Cell cycle scoring
Katharina Hembach
6/18/2020
Last updated: 2020-06-26
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
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| Rmd | 0983178 | khembach | 2020-06-26 | Fix tabset |
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| Rmd | f349423 | khembach | 2020-06-21 | regress out number of UMIs and perc mitochondrial features; cyclone |
Load packages
library(cowplot)
library(ggplot2)
library(RColorBrewer)
library(viridis)
library(scran)
library(Seurat)
library(SingleCellExperiment)
library(stringr)
library(RCurl)
library(BiocParallel)
library(dplyr)Load data & convert to SCE
so <- readRDS(file.path("output", "so_04_clustering.rds"))
sce <- as.SingleCellExperiment(so, assay = "RNA")
colData(sce) <- as.data.frame(colData(sce)) %>%
mutate_if(is.character, as.factor) %>%
DataFrame(row.names = colnames(sce))
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
sce$cluster_id <- Idents(so)Cell cycle scoring with Seurat
We assign each cell a cell cycle scores and visualize them in the DR plots. We use known G2/M and S phase markers that come with the Seurat package. The markers are anticorrelated and cells that to not express the markers should be in G1 phase.
We compute cell cycle phase:
DefaultAssay(so) <- "RNA"
# A list of cell cycle markers, from Tirosh et al, 2015
cc_file <- getURL("https://raw.githubusercontent.com/hbc/tinyatlas/master/cell_cycle/Homo_sapiens.csv")
cc_genes <- read.csv(text = cc_file)
# match the marker genes to the features
m <- match(cc_genes$geneID[cc_genes$phase == "S"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
s_genes <- rownames(GetAssayData(so))[m]
(s_genes <- s_genes[!is.na(s_genes)]) [1] "ENSG00000012963.UBR7" "ENSG00000049541.RFC2"
[3] "ENSG00000051180.RAD51" "ENSG00000073111.MCM2"
[5] "ENSG00000075131.TIPIN" "ENSG00000076003.MCM6"
[7] "ENSG00000076248.UNG" "ENSG00000077514.POLD3"
[9] "ENSG00000092470.WDR76" "ENSG00000092853.CLSPN"
[11] "ENSG00000093009.CDC45" "ENSG00000094804.CDC6"
[13] "ENSG00000095002.MSH2" "ENSG00000100297.MCM5"
[15] "ENSG00000101868.POLA1" "ENSG00000104738.MCM4"
[17] "ENSG00000111247.RAD51AP1" "ENSG00000112312.GMNN"
[19] "ENSG00000117748.RPA2" "ENSG00000118412.CASP8AP2"
[21] "ENSG00000119969.HELLS" "ENSG00000129173.E2F8"
[23] "ENSG00000131153.GINS2" "ENSG00000132646.PCNA"
[25] "ENSG00000132780.NASP" "ENSG00000136492.BRIP1"
[27] "ENSG00000136982.DSCC1" "ENSG00000143476.DTL"
[29] "ENSG00000144354.CDCA7" "ENSG00000151725.CENPU"
[31] "ENSG00000156802.ATAD2" "ENSG00000159259.CHAF1B"
[33] "ENSG00000162607.USP1" "ENSG00000163950.SLBP"
[35] "ENSG00000167325.RRM1" "ENSG00000168496.FEN1"
[37] "ENSG00000171848.RRM2" "ENSG00000174371.EXO1"
[39] "ENSG00000175305.CCNE2" "ENSG00000176890.TYMS"
[41] "ENSG00000197299.BLM" "ENSG00000198056.PRIM1"
[43] "ENSG00000276043.UHRF1" m <- match(cc_genes$geneID[cc_genes$phase == "G2/M"],
str_split(rownames(GetAssayData(so)),
pattern = "\\.", simplify = TRUE)[,1])
g2m_genes <- rownames(GetAssayData(so))[m]
(g2m_genes <- g2m_genes[!is.na(g2m_genes)]) [1] "ENSG00000010292.NCAPD2" "ENSG00000011426.ANLN"
[3] "ENSG00000013810.TACC3" "ENSG00000072571.HMMR"
[5] "ENSG00000075218.GTSE1" "ENSG00000080986.NDC80"
[7] "ENSG00000087586.AURKA" "ENSG00000088325.TPX2"
[9] "ENSG00000089685.BIRC5" "ENSG00000092140.G2E3"
[11] "ENSG00000094916.CBX5" "ENSG00000100401.RANGAP1"
[13] "ENSG00000102974.CTCF" "ENSG00000111665.CDCA3"
[15] "ENSG00000112742.TTK" "ENSG00000113810.SMC4"
[17] "ENSG00000114346.ECT2" "ENSG00000115163.CENPA"
[19] "ENSG00000117399.CDC20" "ENSG00000117650.NEK2"
[21] "ENSG00000117724.CENPF" "ENSG00000120802.TMPO"
[23] "ENSG00000123485.HJURP" "ENSG00000123975.CKS2"
[25] "ENSG00000126787.DLGAP5" "ENSG00000129195.PIMREG"
[27] "ENSG00000131747.TOP2A" "ENSG00000134222.PSRC1"
[29] "ENSG00000134690.CDCA8" "ENSG00000136108.CKAP2"
[31] "ENSG00000137804.NUSAP1" "ENSG00000137807.KIF23"
[33] "ENSG00000138160.KIF11" "ENSG00000138182.KIF20B"
[35] "ENSG00000138778.CENPE" "ENSG00000139354.GAS2L3"
[37] "ENSG00000142945.KIF2C" "ENSG00000143228.NUF2"
[39] "ENSG00000143401.ANP32E" "ENSG00000143815.LBR"
[41] "ENSG00000148773.MKI67" "ENSG00000157456.CCNB2"
[43] "ENSG00000158402.CDC25C" "ENSG00000164104.HMGB2"
[45] "ENSG00000169607.CKAP2L" "ENSG00000169679.BUB1"
[47] "ENSG00000170312.CDK1" "ENSG00000173207.CKS1B"
[49] "ENSG00000175063.UBE2C" "ENSG00000175216.CKAP5"
[51] "ENSG00000178999.AURKB" "ENSG00000184661.CDCA2"
[53] "ENSG00000188229.TUBB4B" "ENSG00000189159.JPT1" so <- CellCycleScoring(so, s.features = s_genes, g2m.features = g2m_genes,
set.ident = TRUE)
DefaultAssay(so) <- "integrated"Cell cycle assignment using cyclone
## read pretrained set of human cell cycle markers
human_pairs <- readRDS(system.file("exdata", "human_cycle_markers.rds",
package="scran"))
# Using Ensembl IDs to match up with the annotation in 'mm.pairs'.
assignments <- cyclone(sce, human_pairs,
gene.names = str_split(rownames(sce), pattern = "\\.",
simplify = TRUE)[,1],
BPPARAM = MulticoreParam(workers = 20),
verbose = TRUE)
table(assignments$phases, colData(sce)$cluster_id)
0 1 2 3 4 5 6 7 8 9 10 11 12 13
G1 7717 1745 678 2019 2415 1247 1232 1214 1684 1039 957 950 747 472
G2M 637 241 1581 136 26 149 89 92 30 99 101 37 5 33
S 2840 1952 597 608 167 1085 1146 1026 234 732 282 341 424 471
14 15 16
G1 601 401 197
G2M 15 14 27
S 299 73 93## Add cell cycle phases to Seurat object
so$cyclone_phase <- assignments$phasesColored DR
cs <- sample(colnames(so), 5e3)
.plot_dr <- function(so, dr, id)
DimPlot(so, cells = cs, group.by = id, reduction = dr, pt.size = 0.4) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
ids <- c("cluster_id", "group_id", "sample_id", "Phase", "cyclone_phase")
for (id in ids) {
cat("## ", id, "\n")
p1 <- .plot_dr(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none")
p2 <- .plot_dr(so, "umap", id) + theme(legend.position = "none")
ps <- plot_grid(plotlist = list(p1, p2), nrow = 1)
p <- plot_grid(ps, lgd, nrow = 1, rel_widths = c(1, 0.2))
print(p)
cat("\n\n")
}
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_04_1_cell_cycle.rds"))
sessionInfo()R version 4.0.0 (2020-04-24)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.6 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.0/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.0/lib/libRlapack.so
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
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_0.8.5 BiocParallel_1.22.0
[3] RCurl_1.98-1.2 stringr_1.4.0
[5] Seurat_3.1.5 scran_1.16.0
[7] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[9] DelayedArray_0.14.0 matrixStats_0.56.0
[11] Biobase_2.48.0 GenomicRanges_1.40.0
[13] GenomeInfoDb_1.24.0 IRanges_2.22.2
[15] S4Vectors_0.26.1 BiocGenerics_0.34.0
[17] viridis_0.5.1 viridisLite_0.3.0
[19] RColorBrewer_1.1-2 ggplot2_3.3.0
[21] cowplot_1.0.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 ggbeeswarm_0.6.0
[3] colorspace_1.4-1 ellipsis_0.3.1
[5] ggridges_0.5.2 rprojroot_1.3-2
[7] XVector_0.28.0 BiocNeighbors_1.6.0
[9] fs_1.4.1 farver_2.0.3
[11] leiden_0.3.3 listenv_0.8.0
[13] ggrepel_0.8.2 codetools_0.2-16
[15] splines_4.0.0 knitr_1.28
[17] scater_1.16.0 jsonlite_1.6.1
[19] ica_1.0-2 cluster_2.1.0
[21] png_0.1-7 uwot_0.1.8
[23] sctransform_0.2.1 compiler_4.0.0
[25] httr_1.4.1 dqrng_0.2.1
[27] backports_1.1.7 lazyeval_0.2.2
[29] assertthat_0.2.1 Matrix_1.2-18
[31] limma_3.44.1 later_1.0.0
[33] BiocSingular_1.4.0 htmltools_0.4.0
[35] tools_4.0.0 rsvd_1.0.3
[37] igraph_1.2.5 gtable_0.3.0
[39] glue_1.4.1 GenomeInfoDbData_1.2.3
[41] reshape2_1.4.4 RANN_2.6.1
[43] rappdirs_0.3.1 Rcpp_1.0.4.6
[45] vctrs_0.3.0 ape_5.3
[47] nlme_3.1-148 DelayedMatrixStats_1.10.0
[49] lmtest_0.9-37 xfun_0.14
[51] globals_0.12.5 lifecycle_0.2.0
[53] irlba_2.3.3 statmod_1.4.34
[55] future_1.17.0 edgeR_3.30.0
[57] zlibbioc_1.34.0 MASS_7.3-51.6
[59] zoo_1.8-8 scales_1.1.1
[61] promises_1.1.0 yaml_2.2.1
[63] reticulate_1.16 pbapply_1.4-2
[65] gridExtra_2.3 stringi_1.4.6
[67] rlang_0.4.6 pkgconfig_2.0.3
[69] bitops_1.0-6 evaluate_0.14
[71] lattice_0.20-41 ROCR_1.0-11
[73] purrr_0.3.4 labeling_0.3
[75] htmlwidgets_1.5.1 patchwork_1.0.0
[77] tidyselect_1.1.0 RcppAnnoy_0.0.16
[79] plyr_1.8.6 magrittr_1.5
[81] R6_2.4.1 pillar_1.4.4
[83] whisker_0.4 withr_2.2.0
[85] fitdistrplus_1.1-1 survival_3.1-12
[87] tsne_0.1-3 tibble_3.0.1
[89] future.apply_1.5.0 crayon_1.3.4
[91] KernSmooth_2.23-17 plotly_4.9.2.1
[93] rmarkdown_2.1 locfit_1.5-9.4
[95] grid_4.0.0 data.table_1.12.8
[97] git2r_0.27.1 digest_0.6.25
[99] tidyr_1.1.0 httpuv_1.5.2
[101] munsell_0.5.0 beeswarm_0.2.3
[103] vipor_0.4.5