Organoid stage integration
Katharina Hembach
9/18/2020
Last updated: 2020-09-18
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d1615eb | khembach | 2020-09-18 | integrate by cell line and stage |
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 integration_group, sample_id, group_id, Stage, nUMI, fractionMt, nGene,
so_nsc$Stage <- so_nsc$group_id
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$Sample
so_org$group_id <- so_org$Line
so_org$fractionMt <- so_org$PercentMito
so_org$Line <- NULL
so_org$PercentMito <- NULL
so_nsc$integration_group <- so_nsc$group_id
so_org$integration_group <- paste0(so_org$Stage, "_", so_org$group_id)
# split cells by integration group
cells_by_sample <- split(colnames(so_nsc), so_nsc$integration_group)
so_nsc <- lapply(cells_by_sample, function(i) subset(so_nsc, cells = i))
cells_by_sample <- split(colnames(so_org), so_org$integration_group)
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$group_id <- factor(so$group_id,
levels = c("P22", "D52", "D96", "H9", "409b2"))
## order levels according to experiment timeline (Fig. 1a)
so$Stage <- factor(so$Stage, levels = c("P22", "D52", "D96", "iPSCs", "EB",
"Neuroectoderm", "Neuroepithelium",
"Organoid-1M", "Organoid-2M",
"Organoid-4M"))
## merge the lineage labels of identical cell types
so$cl_FullLineage <- as.factor(so$cl_FullLineage)
levels(so$cl_FullLineage) <- c("choroid plexus/mesenchymal-like cells",
"cortical neurons", "cortical neurons",
"cycling dorsal progenitors", "cycling ventral progenitors",
"ectodermal/neuroectodermal-like cells",
"gliogenic/outer RGCs and astrocytes",
"IPs and early cortical neurons", "midbrain/hindbrain cells",
"neuroepithelial-like cells", "retina progenitors", "RGCs",
"RGCs early", "RGCs early", "stem cells", "stem cells",
"stem cells", "ventral progenitors and neurons",
"ventral progenitors and neurons",
"ventral progenitors and neurons")Dimension reduction plots
We plot the dimension reduction (DR) and color by the groups used for integration, sample/cell line, group/Stage, organoid cluster labels, cluster ID.
# set cluster IDs to resolution 0.4 clustering
so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$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", "Stage",
"cl_FullLineage", "cl_LineComp", "PredCellType", "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
Stage
cl_FullLineage
cl_LineComp
PredCellType
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-stage_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_1.0.2
[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_1.1.9 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_0.4.7 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_1.0.5
[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_1.4.6 igraph_1.2.5
[70] future.apply_1.6.0 reshape2_1.4.4 codetools_0.2-16
[73] leiden_0.3.3 glue_1.4.2 evaluate_0.14
[76] data.table_1.12.8 vctrs_0.3.4 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_3.0.3
[91] cluster_2.1.0 globals_0.12.5 fitdistrplus_1.1-1
[94] ellipsis_0.3.1 ROCR_1.0-11