Cluster annotation
Katharina Hembach
5/29/2020
Last updated: 2020-05-29
Checks: 7 0
Knit directory: neural_scRNAseq/
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| Rmd | 97d5a52 | khembach | 2020-05-29 | cluster analysis |
Load packages
library(ComplexHeatmap)
library(cowplot)
library(ggplot2)
library(dplyr)
library(muscat)
library(purrr)
library(RColorBrewer)
library(viridis)
library(scran)
library(Seurat)
library(SingleCellExperiment)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))Nb. of clusters by resolution
cluster_cols <- grep("res.[0-9]", colnames(colData(sce)), value = TRUE)
sapply(colData(sce)[cluster_cols], nlevels)integrated_snn_res.0.1 integrated_snn_res.0.2 integrated_snn_res.0.4
9 12 18
integrated_snn_res.0.8 integrated_snn_res.1 integrated_snn_res.1.2
25 31 35
integrated_snn_res.2
41 Cluster-sample counts
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
sce$cluster_id <- Idents(so)
(n_cells <- table(sce$cluster_id, sce$sample_id))
1NSC 2NSC 3NC52 4NC52 5NC96 6NC96
0 6099 6218 97 68 906 104
1 10 18 29 20 10244 117
2 11 7 1827 1260 357 561
3 55 55 1048 851 457 844
4 0 0 1361 904 212 336
5 721 700 292 226 466 332
6 174 174 629 606 749 372
7 0 0 1154 866 219 451
8 1 0 906 638 493 550
9 6 5 716 501 126 222
10 0 0 685 467 154 227
11 451 475 95 95 124 96
12 595 535 53 45 18 9
13 161 146 319 266 138 171
14 0 0 359 325 80 169
15 0 0 270 222 143 174
16 68 79 48 43 271 20
17 2 4 36 43 15 37Relative cluster-abundances
fqs <- prop.table(n_cells, margin = 2)
mat <- as.matrix(unclass(fqs))
Heatmap(mat,
col = rev(brewer.pal(11, "RdGy")[-6]),
name = "Frequency",
cluster_rows = FALSE,
cluster_columns = FALSE,
row_names_side = "left",
row_title = "cluster_id",
column_title = "sample_id",
column_title_side = "bottom",
rect_gp = gpar(col = "white"),
cell_fun = function(i, j, x, y, width, height, fill)
grid.text(round(mat[j, i] * 100, 2), x = x, y = y,
gp = gpar(col = "white", fontsize = 8)))
DR colored by cluster ID
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")
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")
}cluster_id
group_id
sample_id
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 grid stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] Seurat_3.1.5 scran_1.16.0
[3] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[5] DelayedArray_0.14.0 matrixStats_0.56.0
[7] Biobase_2.48.0 GenomicRanges_1.40.0
[9] GenomeInfoDb_1.24.0 IRanges_2.22.2
[11] S4Vectors_0.26.1 BiocGenerics_0.34.0
[13] viridis_0.5.1 viridisLite_0.3.0
[15] RColorBrewer_1.1-2 purrr_0.3.4
[17] muscat_1.2.0 dplyr_0.8.5
[19] ggplot2_3.3.0 cowplot_1.0.0
[21] ComplexHeatmap_2.4.2 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.7 circlize_0.4.9
[3] blme_1.0-4 igraph_1.2.5
[5] plyr_1.8.6 lazyeval_0.2.2
[7] TMB_1.7.16 splines_4.0.0
[9] BiocParallel_1.22.0 listenv_0.8.0
[11] scater_1.16.0 digest_0.6.25
[13] foreach_1.5.0 htmltools_0.4.0
[15] gdata_2.18.0 lmerTest_3.1-2
[17] magrittr_1.5 memoise_1.1.0
[19] cluster_2.1.0 doParallel_1.0.15
[21] ROCR_1.0-11 limma_3.44.1
[23] globals_0.12.5 annotate_1.66.0
[25] prettyunits_1.1.1 colorspace_1.4-1
[27] rappdirs_0.3.1 ggrepel_0.8.2
[29] blob_1.2.1 xfun_0.14
[31] jsonlite_1.6.1 crayon_1.3.4
[33] RCurl_1.98-1.2 genefilter_1.70.0
[35] lme4_1.1-23 zoo_1.8-8
[37] ape_5.3 survival_3.1-12
[39] iterators_1.0.12 glue_1.4.1
[41] gtable_0.3.0 zlibbioc_1.34.0
[43] XVector_0.28.0 leiden_0.3.3
[45] GetoptLong_0.1.8 BiocSingular_1.4.0
[47] future.apply_1.5.0 shape_1.4.4
[49] scales_1.1.1 DBI_1.1.0
[51] edgeR_3.30.0 Rcpp_1.0.4.6
[53] xtable_1.8-4 progress_1.2.2
[55] clue_0.3-57 reticulate_1.16
[57] dqrng_0.2.1 bit_1.1-15.2
[59] rsvd_1.0.3 tsne_0.1-3
[61] htmlwidgets_1.5.1 httr_1.4.1
[63] gplots_3.0.3 ellipsis_0.3.1
[65] ica_1.0-2 farver_2.0.3
[67] pkgconfig_2.0.3 XML_3.99-0.3
[69] uwot_0.1.8 locfit_1.5-9.4
[71] labeling_0.3 tidyselect_1.1.0
[73] rlang_0.4.6 reshape2_1.4.4
[75] later_1.0.0 AnnotationDbi_1.50.0
[77] munsell_0.5.0 tools_4.0.0
[79] RSQLite_2.2.0 ggridges_0.5.2
[81] evaluate_0.14 stringr_1.4.0
[83] yaml_2.2.1 knitr_1.28
[85] bit64_0.9-7 fs_1.4.1
[87] fitdistrplus_1.1-1 caTools_1.18.0
[89] RANN_2.6.1 pbapply_1.4-2
[91] future_1.17.0 nlme_3.1-148
[93] whisker_0.4 pbkrtest_0.4-8.6
[95] compiler_4.0.0 plotly_4.9.2.1
[97] beeswarm_0.2.3 png_0.1-7
[99] variancePartition_1.18.0 tibble_3.0.1
[101] statmod_1.4.34 geneplotter_1.66.0
[103] stringi_1.4.6 lattice_0.20-41
[105] Matrix_1.2-18 nloptr_1.2.2.1
[107] vctrs_0.3.0 pillar_1.4.4
[109] lifecycle_0.2.0 lmtest_0.9-37
[111] GlobalOptions_0.1.1 RcppAnnoy_0.0.16
[113] BiocNeighbors_1.6.0 data.table_1.12.8
[115] bitops_1.0-6 irlba_2.3.3
[117] patchwork_1.0.0 httpuv_1.5.2
[119] colorRamps_2.3 R6_2.4.1
[121] promises_1.1.0 KernSmooth_2.23-17
[123] gridExtra_2.3 vipor_0.4.5
[125] codetools_0.2-16 boot_1.3-25
[127] MASS_7.3-51.6 gtools_3.8.2
[129] assertthat_0.2.1 DESeq2_1.28.1
[131] rprojroot_1.3-2 rjson_0.2.20
[133] withr_2.2.0 sctransform_0.2.1
[135] GenomeInfoDbData_1.2.3 hms_0.5.3
[137] tidyr_1.1.0 glmmTMB_1.0.1
[139] minqa_1.2.4 rmarkdown_2.1
[141] DelayedMatrixStats_1.10.0 Rtsne_0.15
[143] git2r_0.27.1 numDeriv_2016.8-1.1
[145] ggbeeswarm_0.6.0