Integration NSC from Lam et al.
Katharina Hembach
7/3/2020
Last updated: 2020-07-10
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Knit directory: neural_scRNAseq/
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|---|---|---|---|---|
| Rmd | 15a0ad2 | khembach | 2020-07-10 | compare cell cluster membership before and after NES integration; merge |
| html | a1ebb78 | khembach | 2020-07-08 | Build site. |
| Rmd | d8bd339 | khembach | 2020-07-08 | NSC integration with NES from Lam et al. |
Load packages
library(cowplot)
library(ggplot2)
library(Seurat)
library(SingleCellExperiment)
library(stringr)
library(Seurat)
library(rtracklayer)
library(future)
library(biomaRt)
library(dplyr)
library(data.table)
library(ComplexHeatmap)
library(RColorBrewer)# increase future's maximum allowed size of exported globals to 4GB
# the default is 2GB
options(future.globals.maxSize = 4096 * 1024 ^ 2)
# change the current plan to access parallelization
plan("multiprocess", workers = 20)Load our NSC data
# sce <- readRDS(file.path("output", "sce_03_filtering.rds"))
## our NSC from sample 1 and 2
# so <- readRDS(file.path("output", "NSC_1_clustering.rds"))
sce <- readRDS(file.path("output", "sce_03_filtering.rds"))
## subset the two NSC samples
sce <- sce[,colData(sce)$sample_id %in% c("1NSC", "2NSC")]
sce$sample_id <- droplevels(sce$sample_id)
# ## we filter genes and require > 1 count in at least 20 cells
# sce <- sce[rowSums(counts(sce) > 1) >= 20, ]
# dim(sce)
# create SeuratObject
so <- CreateSeuratObject(
counts = counts(sce),
meta.data = data.frame(colData(sce)),
project = "neural_cultures")Load NES data and map symbol to Ensembl IDs
nes_meta <- read.table(file.path("data", "Lam_et_al", "figure2", "Figure_2_metadata.NES.Healthy.Cell.lines.healthy_NES.7.clusters.final.figure.txt"))
nes_counts <- read.table(file.path("data", "Lam_et_al", "figure2", "Figure_2_NES.Healthy.Cell.lines.healthy_NES.7.clusters.final.figure.txt"))
sce_nes <- SingleCellExperiment(list(counts=nes_counts), colData = nes_meta)
dim(sce_nes)[1] 14086 768## I think Lam et al. used UCSC hg19 gene annotations for their analysis.
## --> we map the gene symbols to hg19 Ensembl IDs
grch37 <- biomaRt::useMart(biomart="ENSEMBL_MART_ENSEMBL", host="grch37.ensembl.org",
path="/biomart/martservice", dataset="hsapiens_gene_ensembl")
res <- getBM(attributes=c('hgnc_symbol', 'ensembl_gene_id'),
filters = 'hgnc_symbol',
values = rownames(sce_nes),
mart = grch37)
table(rownames(sce_nes) %in% res$hgnc_symbol)
FALSE TRUE
1450 12636 res_split <- split(res$ensembl_gene_id, res$hgnc_symbol)
# we match our Ensembl IDs to the biomaRt IDs and only keep rows from Lam et al.
# that match one of our IDs
a <- res %>% dplyr::left_join(data.frame(rowData(sce)),
by = c("ensembl_gene_id" = "ensembl_id"))
## if a symbols has multiple IDs, we keep the ID with the same symbol in our dataset
## find all duplicates
dups <- duplicated(a$hgnc_symbol) | duplicated(a$hgnc_symbol, fromLast=TRUE)
## check the symbol in our dataset and remove the duplicate with wrong symbol
b_split <- split(a[dups,], a[dups,]$hgnc_symbol)
## if GRCh38 symbol matches the hgnc_symbol, we keep the corresponding Ensembl ID
## otherwise we only keep the gene symbol without an ID
resolve_dups <- function(b){
res <- b[which(b$hgnc_symbol == b$symbol),]
if (nrow(res) != 1){
res <- b[1,]
res[, c("ensembl_gene_id", "symbol")] <- ""
}
res
}
b_resolved <- lapply(b_split, resolve_dups)
b_resolved <- rbindlist(b_resolved)
id_map <- rbind(a[!dups,], b_resolved)
id_map$symbol <- NULL
colnames(id_map) <- c("symbol", "ensembl_id")
## Remove the features for which we don't have an ensembl ID
sce_nes <- sce_nes[rownames(sce_nes) %in% id_map$symbol,]
dim(sce_nes)[1] 12636 768## We have to use identical gene symbols for all datasets if we want to integrate the data.
## We replace the Lam et al. gene symbols with GRCh38 symbols for all features
## where the Ensembl IDs could be mapped.
gtf <- import(file.path("data", "Homo_sapiens.GRCh38.98.sorted.gtf"))
gene <- gtf[gtf$type == "gene"]
m <- match(id_map$symbol, gene$gene_name)
id_map[!is.na(m),]$symbol <- gene$gene_name[m[!is.na(m)]]
## adjust row and colData
colData(sce_nes)$sample_id <- factor("NES")
rowData(sce_nes) <- id_map[match(rownames(sce_nes), id_map$symbol),]
rownames(sce_nes) <- with(rowData(sce_nes),
paste(ensembl_id, symbol, sep = "."))
sce_nes$group_id <- "NES"
## how many genes could be mapped between datasets?
rownames(sce_nes) %in% rownames(sce) %>% table.
FALSE TRUE
1867 10769 ## The data already comes with the number of RNAs and features
names(colData(sce_nes))[names(colData(sce_nes)) == 'BARCODE'] <- 'barcode'
names(colData(sce_nes))[names(colData(sce_nes)) == 'nCount_RNA'] <- 'sum'
names(colData(sce_nes))[names(colData(sce_nes)) == 'nFeature_RNA'] <- 'detected'
# ## we filter genes and require > 1 count in at least 5 cells
# sce_nes <- sce_nes[rowSums(counts(sce_nes) > 1) >= 5, ]
# dim(sce_nes)
## Only keep the features that are measured in both data sets
sce_nes <- sce_nes[rownames(sce_nes) %in% rownames(sce),]
sce <- sce[rownames(sce) %in% rownames(sce_nes),]
so_nes <- CreateSeuratObject(
counts = counts(sce_nes),
meta.data = data.frame(colData(sce_nes)),
project = "neural_cultures")Clustering before integration
We identify variable features and run PCA, followed by UMAP and clustering without integration. We want to know if there are big differences between the two datasets that make an integration necessary.
## combined Seurat object with both datasets
m <- match(rownames(sce_nes), rownames(sce))
counts <- DelayedArray::cbind(counts(sce), DelayedArray(counts(sce_nes)[m,]))
cdata <- colData(sce) %>% data.frame %>%
mutate(rownames = rownames(colData(sce))) %>%
dplyr::full_join(colData(sce_nes) %>%
data.frame %>%
mutate(rownames = rownames(colData(sce_nes))))
rownames(cdata) <- cdata$rownames
cdata$rownames <- NULL
so <- CreateSeuratObject(
counts = counts,
meta.data = cdata,
project = "neural_cultures")
so <- NormalizeData(so, verbose = FALSE, scale.factor = 10000,
normalization.method = "LogNormalize")
so <- FindVariableFeatures(so, nfeatures = 2000,
selection.method = "vst", verbose = FALSE)
so <- ScaleData(so, verbose = FALSE, vars.to.regress = "sum")
so <- RunPCA(so, npcs = 30, verbose = FALSE)
so <- RunTSNE(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, do.fast = TRUE, verbose = FALSE)
so <- RunUMAP(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, verbose = FALSE)Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric
To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation'
This message will be shown once per sessionDimPlot(so, reduction = "pca", group.by = "sample_id")
so <- FindNeighbors(so, reduction = "pca", dims = seq_len(20), verbose = FALSE)
so <- FindClusters(so, resolution = 0.4, random.seed = 1, verbose = FALSE)
thm <- theme(aspect.ratio = 1, legend.position = "none")
ps <- lapply(c("sample_id", "group_id", "ident"), function(u) {
p1 <- DimPlot(so, reduction = "tsne", group.by = u) + thm
p2 <- DimPlot(so, reduction = "umap", group.by = u)
lgd <- get_legend(p2)
p2 <- p2 + thm
list(p1, p2, lgd)
plot_grid(p1, p2, lgd, nrow = 1,
rel_widths = c(1, 1, 0.5))
})
plot_grid(plotlist = ps, ncol = 1)
The NES cells cluster apart from our NSCs and we need to integrate the datasets.
Integrated analysis
Now we repeat the clustering on the integrated data.
Normalisation
# split by sample
cells_by_sample <- split(colnames(sce), sce$sample_id)
so <- lapply(cells_by_sample, function(i) subset(so, cells = i))
## add the NES
so[["NES"]] <- so_nes
## log normalize the data using a scaling factor of 10000
so <- lapply(so, NormalizeData, verbose = FALSE, scale.factor = 10000,
normalization.method = "LogNormalize")## Identify the top 2000 genes with high cell-to-cell variation
so <- lapply(so, FindVariableFeatures, nfeatures = 2000,
selection.method = "vst", verbose = FALSE)
## Plot variable features
for (i in names(so)) {
# Identify the 10 most highly variable genes
top10 <- head(VariableFeatures(so[[i]]), 10)
p <- VariableFeaturePlot(so[[i]])
p <- LabelPoints(plot = p, points = top10,
labels = str_split(top10, "\\.", simplify = TRUE)[,2],
repel = TRUE)
print(p)
}
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
# find anchors & integrate
as <- FindIntegrationAnchors(so, verbose = FALSE)
so <- IntegrateData(anchorset = as, dims = seq_len(30), verbose = FALSE)
## We scale the data so that mean expression is 0 and variance is 1, across cells
## We also regress out the number of UMIs.
## We don't have mitochondrial genes for the NES
DefaultAssay(so) <- "integrated"
so <- ScaleData(so, verbose = FALSE, vars.to.regress = "sum")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")
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
## PCA plot
DimPlot(so, reduction = "pca", group.by = "sample_id")
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
# elbow plot with the ranking of PCs based on the % of variance explained
ElbowPlot(so, ndims = 30)
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
## heatmaps of the top 20 PCs and the 500 most extreme cells for each component
DimHeatmap(so, dims = 1:20, cells = 500, balanced = TRUE, nfeatures = 20 )
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
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(0.1, 0.2, 0.4, 0.8, 1, 1.2, 2))
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)
| Version | Author | Date |
|---|---|---|
| a1ebb78 | khembach | 2020-07-08 |
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_legend(nrow = 11,
override.aes = list(size = 3, alpha = 1))) +
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")
}
Evaluation of cluster before and after integration
We evaluate if cells which were together in a cluster before the integration of the NES cells are still in the same cluster after integration. We use resolution 0.4 for this analysis.
## Load the Seurat object from our NSC analysis
so_before <- readRDS(file.path("output", "NSC_1_clustering.rds"))
so_before <- SetIdent(so_before, value = "integrated_snn_res.0.4")
so_before@meta.data$cluster_id <- Idents(so_before)
table(so_before@meta.data$cluster_id)
0 1 2 3 4 5 6
5826 3425 3229 2121 1425 665 48 so <- SetIdent(so, value = "integrated_snn_res.0.4")
so@meta.data$cluster_id <- Idents(so)
## subset to our NSCs
cs <- which(so@meta.data$group_id == "P22")
sub <- subset(so, cells = cs)
table(sub@meta.data$cluster_id)
0 1 2 3 4 5 6
5920 3495 3251 2025 1164 813 71 ## join the cluster_ids from both clustering runs
before <- data.frame(cell = colnames(so_before),
cluster_before = so_before@meta.data[,c("cluster_id")])
after <- data.frame(cell = colnames(sub),
cluster_after = sub@meta.data[,c("cluster_id")])
clusters <- before %>% full_join(after)
## check if cells from the same cluster are still in the same cluster
(n_clusters <- table(clusters$cluster_before, clusters$cluster_after))
0 1 2 3 4 5 6
0 5360 166 81 109 25 67 18
1 101 3131 33 20 15 122 3
2 82 77 3038 27 0 4 1
3 88 97 89 1827 17 3 0
4 240 21 8 38 1106 11 1
5 49 3 2 4 1 606 0
6 0 0 0 0 0 0 48fqs <- prop.table(n_clusters, 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 = "clusters before NES integration",
column_title = "clusters after NES integration",
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)))
Save Seurat object to RDS
saveRDS(so, file.path("output", "Lam-01-clustering.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] grid parallel stats4 stats graphics grDevices utils
[8] datasets methods base
other attached packages:
[1] HDF5Array_1.16.1 rhdf5_2.32.2
[3] RColorBrewer_1.1-2 ComplexHeatmap_2.4.2
[5] data.table_1.12.8 dplyr_1.0.0
[7] biomaRt_2.44.1 future_1.17.0
[9] rtracklayer_1.48.0 stringr_1.4.0
[11] SingleCellExperiment_1.10.1 SummarizedExperiment_1.18.1
[13] DelayedArray_0.14.0 matrixStats_0.56.0
[15] Biobase_2.48.0 GenomicRanges_1.40.0
[17] GenomeInfoDb_1.24.2 IRanges_2.22.2
[19] S4Vectors_0.26.1 BiocGenerics_0.34.0
[21] Seurat_3.1.5 ggplot2_3.3.2
[23] cowplot_1.0.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] circlize_0.4.10 backports_1.1.8 BiocFileCache_1.12.0
[4] plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2
[7] splines_4.0.0 BiocParallel_1.22.0 listenv_0.8.0
[10] digest_0.6.25 htmltools_0.5.0 magrittr_1.5
[13] memoise_1.1.0 cluster_2.1.0 ROCR_1.0-11
[16] globals_0.12.5 Biostrings_2.56.0 askpass_1.1
[19] prettyunits_1.1.1 colorspace_1.4-1 blob_1.2.1
[22] rappdirs_0.3.1 ggrepel_0.8.2 xfun_0.15
[25] crayon_1.3.4 RCurl_1.98-1.2 jsonlite_1.7.0
[28] survival_3.2-3 zoo_1.8-8 ape_5.4
[31] glue_1.4.1 gtable_0.3.0 zlibbioc_1.34.0
[34] XVector_0.28.0 leiden_0.3.3 GetoptLong_1.0.1
[37] Rhdf5lib_1.10.0 shape_1.4.4 future.apply_1.6.0
[40] scales_1.1.1 DBI_1.1.0 Rcpp_1.0.4.6
[43] viridisLite_0.3.0 progress_1.2.2 clue_0.3-57
[46] reticulate_1.16 bit_1.1-15.2 rsvd_1.0.3
[49] tsne_0.1-3 htmlwidgets_1.5.1 httr_1.4.1
[52] ellipsis_0.3.1 ica_1.0-2 farver_2.0.3
[55] pkgconfig_2.0.3 XML_3.99-0.4 uwot_0.1.8
[58] dbplyr_1.4.4 labeling_0.3 tidyselect_1.1.0
[61] rlang_0.4.6 reshape2_1.4.4 later_1.1.0.1
[64] AnnotationDbi_1.50.1 munsell_0.5.0 tools_4.0.0
[67] generics_0.0.2 RSQLite_2.2.0 ggridges_0.5.2
[70] evaluate_0.14 yaml_2.2.1 knitr_1.29
[73] bit64_0.9-7 fs_1.4.2 fitdistrplus_1.1-1
[76] purrr_0.3.4 RANN_2.6.1 pbapply_1.4-2
[79] nlme_3.1-148 whisker_0.4 compiler_4.0.0
[82] plotly_4.9.2.1 curl_4.3 png_0.1-7
[85] tibble_3.0.1 stringi_1.4.6 RSpectra_0.16-0
[88] lattice_0.20-41 Matrix_1.2-18 vctrs_0.3.1
[91] pillar_1.4.4 lifecycle_0.2.0 lmtest_0.9-37
[94] GlobalOptions_0.1.2 RcppAnnoy_0.0.16 bitops_1.0-6
[97] irlba_2.3.3 httpuv_1.5.4 patchwork_1.0.1
[100] R6_2.4.1 promises_1.1.1 KernSmooth_2.23-17
[103] gridExtra_2.3 codetools_0.2-16 MASS_7.3-51.6
[106] assertthat_0.2.1 openssl_1.4.2 rprojroot_1.3-2
[109] rjson_0.2.20 withr_2.2.0 GenomicAlignments_1.24.0
[112] sctransform_0.2.1 Rsamtools_2.4.0 GenomeInfoDbData_1.2.3
[115] hms_0.5.3 tidyr_1.1.0 rmarkdown_2.3
[118] Rtsne_0.15 git2r_0.27.1