Trajectory Analysis
2025-02-24
Last updated: 2025-02-24
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Knit directory: muse/
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| Rmd | 4a2d928 | Dave Tang | 2025-02-23 | Trajectory analysis |
Installation
Install dependencies.
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scRNAseq")
BiocManager::install("AnnotationHub")
BiocManager::install("scater")Data
Mouse haematopoietic stem cell (HSC) dataset generated with Smart-seq2 (Nestorowa et al. 2016).
suppressPackageStartupMessages(library(scRNAseq))
sce.nest <- NestorowaHSCData()loading from cacherequire("ensembldb")suppressPackageStartupMessages(library(AnnotationHub))
ens.mm.v97 <- AnnotationHub()[["AH73905"]]loading from cacheanno <- select(ens.mm.v97, keys=rownames(sce.nest),
keytype="GENEID", columns=c("SYMBOL", "SEQNAME"))
rowData(sce.nest) <- anno[match(rownames(sce.nest), anno$GENEID),]
sce.nestclass: SingleCellExperiment
dim: 46078 1920
metadata(0):
assays(1): counts
rownames(46078): ENSMUSG00000000001 ENSMUSG00000000003 ...
ENSMUSG00000107391 ENSMUSG00000107392
rowData names(3): GENEID SYMBOL SEQNAME
colnames(1920): HSPC_007 HSPC_013 ... Prog_852 Prog_810
colData names(9): gate broad ... projected metrics
reducedDimNames(1): diffusion
mainExpName: endogenous
altExpNames(2): ERCC FACSQuality control
unfiltered <- sce.nestFor some reason, no mitochondrial transcripts are available, so we will perform quality control using the spike-in proportions only.
suppressPackageStartupMessages(library(scater))
stats <- perCellQCMetrics(sce.nest)
qc <- quickPerCellQC(stats, percent_subsets="altexps_ERCC_percent")
sce.nest <- sce.nest[,!qc$discard]We examine the number of cells discarded for each reason.
colSums(as.matrix(qc)) low_lib_size low_n_features high_altexps_ERCC_percent
146 28 241
discard
264 We create some diagnostic plots for each metric.
colData(unfiltered) <- cbind(colData(unfiltered), stats)
unfiltered$discard <- qc$discard
gridExtra::grid.arrange(
plotColData(unfiltered, y="sum", colour_by="discard") +
scale_y_log10() + ggtitle("Total count"),
plotColData(unfiltered, y="detected", colour_by="discard") +
scale_y_log10() + ggtitle("Detected features"),
plotColData(unfiltered, y="altexps_ERCC_percent",
colour_by="discard") + ggtitle("ERCC percent"),
ncol=2
)
Normalisation
suppressPackageStartupMessages(library(scran))
set.seed(101000110)
clusters <- quickCluster(sce.nest)
sce.nest <- computeSumFactors(sce.nest, clusters=clusters)
sce.nest <- logNormCounts(sce.nest)We examine some key metrics for the distribution of size factors, and compare it to the library sizes as a sanity check.
summary(sizeFactors(sce.nest)) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.03876 0.42023 0.74297 1.00000 1.24900 16.78896 plot(librarySizeFactors(sce.nest), sizeFactors(sce.nest), pch=16,
xlab="Library size factors", ylab="Deconvolution factors", log="xy")
Variance modelling
We use the spike-in transcripts to model the technical noise as a function of the mean.
set.seed(00010101)
dec.nest <- modelGeneVarWithSpikes(sce.nest, "ERCC")
top.nest <- getTopHVGs(dec.nest, prop=0.1)Per-gene variance as a function of the mean for the log-expression values in the Nestorowa HSC dataset. Each point represents a gene (black) with the mean-variance trend (blue) fitted to the spike-ins (red).
plot(dec.nest$mean, dec.nest$total, pch=16, cex=0.5,
xlab="Mean of log-expression", ylab="Variance of log-expression")
curfit <- metadata(dec.nest)
curve(curfit$trend(x), col='dodgerblue', add=TRUE, lwd=2)
points(curfit$mean, curfit$var, col="red")
Dimensionality reduction
set.seed(101010011)
sce.nest <- denoisePCA(sce.nest, technical=dec.nest, subset.row=top.nest)
sce.nest <- runTSNE(sce.nest, dimred="PCA")We check that the number of retained PCs is sensible.
ncol(reducedDim(sce.nest, "PCA"))[1] 9Clustering
snn.gr <- buildSNNGraph(sce.nest, use.dimred="PCA")
colLabels(sce.nest) <- factor(igraph::cluster_walktrap(snn.gr)$membership)
table(colLabels(sce.nest))
1 2 3 4 5 6 7 8 9 10
198 319 208 147 221 182 21 209 74 77 plotTSNE(sce.nest, colour_by="label")
Marker gene detection
markers <- findMarkers(sce.nest, colLabels(sce.nest),
test.type="wilcox", direction="up", lfc=0.5,
row.data=rowData(sce.nest)[,"SYMBOL",drop=FALSE])To illustrate the manual annotation process, we examine the marker genes for one of the clusters. Upregulation of Car2, Hebp1 amd hemoglobins indicates that cluster 8 contains erythroid precursors.
chosen <- markers[['8']]
best <- chosen[chosen$Top <= 10,]
aucs <- getMarkerEffects(best, prefix="AUC")
rownames(aucs) <- best$SYMBOL
suppressPackageStartupMessages(library(pheatmap))
pheatmap(aucs, color=viridis::plasma(100))
sessionInfo()R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[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: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] pheatmap_1.0.12 scran_1.34.0
[3] scater_1.34.0 ggplot2_3.5.1
[5] scuttle_1.16.0 AnnotationHub_3.14.0
[7] BiocFileCache_2.14.0 dbplyr_2.5.0
[9] ensembldb_2.30.0 AnnotationFilter_1.30.0
[11] GenomicFeatures_1.58.0 AnnotationDbi_1.68.0
[13] scRNAseq_2.20.0 SingleCellExperiment_1.28.1
[15] SummarizedExperiment_1.36.0 Biobase_2.66.0
[17] GenomicRanges_1.58.0 GenomeInfoDb_1.42.3
[19] IRanges_2.40.1 S4Vectors_0.44.0
[21] BiocGenerics_0.52.0 MatrixGenerics_1.18.1
[23] matrixStats_1.4.1 workflowr_1.7.1
loaded via a namespace (and not attached):
[1] later_1.3.2 BiocIO_1.16.0 bitops_1.0-9
[4] filelock_1.0.3 tibble_3.2.1 XML_3.99-0.17
[7] lifecycle_1.0.4 httr2_1.0.5 edgeR_4.4.2
[10] rprojroot_2.0.4 processx_3.8.4 lattice_0.22-6
[13] alabaster.base_1.6.1 magrittr_2.0.3 limma_3.62.2
[16] sass_0.4.9 rmarkdown_2.28 jquerylib_0.1.4
[19] yaml_2.3.10 metapod_1.14.0 httpuv_1.6.15
[22] RColorBrewer_1.1-3 cowplot_1.1.3 DBI_1.2.3
[25] abind_1.4-8 zlibbioc_1.52.0 Rtsne_0.17
[28] purrr_1.0.2 RCurl_1.98-1.16 rappdirs_0.3.3
[31] git2r_0.35.0 GenomeInfoDbData_1.2.13 ggrepel_0.9.6
[34] irlba_2.3.5.1 alabaster.sce_1.6.0 dqrng_0.4.1
[37] codetools_0.2-20 DelayedArray_0.32.0 tidyselect_1.2.1
[40] UCSC.utils_1.2.0 farver_2.1.2 ScaledMatrix_1.14.0
[43] viridis_0.6.5 GenomicAlignments_1.42.0 jsonlite_1.8.9
[46] BiocNeighbors_2.0.1 tools_4.4.1 Rcpp_1.0.13
[49] glue_1.8.0 gridExtra_2.3 SparseArray_1.6.1
[52] xfun_0.48 dplyr_1.1.4 HDF5Array_1.34.0
[55] gypsum_1.2.0 withr_3.0.2 BiocManager_1.30.25
[58] fastmap_1.2.0 rhdf5filters_1.18.0 bluster_1.16.0
[61] fansi_1.0.6 callr_3.7.6 digest_0.6.37
[64] rsvd_1.0.5 R6_2.5.1 mime_0.12
[67] colorspace_2.1-1 RSQLite_2.3.7 utf8_1.2.4
[70] generics_0.1.3 rtracklayer_1.66.0 httr_1.4.7
[73] S4Arrays_1.6.0 whisker_0.4.1 pkgconfig_2.0.3
[76] gtable_0.3.6 blob_1.2.4 XVector_0.46.0
[79] htmltools_0.5.8.1 ProtGenerics_1.38.0 scales_1.3.0
[82] alabaster.matrix_1.6.1 png_0.1-8 knitr_1.48
[85] rstudioapi_0.17.1 rjson_0.2.23 curl_5.2.3
[88] cachem_1.1.0 rhdf5_2.50.2 stringr_1.5.1
[91] BiocVersion_3.20.0 parallel_4.4.1 vipor_0.4.7
[94] restfulr_0.0.15 pillar_1.9.0 grid_4.4.1
[97] alabaster.schemas_1.6.0 vctrs_0.6.5 promises_1.3.0
[100] BiocSingular_1.22.0 beachmat_2.22.0 cluster_2.1.6
[103] beeswarm_0.4.0 evaluate_1.0.1 cli_3.6.3
[106] locfit_1.5-9.10 compiler_4.4.1 Rsamtools_2.22.0
[109] rlang_1.1.4 crayon_1.5.3 labeling_0.4.3
[112] ps_1.8.1 getPass_0.2-4 fs_1.6.4
[115] ggbeeswarm_0.7.2 stringi_1.8.4 alabaster.se_1.6.0
[118] viridisLite_0.4.2 BiocParallel_1.40.0 munsell_0.5.1
[121] Biostrings_2.74.1 lazyeval_0.2.2 Matrix_1.7-0
[124] ExperimentHub_2.14.0 bit64_4.5.2 Rhdf5lib_1.28.0
[127] KEGGREST_1.46.0 statmod_1.5.0 alabaster.ranges_1.6.0
[130] highr_0.11 igraph_2.1.1 memoise_2.0.1
[133] bslib_0.8.0 bit_4.5.0