, and Species.” Nature Biotechnology 36 (5): 411–20.
Crowell, Helena L, Charlotte Soneson, Pierre-Luc Germain, Daniela Calini, Ludovic Collin, Catarina Raposo, Dheeraj Malhotra, and Mark D Robinson. 2020. “Muscat Detects Subpopulation-Specific State Transitions from Multi-Sample Multi-Condition Single-Cell Transcriptomics Data.” Nature Communications 11 (1): 6077.
Diaz-Mejia, J Javier, J Javier Diaz-Mejia, Elaine C Meng, Alexander R Pico, Sonya A MacParland, Troy Ketela, Trevor J Pugh, Gary D Bader, and John H Morris. 2019. “Evaluation of Methods to Assign Cell Type Labels to Cell Clusters from Single-Cell RNA-sequencing Data.” F1000Research 8: 296.
Duò, Angelo, Mark D Robinson, and Charlotte Soneson. 2018. “A Systematic Performance Evaluation of Clustering Methods for Single-Cell RNA-seq Data.” F1000Research 7: 1141.
Freytag, Saskia, Luyi Tian, Ingrid Lönnstedt, Milica Ng, and Melanie Bahlo. 2018. “Comparison of Clustering Tools in R for Medium-Sized 10x Genomics Single-Cell RNA-sequencing Data.” F1000Research 7: 1297.
Hafemeister, Christoph, and Rahul Satija. 2019. “Normalization and Variance Stabilization of Single-Cell RNA-seq Data Using Regularized Negative Binomial Regression.” bioRxiv 576827.
Kang, Hyun Min, Meena Subramaniam, Sasha Targ, Michelle Nguyen, Lenka Maliskova, Elizabeth McCarthy, Eunice Wan, et al. 2018. “Multiplexed Droplet Single-Cell RNA-sequencing Using Natural Genetic Variation.” Nature Biotechnology 36 (1): 89–94.
Love, Michael I, Wolfgang Huber, and Simon Anders. 2014. “Moderated Estimation of Fold Change and Dispersion for RNA-seq Data with DESeq2.” Genome Biology 15 (12): 550.
McCarthy, Davis J, Kieran R Campbell, Aaron T L Lun, and Quin F Wills. 2017. “Scater: Pre-Processing, Quality Control, Normalization and Visualization of Single-Cell RNA-seq Data in R.” Bioinformatics 33 (8): 1179–86.
Ritchie, Matthew E, Belinda Phipson, Di Wu, Yifang Hu, Charity W Law, Wei Shi, and Gordon K Smyth. 2015. “Limma Powers Differential Expression Analyses for RNA-sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47.
Robinson, Mark D, Davis J McCarthy, and Gordon K Smyth. 2010. “EdgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40.
Soneson, Charlotte, and Mark D Robinson. 2018. “Bias, Robustness and Scalability in Single-Cell Differential Expression Analysis.” Nature Methods 15 (4): 255–61.
Stegle, Oliver, Sarah A Teichmann, and John C Marioni. 2015. “Computational and Analytical Challenges in Single-Cell Transcriptomics.” Nature Reviews Genetics 16 (3): 133–45.
Stuart, Tim, Andrew Butler, Paul Hoffman, Christoph Hafemeister, Efthymia Papalexi, William M Mauck 3rd, Yuhan Hao, Marlon Stoeckius, Peter Smibert, and Rahul Satija. 2019. “Comprehensive Integration of Single-Cell Data.” Cell 177 (7): 1888–1902.e21.
Trapnell, Cole. 2015. “Defining Cell Types and States with Single-Cell Genomics.” Genome Research 25 (10): 1491–8.
Wagner, Allon, Aviv Regev, and Nir Yosef. 2016. “Revealing the Vectors of Cellular Identity with Single-Cell Genomics.” Nature Biotechnology 34 (11): 1145–60.
Zhang, Allen W, Ciara O, Elizabeth Chavez, Jamie L P Lim, Andrew McPherson, Matt Wiens, Pascale Walters, et al. 2019. “Probabilistic Cell Type Assignment of Single-Cell Transcriptomic Data Reveals Spatiotemporal Microenvironment Dynamics in Human Cancers.” bioRxiv 521914.