Integrate Kanton et al. by groups
Katharina Hembach
8/26/2020
Last updated: 2020-09-02
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | a44bfa7 | khembach | 2020-09-02 | organoid integration using group label |
Load packages
library(BiocParallel)
library(ggplot2)
library(dplyr)
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(future)# increase future's maximum allowed size of exported globals
# the default is 2GB
options(future.globals.maxSize = 11000 * 1024 ^ 2)
# change the current plan to access parallelization
plan("multiprocess", workers = 20)Load data & convert to SCE
so_nsc <- readRDS(file.path("output", "so_04_clustering.rds"))
DefaultAssay(so_nsc) <- "RNA"so_org <- readRDS(file.path("output", "so_organoid-01-clustering.rds"))
DefaultAssay(so_org) <- "RNA"Integration
We integrate the two datasets using our three groups and the two cell lines from Kanton et al.
## make sure column names match between datasets:
## we will use sample_id, group_id, nUMI, fractionMt, nGene
so_nsc$nUMI <- so_nsc$sum
so_nsc$fractionMt <- so_nsc$subsets_Mt_fraction
so_nsc$nGene <- so_nsc$detected
so_nsc$sum <- NULL
so_nsc$subsets_Mt_fraction <- NULL
so_nsc$detected <- NULL
so_org$sample_id <- so_org$Line
so_org$group_id <- so_org$Stage
so_org$fractionMt <- so_org$PercentMito
so_org$Line <- NULL
so_org$Stage <- NULL
so_org$PercentMito <- NULL
so_nsc$integration_group <- so_nsc$group_id
so_org$integration_group <- so_org$sample_id
# split our cells by group
cells_by_sample <- split(colnames(so_nsc), so_nsc$integration_group)
so_nsc <- lapply(cells_by_sample, function(i) subset(so_nsc, cells = i))
# split organoid cells by cell Line
cells_by_sample <- split(colnames(so_org), so_org$sample_id)
so_org <- lapply(cells_by_sample, function(i) subset(so_org, cells = i))
## we combine the two lists
so <- c(so_nsc, so_org)
## Identify the top 2000 genes with high cell-to-cell variation
so <- lapply(so, FindVariableFeatures, nfeatures = 2000,
selection.method = "vst", verbose = FALSE)## find anchors & integrate
as <- FindIntegrationAnchors(so, verbose = FALSE)
so <- IntegrateData(anchorset = as, dims = seq_len(30), verbose = FALSE)DefaultAssay(so) <- "integrated"
## We scale the data
so <- ScaleData(so, verbose = FALSE,
vars.to.regress = c("nUMI", "fractionMt"))so <- RunPCA(so, npcs = 30, verbose = FALSE)
so <- RunTSNE(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, do.fast = TRUE, verbose = FALSE)
so <- RunUMAP(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, verbose = FALSE)
## PCA plot
DimPlot(so, reduction = "pca", group.by = "sample_id")
# elbow plot with the ranking of PCs based on the % of variance explained
ElbowPlot(so, ndims = 30)
so <- FindNeighbors(so, reduction = "pca", dims = seq_len(20), verbose = FALSE)
so <- FindClusters(so, resolution = 0.4, random.seed = 1, verbose = FALSE)## we use factors for plotting
so$sample_id <- factor(so$sample_id, levels = c("1NSC", "2NSC", "3NC52",
"4NC52", "5NC96", "6NC96",
"H9", "409b2"))
## order levels according to experiment timeline (Fig. 1a)
so$group_id <- factor(so$group_id, levels = c("P22", "D52", "D96", "iPSCs",
"EB", "Neuroectoderm",
"Neuroepithelium", "Organoid-1M",
"Organoid-2M", "Organoid-4M"))Dimension reduction plots
We plot the dimension reduction (DR) and color by the groups used for integration, sample/cell line, group/Stage, organoid cluster labels, cluster ID.
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
cs <- sample(colnames(so), 10e3)
.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("integration_group", "sample_id", "group_id", "cl_FullLineage", "ident")
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, 0.5))
print(p)
cat("\n\n")
}integration_group
sample_id
group_id
cl_FullLineage
ident
QC on DR plots
.plot_features <- function(so, dr, id) {
FeaturePlot(so, cells = cs, features = id, reduction = dr, pt.size = 0.4,
cols = c("grey", "blue")) +
guides(col = guide_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
theme_void() + theme(aspect.ratio = 1)
}
ids <- c("nUMI", "nGene", "fractionMt")
for (id in ids) {
cat("### ", id, "\n")
p1 <- .plot_features(so, "tsne", id)
lgd <- get_legend(p1)
p1 <- p1 + theme(legend.position = "none") + ggtitle("tSNE")
p2 <- .plot_features(so, "umap", id) + theme(legend.position = "none") +
ggtitle("UMAP")
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")
}nUMI
nGene
fractionMt
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_04-group_integration.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] future_1.17.0 SingleCellExperiment_1.10.1
[3] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[5] matrixStats_0.56.0 Biobase_2.48.0
[7] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[9] IRanges_2.22.2 S4Vectors_0.26.1
[11] BiocGenerics_0.34.0 Seurat_3.1.5
[13] cowplot_1.0.0 dplyr_1.0.0
[15] ggplot2_3.3.2 BiocParallel_1.22.0
[17] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] nlme_3.1-148 tsne_0.1-3 bitops_1.0-6
[4] fs_1.4.2 RcppAnnoy_0.0.16 RColorBrewer_1.1-2
[7] httr_1.4.1 rprojroot_1.3-2 sctransform_0.2.1
[10] tools_4.0.0 backports_1.1.8 R6_2.4.1
[13] irlba_2.3.3 KernSmooth_2.23-17 uwot_0.1.8
[16] lazyeval_0.2.2 colorspace_1.4-1 withr_2.2.0
[19] tidyselect_1.1.0 gridExtra_2.3 compiler_4.0.0
[22] git2r_0.27.1 plotly_4.9.2.1 labeling_0.3
[25] scales_1.1.1 lmtest_0.9-37 ggridges_0.5.2
[28] pbapply_1.4-2 rappdirs_0.3.1 stringr_1.4.0
[31] digest_0.6.25 rmarkdown_2.3 XVector_0.28.0
[34] pkgconfig_2.0.3 htmltools_0.5.0 htmlwidgets_1.5.1
[37] rlang_0.4.6 farver_2.0.3 generics_0.0.2
[40] zoo_1.8-8 jsonlite_1.7.0 ica_1.0-2
[43] RCurl_1.98-1.2 magrittr_1.5 GenomeInfoDbData_1.2.3
[46] patchwork_1.0.1 Matrix_1.2-18 Rcpp_1.0.4.6
[49] munsell_0.5.0 ape_5.4 reticulate_1.16
[52] lifecycle_0.2.0 stringi_1.4.6 whisker_0.4
[55] yaml_2.2.1 zlibbioc_1.34.0 MASS_7.3-51.6
[58] Rtsne_0.15 plyr_1.8.6 grid_4.0.0
[61] listenv_0.8.0 promises_1.1.1 ggrepel_0.8.2
[64] crayon_1.3.4 lattice_0.20-41 splines_4.0.0
[67] knitr_1.29 pillar_1.4.4 igraph_1.2.5
[70] future.apply_1.6.0 reshape2_1.4.4 codetools_0.2-16
[73] leiden_0.3.3 glue_1.4.1 evaluate_0.14
[76] data.table_1.12.8 vctrs_0.3.1 png_0.1-7
[79] httpuv_1.5.4 gtable_0.3.0 RANN_2.6.1
[82] purrr_0.3.4 tidyr_1.1.0 xfun_0.15
[85] rsvd_1.0.3 RSpectra_0.16-0 later_1.1.0.1
[88] survival_3.2-3 viridisLite_0.3.0 tibble_3.0.1
[91] cluster_2.1.0 globals_0.12.5 fitdistrplus_1.1-1
[94] ellipsis_0.3.1 ROCR_1.0-11