raceID on seurat data
Ehsan.Rz
2022-05-19
library(RaceID)
library(Seurat)## Attaching SeuratObjectlibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(ggplot2)pbmc <- readRDS("/mnt/nectar_volume/home/eraz0001/pbmc_human/pbmc_tutorial_final.rds")
ndata <- pbmc@assays$RNA@counts[,]
sc <- SCseq(ndata)The first step is the application of filtering for the purpose of quality control. Cells with a relatively low total number of transcripts are discarded.
sc <- filterdata(sc,mintotal=2000)In this example, we filter out cells with <2,000 transcipts. The function allows further filtering of genes by choosing the input parameters minexpr and minnumber, i.e. only genes with at least minexpr transcripts in at least minnumber cells are retained. The filtered and normalized expression matrix (normalized to the minimum total transcript count across all cells retained after filtering) can be retrieved by the getfdata function:
fdata <- getfdata(sc)sc <- compdist(sc,metric="pearson")sc <- clustexp(sc)## Clustering k = 1,2,..., K.max (= 30): ..
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## boot 50plotsaturation(sc,disp=FALSE)
plotsaturation(sc,disp=TRUE)
plotjaccard(sc)
Here, the automated criterion overestimated the cluster number leading
to instability as indicated by low Jaccard’s similarity. Based on visual
inspection of the average within-cluster dispersion as a function of the
cluster number, we manually set the cluster number to 10 without
recomputing the saturation behaviour.
sc <- clustexp(sc,cln=10,sat=FALSE)## boot 1
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## boot 50This function perform k-medoids clustering by default. K-means clustering or hierarchical clustering can be chosen with the FUNcluster argument. For very large datasets, hierarchical clustering leads to significantly smaller run time.
Subsequently, outliers in the initial k-medoids clusters are identified based on an internally computed background model for the expected gene expression variability and the assumption that transcript counts follow a negative binomial distribution defined by the mean and the variance of the expression of each gene in a cluster. Outlier genes will be in the tail of this distribution at a p-value defined by the probthr parameter (1e-3 by default), and outlier cells require the presence of a number of outlier genes defined by the outlg parameter (2 by default).
sc <- findoutliers(sc)## find outliers in cluster 1
find outliers in cluster 2
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## merging outliers 1
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## determine final clustering partition 1
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determine final clustering partition 53 The background noise model can be inspected:
plotbackground(sc)
plotsensitivity(sc)
Another way of checking the presence of outliers is the inspection of a
barplot of p-values across all cells in each cluster:
plotoutlierprobs(sc)
A heatmap of cell-to-cell distances grouped be the final clusters
inferred based on the original clusters and the outliers allows visual
inspection of the clusters:
clustheatmap(sc)
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## [51] 10 8 25sc <- compumap(sc)Parameters for umap can be given as additional argument
plotmap(sc)
sc <- comptsne(sc)
sc <- compfr(sc,knn=10)The t-SNE map can be plotted by
plotmap(sc)
The Fruchterman-Rheingold layout can be plotted by
plotmap(sc,fr=TRUE)
umap presentation
plotmap(sc,um=TRUE)
The dimensional reduction maps can be inspected, e.g., for the
localization of (a subset of) samples included in the analysis:
types <- sub("(\\_\\d+)$","", colnames(sc@ndata))
plotsymbolsmap(sc,types,fr=TRUE)
plotexpmap(sc,"CCR7",logsc=TRUE,fr=TRUE)
g <- c("S100A4", "IL7R")
plotexpmap(sc,g,n="Memory CD4+",logsc=TRUE,fr=TRUE)
For instance, to obtain differentially expressed genes within cluster 4
compared to all other cells:
d <- clustdiffgenes(sc,4,pvalue=.01)
dg <- d$dg
head(dg,25)## mean.ncl mean.cl fc pv padj
## FTH1 9.6290373 31.6791749 3.28996284 0.000000e+00 0.000000e+00
## MALAT1 34.1778602 11.6575998 0.34108630 0.000000e+00 0.000000e+00
## LYZ 4.3617687 21.0657130 4.82962628 0.000000e+00 0.000000e+00
## CST3 1.8530394 8.1435953 4.39472320 0.000000e+00 0.000000e+00
## TYROBP 1.4462505 6.7012763 4.63355152 0.000000e+00 0.000000e+00
## FTL 12.0404304 44.5472409 3.69980469 0.000000e+00 0.000000e+00
## LGALS2 0.2856779 2.8015730 9.80675286 3.000210e-113 2.880202e-111
## S100A9 2.7875662 9.3448063 3.35231726 1.342336e-96 1.127563e-94
## S100A8 1.2979021 5.0767657 3.91151676 2.534042e-76 1.892085e-74
## FCN1 0.6709750 3.2285731 4.81176373 6.592657e-69 4.430266e-67
## LGALS1 1.3402498 4.8105813 3.58931696 3.380739e-65 2.065324e-63
## SAT1 1.2152254 4.3952305 3.61680268 1.351832e-61 7.570261e-60
## S100A6 1.9930820 5.7346055 2.87725523 1.492331e-52 7.714201e-51
## LTB 2.4241102 0.1524501 0.06288908 1.647321e-51 7.907139e-50
## TYMP 0.5048875 2.2602897 4.47681872 4.300544e-48 1.926644e-46
## GSTP1 0.5807220 2.4348709 4.19283390 1.940096e-47 8.148402e-46
## HLA-DRA 2.4502621 6.3635437 2.59708697 4.146877e-47 1.639236e-45
## S100A4 3.6110289 8.4502015 2.34010909 1.380971e-46 5.155626e-45
## AIF1 1.0270824 3.3575036 3.26897201 3.613774e-43 1.278135e-41
## RPS27A 7.0126593 2.3495410 0.33504280 5.909077e-40 1.985450e-38
## HLA-DRB1 1.0089787 3.1903187 3.16192880 1.331112e-39 4.259557e-38
## CTSS 0.9612186 3.0628646 3.18643914 4.248990e-39 1.297873e-37
## COTL1 1.4458032 3.9831465 2.75497146 2.510319e-37 7.334498e-36
## CCL3 0.1648204 1.0547455 6.39936369 4.113219e-37 1.151701e-35
## MS4A6A 0.1126701 0.8670669 7.69562594 1.880493e-36 5.054764e-35types <- sub("(\\_\\d+)$","", colnames(sc@ndata))
genes <- head(rownames(dg)[dg$fc>1],10)
plotmarkergenes(sc,genes,samples=types)
plotmarkergenes(sc,genes,cl=c(2,6,7,8,10),samples=types,order.cells=TRUE)
fractDotPlot(sc, genes, cluster=c(2,6,7,8,10), zsc=TRUE)
A <- names(sc@cpart)[sc@cpart %in% c(2,4)]
B <- names(sc@cpart)[sc@cpart %in% c(3)]
x <- diffexpnb(getfdata(sc,n=c(A,B)), A=A, B=B )
plotdiffgenesnb(x,pthr=.05,lthr=.5,mthr=-1,Aname="Cl.2",Bname="Cl.3,5",show_names=TRUE,padj=TRUE)
ltr <- Ltree(sc)
ltr <- compentropy(ltr)
ltr <- projcells(ltr,cthr=5,nmode=FALSE,fr=TRUE)
ltr <- projback(ltr,pdishuf=100)## pdishuffle: 1
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Building tree: done.ltr <- comppvalue(ltr,pthr=0.1)plotgraph(ltr,scthr=0.2,showCells=FALSE,showMap=TRUE)
To predict the stem cell, the StemID score can be computed and
visualized:
x <- compscore(ltr,scthr=0.2)
plotdistanceratio(ltr)
plotspantree(ltr)
The cell projections onto the links can be directly compared with this
minimal spanning tree:
plotspantree(ltr,projections=TRUE)
plotlinkscore(ltr)
projenrichment(ltr)
x <- getproj(ltr,i=3)
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/lib/libRblas.so
## LAPACK: /mnt/nectar_volume/software/apps/R/4.0.2/lib/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
## [5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
## [7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.6 dplyr_1.0.8 SeuratObject_4.0.4 Seurat_4.1.0
## [5] RaceID_0.2.3
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 igraph_1.2.11 lazyeval_0.2.2
## [4] splines_4.0.2 listenv_0.8.0 scattermore_0.7
## [7] lle_1.1 digest_0.6.29 htmltools_0.5.2
## [10] fansi_1.0.2 magrittr_2.0.2 tensor_1.5
## [13] cluster_2.1.3 ROCR_1.0-11 globals_0.14.0
## [16] matrixStats_0.61.0 askpass_1.1 spatstat.sparse_2.1-0
## [19] princurve_2.1.6 colorspace_2.0-2 ggrepel_0.9.1
## [22] xfun_0.30 crayon_1.4.2 jsonlite_1.7.3
## [25] spatstat.data_2.1-2 survival_3.1-12 zoo_1.8-9
## [28] glue_1.6.1 polyclip_1.10-0 gtable_0.3.0
## [31] leiden_0.3.9 coop_0.6-3 kernlab_0.9-29
## [34] future.apply_1.8.1 DEoptimR_1.0-11 prabclus_2.3-2
## [37] abind_1.4-5 scales_1.1.1 pheatmap_1.0.12
## [40] DBI_1.1.2 som_0.3-5.1 miniUI_0.1.1.1
## [43] Rcpp_1.0.8 viridisLite_0.4.0 xtable_1.8-4
## [46] reticulate_1.24 spatstat.core_2.3-2 mclust_5.4.9
## [49] stats4_4.0.2 umap_0.2.7.0 propr_4.2.6
## [52] htmlwidgets_1.5.4 httr_1.4.2 FNN_1.1.3
## [55] RColorBrewer_1.1-2 fpc_2.2-9 modeltools_0.2-23
## [58] ellipsis_0.3.2 ica_1.0-2 farver_2.1.0
## [61] flexmix_2.3-17 pkgconfig_2.0.3 nnet_7.3-14
## [64] sass_0.4.0 uwot_0.1.11 deldir_1.0-6
## [67] locfit_1.5-9.4 utf8_1.2.2 labeling_0.4.2
## [70] tidyselect_1.1.2 rlang_1.0.2 reshape2_1.4.4
## [73] later_1.3.0 munsell_0.5.0 tools_4.0.2
## [76] cli_3.2.0 generics_0.1.2 ggridges_0.5.3
## [79] evaluate_0.15 stringr_1.4.0 fastmap_1.1.0
## [82] yaml_2.2.2 goftest_1.2-3 knitr_1.39
## [85] fitdistrplus_1.1-6 robustbase_0.95-0 purrr_0.3.4
## [88] randomForest_4.6-14 RANN_2.6.1 pbapply_1.5-0
## [91] future_1.23.0 nlme_3.1-148 mime_0.12
## [94] compiler_4.0.2 rstudioapi_0.13 plotly_4.10.0
## [97] png_0.1-7 spatstat.utils_2.3-0 tibble_3.1.6
## [100] bslib_0.3.1 stringi_1.7.6 highr_0.9
## [103] RSpectra_0.16-0 lattice_0.20-41 Matrix_1.4-0
## [106] vegan_2.6-2 permute_0.9-7 FateID_0.2.1
## [109] vctrs_0.3.8 pillar_1.7.0 lifecycle_1.0.1
## [112] spatstat.geom_2.3-1 lmtest_0.9-39 jquerylib_0.1.4
## [115] RcppAnnoy_0.0.19 data.table_1.14.2 cowplot_1.1.1
## [118] irlba_2.3.5 httpuv_1.6.5 patchwork_1.1.1
## [121] R6_2.5.1 promises_1.2.0.1 KernSmooth_2.23-17
## [124] gridExtra_2.3 parallelly_1.30.0 codetools_0.2-16
## [127] MASS_7.3-51.6 assertthat_0.2.1 openssl_1.4.6
## [130] withr_2.4.3 sctransform_0.3.3 diptest_0.76-0
## [133] mgcv_1.8-31 parallel_4.0.2 quadprog_1.5-8
## [136] grid_4.0.2 rpart_4.1-15 class_7.3-17
## [139] tidyr_1.2.0 rmarkdown_2.14 Rtsne_0.15
## [142] scatterplot3d_0.3-41 shiny_1.7.1 snowfall_1.84-6.1