Clustering of timeline experiment
Katharina Hembach
10/15/2020
Last updated: 2020-10-15
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e6e6710 | khembach | 2020-10-15 | cluster old samples with D223 sample (no integration) |
Load packages
library(BiocParallel)
library(ggplot2)
library(dplyr)
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(future)Load data
## first dataset
sce <- readRDS(file.path("output", "sce_03_filtering_all_genes.rds"))
## second dataset
sce2 <- readRDS(file.path("output", "sce_TDP_03_filtering_all_genes.rds"))
## we only keep the two samples of 223days old neural cultures
sce2 <- sce2[,colData(sce2)$sample_id %in% c("NC223a", "NC223b")]
sce2$sample_id <- droplevels(sce2$sample_id)
sce2$group_id <- "D223"We merge the samples from the two data sets into a Seurat object.
so <- CreateSeuratObject(
counts = counts(sce),
meta.data = data.frame(colData(sce)),
project = "time_line")Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')so2 <- CreateSeuratObject(
counts = counts(sce2),
meta.data = data.frame(colData(sce2)),
project = "d223")Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')## merge the two Seurat objects
so <- merge(so, y = so2, add.cell.ids = c("time_line", "d223"),
project = "neural_cultures", merge.data = TRUE)
so$group_id <- factor(so$group_id, levels = c("P22", "D52", "D96", "D223"))Normalization
# split by sample
cells_by_sample <- split(colnames(so), so$sample_id)
so <- lapply(cells_by_sample, function(i) subset(so, cells = i))
## log normalize the data using a scaling factor of 10000
so <- lapply(so, NormalizeData, verbose = FALSE, scale.factor = 10000,
normalization.method = "LogNormalize")We merge the normalized and data of the six samples into a combined Seurat object and compute variable features.
## merge the individual Seurat objects and conserve the normalized and scaled data
so <- merge(so[[1]], y = so[2:length(so)], project = "NC_timeline",
merge.data = TRUE)so <- FindVariableFeatures(so, nfeatures = 2000,
selection.method = "vst", verbose = FALSE)
so <- ScaleData(so, verbose = FALSE, vars.to.regress = c("sum",
"subsets_Mt_percent"))Dimension reduction
We perform dimension reduction with t-SNE and UMAP based on PCA results.
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)Plot PCA results
# top genes that are associated with the first two PCs
VizDimLoadings(so, dims = 1:2, reduction = "pca")
## 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)
Clustering
We cluster the cells using the reduced PCA dimensions.
so <- FindNeighbors(so, reduction = "pca", dims = seq_len(20), verbose = FALSE)
for (res in c(0.2, 0.4, 0.8, 1))
so <- FindClusters(so, resolution = res, random.seed = 1, verbose = FALSE)Dimension reduction plots
We plot the dimension reduction (DR) and color by sample, group and cluster ID
thm <- theme(aspect.ratio = 1, legend.position = "none")
ps <- lapply(c("sample_id", "group_id", "ident"), function(u) {
p1 <- DimPlot(so, reduction = "tsne", group.by = u) + thm
p2 <- DimPlot(so, reduction = "umap", group.by = u)
lgd <- get_legend(p2)
p2 <- p2 + thm
list(p1, p2, lgd)
plot_grid(p1, p2, lgd, nrow = 1,
rel_widths = c(1, 1, 0.5))
})
plot_grid(plotlist = ps, ncol = 1)
QC on DR plots
cs <- sample(colnames(so), 1e4) ## subsample cells
.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_colourbar()) +
theme_void() + theme(aspect.ratio = 1)
}
ids <- c("sum", "detected", "subsets_Mt_percent")
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")
}sum
detected
subsets_Mt_percent
Save Seurat object to RDS
saveRDS(so, file.path("output", "so_06-clustering_all_timepoints.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] HDF5Array_1.16.1 rhdf5_2.32.2
[3] future_1.17.0 SingleCellExperiment_1.10.1
[5] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[7] matrixStats_0.56.0 Biobase_2.48.0
[9] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[11] IRanges_2.22.2 S4Vectors_0.26.1
[13] BiocGenerics_0.34.0 Seurat_3.1.5
[15] cowplot_1.0.0 dplyr_1.0.2
[17] ggplot2_3.3.2 BiocParallel_1.22.0
[19] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 ellipsis_0.3.1
[4] ggridges_0.5.2 rprojroot_1.3-2 XVector_0.28.0
[7] fs_1.4.2 leiden_0.3.3 listenv_0.8.0
[10] farver_2.0.3 ggrepel_0.8.2 RSpectra_0.16-0
[13] codetools_0.2-16 splines_4.0.0 knitr_1.29
[16] jsonlite_1.7.0 ica_1.0-2 cluster_2.1.0
[19] png_0.1-7 uwot_0.1.8 sctransform_0.2.1
[22] compiler_4.0.0 httr_1.4.1 backports_1.1.9
[25] Matrix_1.2-18 lazyeval_0.2.2 later_1.1.0.1
[28] htmltools_0.5.0 tools_4.0.0 rsvd_1.0.3
[31] igraph_1.2.5 gtable_0.3.0 glue_1.4.2
[34] GenomeInfoDbData_1.2.3 RANN_2.6.1 reshape2_1.4.4
[37] rappdirs_0.3.1 Rcpp_1.0.5 vctrs_0.3.4
[40] ape_5.4 nlme_3.1-148 lmtest_0.9-37
[43] xfun_0.15 stringr_1.4.0 globals_0.12.5
[46] lifecycle_0.2.0 irlba_2.3.3 MASS_7.3-51.6
[49] zlibbioc_1.34.0 zoo_1.8-8 scales_1.1.1
[52] promises_1.1.1 RColorBrewer_1.1-2 yaml_2.2.1
[55] reticulate_1.16 pbapply_1.4-2 gridExtra_2.3
[58] stringi_1.4.6 rlang_0.4.7 pkgconfig_2.0.3
[61] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
[64] ROCR_1.0-11 purrr_0.3.4 Rhdf5lib_1.10.0
[67] patchwork_1.0.1 htmlwidgets_1.5.1 labeling_0.3
[70] tidyselect_1.1.0 RcppAnnoy_0.0.16 plyr_1.8.6
[73] magrittr_1.5 R6_2.4.1 generics_0.0.2
[76] pillar_1.4.6 whisker_0.4 withr_2.2.0
[79] fitdistrplus_1.1-1 survival_3.2-3 RCurl_1.98-1.2
[82] tibble_3.0.3 future.apply_1.6.0 tsne_0.1-3
[85] crayon_1.3.4 KernSmooth_2.23-17 plotly_4.9.2.1
[88] rmarkdown_2.3 grid_4.0.0 data.table_1.12.8
[91] git2r_0.27.1 digest_0.6.25 tidyr_1.1.0
[94] httpuv_1.5.4 munsell_0.5.0 viridisLite_0.3.0