Differential detection analysis
November 13, 2024
Abstract
In this vignette, we display how muscat can be used to perform differrential detection (DD) analyses in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data. Furthermore, we show how DD and differential state (DS) analysis results on the same data can be effectively combined. This vignette thus introduces a workflow that allows users to jointly assess two biological hypotheses that often contain orthogonal information, which thus can be expected to improve their understanding of complex biological phenomena, at no extra cost.
Package
muscat 1.21.0
Contents
Based on Gilis et al. (2023)
Gilis J, Perin L, Malfait M, Van den Berge K,
Assefa AT, Verbist B, Risso D, and Clement L:
Differential detection workflows for
multi-sample single-cell RNA-seq data.
bioRxiv (2023). DOI: 10.1101/2023.12.17.572043
Load packages
library(dplyr)
library(purrr)
library(tidyr)
library(scater)
library(muscat)
library(ggplot2)
library(patchwork)1 Introduction
Single-cell RNA-sequencing (scRNA-seq) has improved our understanding of complex biological processes by elucidating cell-level heterogeneity in gene expression. One of the key tasks in the downstream analysis of scRNA-seq data is studying differential gene expression (DE). Most DE analysis methods aim to identify genes for which the average expression differs between biological groups of interest, e.g., between cell types or between diseased and healthy cells. As such, most methods allow for assessing only one aspect of the gene expression distribution: the mean. However, in scRNA-seq data, differences in other characteristics between count distributions can commonly be observed.
One such characteristic is gene detection, i.e., the number of cells in which a gene is (detectably) expressed. Analogous to a DE analysis, a differential detection (DD) analysis aims to identify genes for which the average fraction of cells in which the gene is detected changes between groups. In Gilis et al. (2023), we show how DD analysis contain information that is biologically relevant, and that is largely orthogonal to the information obtained from DE analysis on the same data.
In this vignette, we display how muscat can be used to perform DD analyses in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data. Furthermore, we show how DD and DS analysis results on the same data can be effectively combined using a two-stage testing approach. This workflow thus allows users to jointly assess two biological hypotheses containing orthogonal information, which thus can be expected to improve their understanding of complex biological phenomena, at no extra cost.
2 Setup
We will use the same data as in the differential state (DS) analyses described in muscat, namely, scRNA-seq data acquired on PBMCs from 8 patients before and after IFN-\(\beta\) treatment. For a more detailed description of these data and subsequent preprocessing, we refer to muscat.
library(ExperimentHub)
eh <- ExperimentHub()
query(eh, "Kang")## ExperimentHub with 3 records
## # snapshotDate(): 2024-11-13
## # $dataprovider: NCI_GDC, GEO
## # $species: Homo sapiens
## # $rdataclass: character, SingleCellExperiment, BSseq
## # additional mcols(): taxonomyid, genome, description,
## # coordinate_1_based, maintainer, rdatadateadded, preparerclass, tags,
## # rdatapath, sourceurl, sourcetype
## # retrieve records with, e.g., 'object[["EH1661"]]'
##
## title
## EH1661 | Whole Genome Bisulfit Sequencing Data for 47 samples
## EH1662 | Whole Genome Bisulfit Sequencing Data for 47 samples
## EH2259 | Kang18_8vs8(sce <- eh[["EH2259"]])## class: SingleCellExperiment
## dim: 35635 29065
## metadata(0):
## assays(1): counts
## rownames(35635): MIR1302-10 FAM138A ... MT-ND6 MT-CYB
## rowData names(2): ENSEMBL SYMBOL
## colnames(29065): AAACATACAATGCC-1 AAACATACATTTCC-1 ... TTTGCATGGTTTGG-1
## TTTGCATGTCTTAC-1
## colData names(5): ind stim cluster cell multiplets
## reducedDimNames(1): TSNE
## mainExpName: NULL
## altExpNames(0):We further apply some minimal filtering to remove low-quality genes and cells, and use prepSCE() to standardize cell metadata such that slots specifying cluster (cell), sample (stim+ind), and group (stim) identifiers conform with the muscat framework:
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
qc <- perCellQCMetrics(sce)
sce <- sce[, !isOutlier(qc$detected, nmads=2, log=TRUE)]
sce <- sce[rowSums(counts(sce) > 1) >= 10, ]
sce$id <- paste0(sce$stim, sce$ind)
sce <- prepSCE(sce, "cell", "id", "stim")
table(sce$cluster_id, sce$group_id)##
## ctrl stim
## B cells 1422 1313
## CD14+ Monocytes 2855 2662
## CD4 T cells 5874 5897
## CD8 T cells 1369 1188
## Dendritic cells 90 130
## FCGR3A+ Monocytes 698 791
## Megakaryocytes 117 130
## NK cells 1038 1246table(sce$sample_id)##
## ctrl101 ctrl1015 ctrl1016 ctrl1039 ctrl107 ctrl1244 ctrl1256 ctrl1488
## 912 2880 2062 436 586 2091 2267 2229
## stim101 stim1015 stim1016 stim1039 stim107 stim1244 stim1256 stim1488
## 1197 2494 1901 639 578 1642 2127 27792.1 Aggregation
In general, aggregateData() will aggregate the data by the colData variables specified with argument by, and return a SingleCellExperiment containing pseudobulk data.
To perform a pseudobulk-level 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.
In a differential detection (DD) analysis, the default choice of the summary statistic used for aggregation is fun = "num.detected". This strategy can be thought of as first binarizing the gene expression values (1: expressed, 0: not expressed), and subsequently performing a simple summation of the binarized gene expression counts for cells belonging to the same cluster-sample level. Hence, the resulting pseudobulk-level expression count reflects the total number of cells in a particular cluster-sample level with a non-zero gene expression value.
In a differential state (DS) analysis, the default choice for aggregation is fun = "sum", which amounts to the simple summation of the raw gene expression counts of cells belonging to the same cluster-sample level.
pb_sum <- aggregateData(sce,
assay="counts", fun="sum",
by=c("cluster_id", "sample_id"))
pb_det <- aggregateData(sce,
assay="counts", fun="num.detected",
by=c("cluster_id", "sample_id"))
t(head(assay(pb_det)))## NOC2L HES4 ISG15 TNFRSF18 TNFRSF4 SDF4
## ctrl101 11 0 10 7 1 10
## ctrl1015 37 4 86 43 10 39
## ctrl1016 12 2 30 7 0 15
## ctrl1039 2 1 10 3 1 1
## ctrl107 5 0 4 3 1 5
## ctrl1244 17 5 14 17 4 6
## ctrl1256 21 1 45 22 6 16
## ctrl1488 16 1 28 18 3 20
## stim101 12 8 142 3 1 8
## stim1015 32 26 356 16 2 16
## stim1016 5 7 129 1 3 6
## stim1039 2 2 39 1 1 1
## stim107 3 2 56 1 0 3
## stim1244 11 9 93 5 2 5
## stim1256 14 8 210 8 1 6
## stim1488 16 4 282 9 1 15Qiu (2020) demonstrated that binarizing scRNA-seq counts generates expression profiles that still accurately reflect biological variation. This finding was confirmed by Bouland, Mahfouz, and Reinders (2021), who showed that the frequencies of zero counts capture biological variability, and further claimed that a binarized representation of the single-cell expression data allows for a more robust description of the relative abundance of transcripts than counts.
pbMDS(pb_sum) + ggtitle("Σ counts") +
pbMDS(pb_det) + ggtitle("# detected") +
plot_layout(guides="collect") +
plot_annotation(tag_levels="A") &
theme(legend.key.size=unit(0.5, "lines"))
Figure 1: Pseudobulk-level multidimensional scaling (MDS) plot based on (A) sum of counts and (B) sum of binarized counts (i.e., counting the number of detected features) in each cluster-sample
2.2 Analysis
Once we have assembled the pseudobulk data, we can test for DD using pbDD(). By default, a \(\sim\)group_id model is fit, and the last coefficient of the linear model is tested to be equal to zero.
res_DD <- pbDD(pb_det, min_cells=0, filter="none", verbose=FALSE)2.3 Handling and visualizing results
Inspection, manipulation, and visualization of DD analysis results follows the same principles as for a DS analysis. For a detailed description, we refer to the DS analysis vignettemuscat. Below, some basic functionalities are being displayed.
tbl <- res_DD$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 -3.0e-01 7.6 2.4e+00 1.2e-01 2.9e-01 3.1e-01
## 2 HES4 B cells 2.2e+00 6.5 3.5e+01 1.1e-08 2.4e-07 2.8e-07
## 3 ISG15 B cells 2.6e+00 10.3 1.1e+03 9.3e-88 1.1e-84 8.6e-84
## 4 TNFRSF18 B cells -1.4e+00 7.3 3.4e+01 1.6e-08 3.5e-07 4.0e-07
## 5 TNFRSF4 B cells -1.1e+00 5.8 5.7e+00 1.8e-02 7.5e-02 8.0e-02
## 6 SDF4 B cells -8.4e-01 7.3 1.5e+01 1.7e-04 1.7e-03 1.8e-03# filter FDR < 5%, |logFC| > 1 & sort by adj. p-value
tbl_fil <- lapply(tbl, \(u)
filter(u,
p_adj.loc < 0.05,
abs(logFC) > 1) |>
arrange(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("#DD" = n_de, "%DD" = p_de, check.names = FALSE)## #DD %DD
## B cells 715 10.04
## CD14+ Monocytes 1982 27.84
## CD4 T cells 498 7.00
## CD8 T cells 333 4.68
## Dendritic cells 277 3.89
## FCGR3A+ Monocytes 1110 15.59
## Megakaryocytes 109 1.53
## NK cells 423 5.94library(UpSetR)
de_gs_by_k <- map(tbl_fil, "gene")
upset(fromList(de_gs_by_k))
3 Stagewise anaysis
While DD analysis results may contain biologically relevant information in their own right, we show in Gilis et al. (2023) that combing DD and DS analysis results on the same data can further improve our understanding of complex biological phenomena. In the remainder of this vignette, we show how DD and DS analysis results on the same data can be effectively combined.
For this, we build on the two-stage testing paradigm proposed by Van den Berge et al. (2017). In the first stage of this testing procedure, we identify differential genes by using an omnibus test for differential detection and differential expression (DE). The null hypothesis for this test is that the gene is neither differentially detected, nor differentially expressed.
In the second stage, we perform post-hoc tests on the differential genes from stage one to unravel whether they are DD, DE or both. Compared to the individual DD and DS analysis results, the two-stage approach increases statistical power and provides better type 1 error control.
res_DS <- pbDS(pb_sum, min_cells=0, filter="none", verbose=FALSE)res <- stagewise_DS_DD(res_DS, res_DD, verbose=FALSE)
head(res[[1]][[1]]) # results for 1st cluster## DataFrame with 6 rows and 8 columns
## gene p_adj p_val.DS p_val.DD cluster_id contrast
## <character> <numeric> <numeric> <numeric> <character> <character>
## 1 NOC2L 3.73143e-01 NA NA B cells stim
## 2 HES4 4.71932e-07 1.35239e-05 5.53293e-08 B cells stim
## 3 ISG15 2.17494e-84 2.62823e-32 4.67394e-87 B cells stim
## 4 TNFRSF18 6.86768e-07 1.00944e-04 8.19769e-08 B cells stim
## 5 TNFRSF4 1.24236e-02 4.70871e-03 9.07348e-02 B cells stim
## 6 SDF4 3.21728e-03 6.89599e-02 8.71664e-04 B cells stim
## res_DS res_DD
## <data.frame> <data.frame>
## 1 NOC2L:B cells:-0.208711:... NOC2L:B cells:-0.303141:...
## 2 HES4:B cells: 2.225287:... HES4:B cells: 2.201890:...
## 3 ISG15:B cells: 5.521586:... ISG15:B cells: 2.568004:...
## 4 TNFRSF18:B cells:-1.271499:... TNFRSF18:B cells:-1.382451:...
## 5 TNFRSF4:B cells:-1.645505:... TNFRSF4:B cells:-1.126530:...
## 6 SDF4:B cells:-0.612369:... SDF4:B cells:-0.844448:...3.1 Comparison
# for each approach, get adjusted p-values across clusters
ps <- map_depth(res, 2, \(df) {
data.frame(
df[, c("gene", "cluster_id")],
p_adj.stagewise=df$p_adj,
p_adj.DS=df$res_DS$p_adj.loc,
p_adj.DD=df$res_DD$p_adj.loc)
}) |>
lapply(do.call, what=rbind) |>
do.call(what=rbind) |>
data.frame(row.names=NULL)
head(ps)## gene cluster_id p_adj.stagewise p_adj.DS p_adj.DD
## 1 NOC2L B cells 3.731430e-01 6.064017e-01 2.863777e-01
## 2 HES4 B cells 4.719323e-07 5.686554e-05 2.399617e-07
## 3 ISG15 B cells 2.174939e-84 1.834502e-29 1.077728e-84
## 4 TNFRSF18 B cells 6.867680e-07 3.576604e-04 3.468337e-07
## 5 TNFRSF4 B cells 1.242358e-02 9.526608e-03 7.494398e-02
## 6 SDF4 B cells 3.217278e-03 8.069408e-02 1.727711e-03To get an overview of how different approaches compare, we can count the number of genes found differential in each cluster for a given FDR threshold:
# for each approach & cluster, count number
# of genes falling below 5% FDR threshold
ns <- lapply(seq(0, 0.2, 0.005), \(th) {
ps |>
mutate(th=th) |>
group_by(cluster_id, th) |>
summarise(
.groups="drop",
across(starts_with("p_"),
\(.) sum(. < th, na.rm=TRUE)))
}) |>
do.call(what=rbind) |>
pivot_longer(starts_with("p_"))
ggplot(ns, aes(th, value, col=name)) +
geom_line(linewidth=0.8, key_glyph="point") +
geom_vline(xintercept=0.05, lty=2, linewidth=0.4) +
guides(col=guide_legend(NULL, override.aes=list(size=3))) +
labs(x="FDR threshold", y="number of significantly\ndifferential genes") +
facet_wrap(~cluster_id, scales="free_y", nrow=2) +
theme_bw() + theme(
panel.grid.minor=element_blank(),
legend.key.size=unit(0.5, "lines"))
We can further identify which hits are shared between or unique to a given approach. In the example below, for instance, the vast majority of hits is common to all approaches, many hits are shared between DD and stagewise testing, and only few genes are specific to any one approach:
# subset adjuster p-values for cluster of interest
qs <- ps[grep("CD4", ps$cluster_id), grep("p_", names(ps))]
# for each approach, extract genes at 5% FDR threshold
gs <- apply(qs, 2, \(.) ps$gene[. < 0.05])
# visualize set intersections between approaches
UpSetR::upset(UpSetR::fromList(gs), order.by="freq")
Figure 2: Upset plot of differential findings (FDR < 0.05) across DS, DD, and stagewise analysis for an exemplary cluster; shown are the 50 most frequent interactions
# extract genes unique to stagewise testing
sw <- grep("stagewise", names(gs))
setdiff(gs[[sw]], unlist(gs[-sw]))## [1] "OLIG1" "MRPL39" "SLC31A1"Session info
sessionInfo()## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-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] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] tidyr_1.3.1 UpSetR_1.4.0
## [3] scater_1.35.0 scuttle_1.17.0
## [5] muscData_1.21.0 SingleCellExperiment_1.29.1
## [7] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [9] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [11] IRanges_2.41.0 S4Vectors_0.45.1
## [13] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [15] ExperimentHub_2.15.0 AnnotationHub_3.15.0
## [17] BiocFileCache_2.15.0 dbplyr_2.5.0
## [19] BiocGenerics_0.53.2 generics_0.1.3
## [21] purrr_1.0.2 muscat_1.21.0
## [23] limma_3.63.2 ggplot2_3.5.1
## [25] dplyr_1.1.4 cowplot_1.1.3
## [27] patchwork_1.3.0 BiocStyle_2.35.0
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## loaded via a namespace (and not attached):
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## [4] bitops_1.0-9 tibble_3.2.1 lifecycle_1.0.4
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