Using scrapper to analyze single-cell data
Revised: September 8, 2024
Package
scrapper 1.1.7
Contents
1 Overview
scrapper implements bindings to C++ code for analyzing single-cell data, mostly from the libscran libraries. Each function performs an individual analysis step ranging from quality control to clustering and marker detection. scrapper is mostly intended for other Bioconductor package developers to build more user-friendly end-to-end workflows.
2 Quick start
Let’s fetch a small single-cell RNA-seq dataset for demonstration purposes:
library(scRNAseq)
sce.z <- ZeiselBrainData()
sce.z## class: SingleCellExperiment
## dim: 20006 3005
## metadata(0):
## assays(1): counts
## rownames(20006): Tspan12 Tshz1 ... mt-Rnr1 mt-Nd4l
## rowData names(1): featureType
## colnames(3005): 1772071015_C02 1772071017_G12 ... 1772066098_A12
## 1772058148_F03
## colData names(9): tissue group # ... level1class level2class
## reducedDimNames(0):
## mainExpName: gene
## altExpNames(2): repeat ERCCWe run it through all the scrapper functions.
# Wrapping it in a DelayedArray to avoid making unnecessary copies when we
# do the various subsetting steps.
library(DelayedArray)
counts <- DelayedArray(assay(sce.z))
# We can set the number of threads higher in real applications, but
# we want to avoid stressing out Bioconductor's build system.
nthreads <- 2
library(scrapper)
is.mito <- grepl("^mt-", rownames(counts))
rna.qc.metrics <- computeRnaQcMetrics(counts, subsets=list(mt=is.mito), num.threads=nthreads)
rna.qc.thresholds <- suggestRnaQcThresholds(rna.qc.metrics)
rna.qc.filter <- filterRnaQcMetrics(rna.qc.thresholds, rna.qc.metrics)
filtered <- counts[,rna.qc.filter,drop=FALSE]
size.factors <- centerSizeFactors(rna.qc.metrics$sum[rna.qc.filter])
normalized <- normalizeCounts(filtered, size.factors)
gene.var <- modelGeneVariances(normalized, num.threads=nthreads)
top.hvgs <- chooseHighlyVariableGenes(gene.var$statistics$residuals)
pca <- runPca(normalized[top.hvgs,], num.threads=nthreads)
umap.out <- runUmap(pca$components, num.threads=nthreads)
tsne.out <- runTsne(pca$components, num.threads=nthreads)
snn.graph <- buildSnnGraph(pca$components, num.threads=nthreads)
clust.out <- clusterGraph(snn.graph)
markers <- scoreMarkers(normalized, groups=clust.out$membership, num.threads=nthreads)Now we can have a look at some of the results. For example, we can make a t-SNE plot:
plot(tsne.out[,1], tsne.out[,2], pch=clust.out$membership)
We can also have a look at the top markers for each cluster, say, based on the median AUC:
top.markers <- lapply(markers$auc, function(x) {
head(rownames(x)[order(x$median, decreasing=TRUE)], 10)
})
head(top.markers)## $`1`
## [1] "Gad1" "Gad2" "Ndrg4" "Stmn3" "Tspyl4" "Scg5" "Syp" "Snap25"
## [9] "Nap1l5" "Rab3a"
##
## $`2`
## [1] "Plp1" "Mbp" "Cnp" "Mobp" "Mog" "Sept4" "Ugt8a" "Trf"
## [9] "Gsn" "Cldn11"
##
## $`3`
## [1] "Stmn3" "Calm1" "Scg5" "Chn1" "Snap25" "Pcsk2" "Calm2" "Mdh1"
## [9] "Nme1" "Stmn2"
##
## $`4`
## [1] "Chn1" "Stmn3" "Hpca" "Calm2"
## [5] "Neurod6" "3110035E14Rik" "Crym" "Calm1"
## [9] "Ppp3r1" "Ppp3ca"
##
## $`5`
## [1] "Ywhah" "Syp" "Snap25" "Ndrg4" "Vsnl1" "Rab3a" "Chn1" "Stmn3"
## [9] "Uchl1" "Ppp3r1"
##
## $`6`
## [1] "Atp1b1" "Ppp3ca" "Nsg2" "Tspan13"
## [5] "Syp" "Crym" "Chn1" "3110035E14Rik"
## [9] "Thy1" "Gria1"3 Dealing with multiple batches
Let’s fetch another brain dataset and combine it with the previous one.
sce.t <- TasicBrainData()
common <- intersect(rownames(sce.z), rownames(sce.t))
combined <- cbind(
DelayedArray(assay(sce.t))[common,],
DelayedArray(assay(sce.z))[common,]
)
block <- rep(c("tasic", "zeisel"), c(ncol(sce.t), ncol(sce.z)))We can now perform the analysis while blocking on the dataset of origin.
# No mitochondrial genes in the combined set, so we'll just skip it.
library(scrapper)
rna.qc.metrics <- computeRnaQcMetrics(combined, subsets=list(), num.threads=nthreads)
rna.qc.thresholds <- suggestRnaQcThresholds(rna.qc.metrics, block=block)
rna.qc.filter <- filterRnaQcMetrics(rna.qc.thresholds, rna.qc.metrics, block=block)
filtered <- combined[,rna.qc.filter,drop=FALSE]
filtered.block <- block[rna.qc.filter]
size.factors <- centerSizeFactors(rna.qc.metrics$sum[rna.qc.filter], block=filtered.block)
normalized <- normalizeCounts(filtered, size.factors)
gene.var <- modelGeneVariances(normalized, num.threads=nthreads, block=filtered.block)
top.hvgs <- chooseHighlyVariableGenes(gene.var$statistics$residuals)
pca <- runPca(normalized[top.hvgs,], num.threads=nthreads, block=filtered.block)
# Now we do a MNN correction to get rid of the batch effect.
corrected <- correctMnn(pca$components, block=filtered.block, num.threads=nthreads)
umap.out <- runUmap(corrected$corrected, num.threads=nthreads)
tsne.out <- runTsne(corrected$corrected, num.threads=nthreads)
snn.graph <- buildSnnGraph(corrected$corrected, num.threads=nthreads)
clust.out <- clusterGraph(snn.graph)
markers <- scoreMarkers(normalized, groups=clust.out$membership, block=filtered.block, num.threads=nthreads)We then check whether the datasets were suitably merged together.
plot(tsne.out[,1], tsne.out[,2], pch=16, col=factor(filtered.block))
We can also compute pseudo-bulk profiles for each cluster-dataset combination, e.g., for differential expression analyses.
aggregates <- aggregateAcrossCells(filtered, list(cluster=clust.out$membership, dataset=filtered.block))4 Handling multi-modal data
Let’s fetch a single-cell dataset with both RNA-seq and CITE-seq data. To keep the run-time short, we’ll only consider the first 5000 cells.
sce.s <- StoeckiusHashingData(mode=c("human", "adt1", "adt2"))
sce.s <- sce.s[,1:5000]
sce.s## class: SingleCellExperiment
## dim: 27679 5000
## metadata(0):
## assays(1): counts
## rownames(27679): A1BG A1BG-AS1 ... hsa-mir-8072 snoU2-30
## rowData names(0):
## colnames(5000): TGCCAAATCTCTAAGG ACTGCTCAGGTGTTAA ... CGCTATCGTCCGTCAG
## GGCGACTGTAAGGGAA
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(2): adt1 adt2We extract the counts for both the RNA and the ADTs.
rna.counts <- DelayedArray(assay(sce.s))
adt.counts <- DelayedArray(rbind(
assay(altExp(sce.s, "adt1")),
assay(altExp(sce.s, "adt2"))
))We run through the analysis workflow with both modalities.
# QC in both modalities, only keeping the cells that pass in both.
library(scrapper)
is.mito <- grepl("^MT-", rownames(rna.counts))
rna.qc.metrics <- computeRnaQcMetrics(rna.counts, subsets=list(MT=is.mito), num.threads=nthreads)
rna.qc.thresholds <- suggestRnaQcThresholds(rna.qc.metrics)
rna.qc.filter <- filterRnaQcMetrics(rna.qc.thresholds, rna.qc.metrics)
is.igg <- grepl("^IgG", rownames(adt.counts))
adt.qc.metrics <- computeAdtQcMetrics(adt.counts, subsets=list(IgG=is.igg), num.threads=nthreads)
adt.qc.thresholds <- suggestAdtQcThresholds(adt.qc.metrics)
adt.qc.filter <- filterAdtQcMetrics(adt.qc.thresholds, adt.qc.metrics)
keep <- rna.qc.filter & adt.qc.filter
rna.filtered <- rna.counts[,keep,drop=FALSE]
adt.filtered <- adt.counts[,keep,drop=FALSE]
rna.size.factors <- centerSizeFactors(rna.qc.metrics$sum[keep])
rna.normalized <- normalizeCounts(rna.filtered, rna.size.factors)
adt.size.factors <- computeClrm1Factors(adt.filtered, num.threads=nthreads)
adt.size.factors <- centerSizeFactors(adt.size.factors)
adt.normalized <- normalizeCounts(adt.filtered, adt.size.factors)
gene.var <- modelGeneVariances(rna.normalized, num.threads=nthreads)
top.hvgs <- chooseHighlyVariableGenes(gene.var$statistics$residuals)
rna.pca <- runPca(rna.normalized[top.hvgs,], num.threads=nthreads)
# Combining the RNA-derived PCs with ADT expression. Here, there's very few ADT
# tags so there's no need for further dimensionality reduction.
combined <- scaleByNeighbors(list(rna.pca$components, as.matrix(adt.normalized)), num.threads=nthreads)
umap.out <- runUmap(combined$combined, num.threads=nthreads)
tsne.out <- runTsne(combined$combined, num.threads=nthreads)
snn.graph <- buildSnnGraph(combined$combined, num.threads=nthreads)
clust.out <- clusterGraph(snn.graph)
rna.markers <- scoreMarkers(rna.normalized, groups=clust.out$membership, num.threads=nthreads)
adt.markers <- scoreMarkers(adt.normalized, groups=clust.out$membership, num.threads=nthreads)5 Other useful functions
The runAllNeighborSteps() will run runUmap(), runTsne(), buildSnnGraph() and clusterGraph() in a single call.
This runs the UMAP/t-SNE iterations and the clustering in parallel to maximize use of multiple threads.
The scoreGeneSet() function will compute a gene set score based on the input expression matrix.
This can be used to summarize the activity of pathways into a single per-cell score for visualization.
The subsampleByNeighbors() function will deterministically select a representative subset of cells based on their local neighborhood.
This can be used to reduce the compute time of the various steps downstream of the PCA.
For CRISPR data, quality control can be performed using computeCrisprQcMetrics(), suggestCrisprQcThresholds() and filterCrisprQcMetrics().
To normalize, we use size factors defined by centering the total sum of guide counts for each cell.
The resulting matrix can then be directly used in scaleByNeighbors().
Session information
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
##
## other attached packages:
## [1] scrapper_1.1.7 DelayedArray_0.33.3
## [3] SparseArray_1.7.2 S4Arrays_1.7.1
## [5] abind_1.4-8 Matrix_1.7-1
## [7] scRNAseq_2.21.0 SingleCellExperiment_1.29.1
## [9] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [11] GenomicRanges_1.59.1 GenomeInfoDb_1.43.2
## [13] IRanges_2.41.2 S4Vectors_0.45.2
## [15] BiocGenerics_0.53.3 generics_0.1.3
## [17] MatrixGenerics_1.19.0 matrixStats_1.4.1
## [19] knitr_1.49 BiocStyle_2.35.0
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9 httr2_1.0.7
## [4] rlang_1.1.4 magrittr_2.0.3 gypsum_1.3.0
## [7] compiler_4.5.0 RSQLite_2.3.9 GenomicFeatures_1.59.1
## [10] png_0.1-8 vctrs_0.6.5 ProtGenerics_1.39.1
## [13] pkgconfig_2.0.3 crayon_1.5.3 fastmap_1.2.0
## [16] magick_2.8.5 dbplyr_2.5.0 XVector_0.47.0
## [19] Rsamtools_2.23.1 rmarkdown_2.29 UCSC.utils_1.3.0
## [22] purrr_1.0.2 tinytex_0.54 bit_4.5.0.1
## [25] xfun_0.49 zlibbioc_1.53.0 cachem_1.1.0
## [28] beachmat_2.23.5 jsonlite_1.8.9 blob_1.2.4
## [31] rhdf5filters_1.19.0 Rhdf5lib_1.29.0 BiocParallel_1.41.0
## [34] parallel_4.5.0 R6_2.5.1 bslib_0.8.0
## [37] rtracklayer_1.67.0 jquerylib_0.1.4 Rcpp_1.0.13-1
## [40] bookdown_0.41 igraph_2.1.2 tidyselect_1.2.1
## [43] yaml_2.3.10 codetools_0.2-20 curl_6.0.1
## [46] lattice_0.22-6 alabaster.sce_1.7.0 tibble_3.2.1
## [49] withr_3.0.2 KEGGREST_1.47.0 evaluate_1.0.1
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## [55] Biostrings_2.75.3 pillar_1.10.0 BiocManager_1.30.25
## [58] filelock_1.0.3 RCurl_1.98-1.16 BiocVersion_3.21.1
## [61] ensembldb_2.31.0 alabaster.base_1.7.2 glue_1.8.0
## [64] alabaster.ranges_1.7.0 alabaster.matrix_1.7.4 lazyeval_0.2.2
## [67] tools_4.5.0 AnnotationHub_3.15.0 BiocIO_1.17.1
## [70] BiocNeighbors_2.1.2 GenomicAlignments_1.43.0 XML_3.99-0.17
## [73] rhdf5_2.51.1 grid_4.5.0 AnnotationDbi_1.69.0
## [76] GenomeInfoDbData_1.2.13 HDF5Array_1.35.2 restfulr_0.0.15
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