13 Lab 7: Pseudotime analyses
Aims
- Understand the requirements for RNA velocity computation
- Process scRNAseq using ‘spliced’ counts
- Perform lineage inference
- Compute RNA velocity and use it to orientate lineages
13.1 Process testis data in R
13.1.1 Import testis data from GSE112013 and pre-process it
The workflow we have covered in the previous days can be reused here.
- Import testis dataset in R, remove doublets, filter genes, normalize
counts, correct for batch effect and cluster cells.
R
library(SingleCellExperiment)
library(tidyverse)
# Download raw file
dir.create('Guo_testis')
download.file(
'https://ftp.ncbi.nlm.nih.gov/geo/series/GSE112nnn/GSE112013/suppl/GSE112013_Combined_UMI_table.txt.gz',
'Guo_testis/GSE112013_Combined_UMI_table.txt.gz'
)
system('gunzip Guo_testis/GSE112013_Combined_UMI_table.txt.gz')
# Create SingleCellExperiment object
x <- vroom::vroom('Guo_testis/GSE112013_Combined_UMI_table.txt')
counts <- as.matrix(x[, -1])
counts <- as(counts, 'dgCMatrix')
genes <- as.data.frame(x[, 1])
cells <- data.frame(cellid = colnames(x[, -1]))
testis <- SingleCellExperiment(
assays = list(counts = counts),
colData = cells,
rowData = genes
)
testis$Barcode <- str_replace(testis$cellid, 'Donor.-', '') |> str_replace('-.', '')
testis <- testis[, !duplicated(testis$Barcode)]
testis$donor <- str_replace(testis$cellid, '-.*', '')
testis$replicate <- str_replace(testis$cellid, '.*-', '')
rownames(testis) <- rowData(testis)$Gene
set.seed(1000)
# Remove doublets
testis <- scDblFinder::scDblFinder(testis)
testis <- testis[, testis$scDblFinder.class == 'singlet']
# Recover human genomic, protein-coding gene informations
library(plyranges)
ah <- AnnotationHub::AnnotationHub()
AnnotationHub::query(ah, c('gene annotation', 'ensembl', '102', 'homo_sapiens', 'GRCh38'))
gtf <- AnnotationHub::query(ah, c('Homo_sapiens.GRCh38.102.chr.gtf'))[[1]]
genes <- gtf |>
filter(type == 'gene') |>
filter(gene_biotype == 'protein_coding') |>
filter(gene_source == 'ensembl_havana')
genes <- genes[!duplicated(genes$gene_name)]
# Annotate genes in testis dataset and filter out non-relevant genes
testis <- testis[genes$gene_name[genes$gene_name %in% rownames(testis)], ]
rowRanges(testis) <- genes[match(rownames(testis), genes$gene_name)]
rowData(testis) <- rowData(testis)[, c('gene_name', 'gene_id')]
rownames(testis) <- scuttle::uniquifyFeatureNames(rowData(testis)$gene_id, rowData(testis)$gene_name)
# Get preliminary QCs per cell and per gene
testis <- scuttle::addPerCellQCMetrics(testis)
testis <- scuttle::addPerFeatureQCMetrics(testis)
# Filter out genes not expressed in at least 10 cells
testis <- testis[rowSums(assay(testis, 'counts') > 0) >= 10, ]
# Normalize counts using VST
cnts <- as(assay(testis, 'counts'), 'dgCMatrix')
colnames(cnts) <- testis$cellid
rownames(cnts) <- rownames(testis)
testis_vst <- sctransform::vst(cnts, return_cell_attr = TRUE)
corrected_cnts <- sctransform::correct(testis_vst)
assay(testis, 'corrected_counts', withDimnames = FALSE) <- corrected_cnts
assay(testis, 'logcounts', withDimnames = FALSE) <- log1p(corrected_cnts)
# Computing biological variance of each gene
testis_variance <- scran::modelGeneVar(testis)
HVGs <- scran::getTopHVGs(testis_variance, prop = 0.1)
rowData(testis)$isHVG <- rownames(testis) %in% HVGs
# Embedding dataset in PCA space, correcting for batch effect
mergedBatches <- batchelor::fastMNN(
testis,
batch = testis$donor,
subset.row = HVGs,
BPPARAM = BiocParallel::MulticoreParam(workers = 12)
)
reducedDim(testis, 'corrected') <- reducedDim(mergedBatches, 'corrected')
# Embedding dataset in shared k-nearest neighbors graph for clustering
graph <- scran::buildSNNGraph(testis, use.dimred = 'corrected')
# Cluster cells using Louvain community finding algorithm
testis_clust <- igraph::cluster_louvain(graph)$membership
table(testis_clust)
testis$cluster <- factor(testis_clust)
# Embedding dataset in TSNE space for visualization
set.seed(10)
testis <- scater::runTSNE(testis, dimred = 'corrected')
# Visualize embeddings
p <- cowplot::plot_grid(
scater::plotReducedDim(testis, 'corrected', colour_by = 'donor'),
scater::plotReducedDim(testis, 'corrected', colour_by = 'cluster'),
scater::plotReducedDim(testis, 'TSNE', colour_by = 'donor'),
scater::plotReducedDim(testis, 'TSNE', colour_by = 'cluster')
)13.1.2 Annotate cells using HPA resources
- Load
HPAdata from internet. - Try to format it as a
SummarizedExperiment. - What celltypes are profiled?
R
download.file(
'https://www.proteinatlas.org/download/rna_single_cell_type.tsv.zip',
'Guo_testis/rna_single_cell_type.tsv.zip'
)
system('unzip Guo_testis/rna_single_cell_type.tsv.zip')
system('mv rna_single_cell_type.tsv Guo_testis/')
HPA <- vroom::vroom('Guo_testis/rna_single_cell_type.tsv') |>
pivot_wider(names_from = `Cell type`, values_from = 'nTPM') |>
dplyr::select(-Gene) |>
distinct(`Gene name`, .keep_all = TRUE) |>
column_to_rownames('Gene name')
HPA_se <- SummarizedExperiment::SummarizedExperiment(HPA, colData = tibble(cell_type = colnames(HPA)), assays = list('logcounts' = HPA))- Use these cell type profiles to annotate cell types in the testis dataset.
R
# Transfer annotations to `testis`
predictions <- SingleR::SingleR(
test = testis,
ref = HPA_se,
labels = colData(HPA_se)$cell_type,
clusters = testis$cluster
)
testis$annotation <- predictions$labels[testis$cluster]
p <- cowplot::plot_grid(
scater::plotReducedDim(testis, dimred = 'corrected', colour_by = 'cluster', text_by = 'cluster') + ggtitle('Testis data, PCA, graph-based clusters'),
scater::plotReducedDim(testis, dimred = 'corrected', colour_by = 'annotation', text_by = 'annotation') + ggtitle('PCA, Annotations transferred from HPA'),
scater::plotReducedDim(testis, dimred = 'TSNE', colour_by = 'cluster', text_by = 'cluster') + ggtitle('Testis data, tSNE, graph-based clusters'),
scater::plotReducedDim(testis, dimred = 'TSNE', colour_by = 'annotation', text_by = 'annotation') + ggtitle('tSNE, Annotations transferred from HPA')
)13.1.3 Filter the testis dataset to only germinal cells.
13.2 Trajectory inference (TI) in scRNAseq
An important question in scRNAseq field of research is: how to identify a cell trajectory from high-dimensional expression data and map individual cells onto it? A large number of methods have currently emerged, each one with their own specificities, assumptions, and strengths. A nice breakdown (from 2019, so already very outdated!) is available from Saelens et al., Nat. Biotech. 2018 (doi: 10.1038/s41587-019-0071-9):
13.2.1 Trajectory
Slingshot is perhaps one of the most widely used algorithms for users who want to focus on R-based approaches.
- Read Slingshot documentation to understand how to identify lineages in a scRNAseq dataset in R
- Why is it recommended to infer lineages from PCA space rather than t-SNE or UMAP space, even though these spaces do “reveal” an obvious trajectory in 2D?
- Infer lineages, using cluster annotations as information to build the MST. Note that you will first need to remove the 50th PCA dimension for
slingshotto work (bug reported).
R
reducedDim(germcells, 'corrected_2') <- reducedDim(germcells, 'corrected')[, 1:49]
germcells_slingshot <- slingshot::slingshot(
germcells,
reducedDim = 'corrected_2',
clusterLabels = germcells$annotation
)
slingshot::slingLineages(germcells_slingshot)13.2.2 Pseudotime
- Recover pseudotime values and principal curves from
slingshotoutput
R
germcells$pseudotime <- slingshot::slingPseudotime(germcells_slingshot)[, 'Lineage1']
pca_curve <- slingshot::slingCurves(germcells_slingshot, as.df = TRUE)
colnames(pca_curve) <- paste0('PC', 1:ncol(pca_curve))
tsne_curve <- slingshot::embedCurves(germcells_slingshot, 'TSNE', smoother = 'loess', span = 0.1) |> slingshot::slingCurves(as.df = TRUE)
tsne_curve <- tsne_curve[order(tsne_curve$Order), ]
colnames(tsne_curve) <- paste0('TSNE', 1:ncol(tsne_curve))- Plot PCA and tSNE embeddings, coloring cells by either their annotation or their pseudotime value. Overlay the principal curves to each embedding
R
df <- tibble(
PC1 = reducedDim(germcells, 'corrected')[,1],
PC2 = reducedDim(germcells, 'corrected')[,2],
TSNE1 = reducedDim(germcells, 'TSNE')[,1],
TSNE2 = reducedDim(germcells, 'TSNE')[,2],
annotation = germcells$annotation,
pseudotime = germcells$pseudotime
)
p <- cowplot::plot_grid(
df |>
ggplot() +
geom_point(aes(PC1, PC2, col = annotation)) +
geom_path(data = pca_curve, aes(x = PC1, y = PC2)) +
theme_bw() +
coord_fixed(),
df |>
ggplot() +
geom_point(aes(TSNE1, TSNE2, col = annotation)) +
geom_path(data = tsne_curve, aes(x = TSNE1, y = TSNE2)) +
theme_bw() +
coord_fixed(),
df |>
ggplot() +
geom_point(aes(PC1, PC2, col = pseudotime)) +
geom_path(data = pca_curve, aes(x = PC1, y = PC2)) +
theme_bw() +
coord_fixed(),
df |>
ggplot() +
geom_point(aes(TSNE1, TSNE2, col = pseudotime)) +
geom_path(data = tsne_curve, aes(x = TSNE1, y = TSNE2)) +
theme_bw() +
coord_fixed()
)- Check the distribution of pseudotime values across the different cell clusters.
- What do you observe? Where you expecting this?
R
p <- tibble(
annotation = factor(germcells$annotation, c("Spermatogonia", "Spermatocytes", "Early spermatids", "Late spermatids")),
pseudotime = germcells$pseudotime
) |>
ggplot(aes(x = annotation, y = pseudotime, fill = annotation)) +
geom_violin(scale = 'width') +
geom_boxplot(outlier.shape = NULL, width = 0.1, fill = 'white') +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1, vjust = 1)) - Correct pseudotime values as you would expect it to be.
13.2.3 BONUS: Daunting snippet but that makes a cool figure for a paper: modeling pseudotime-dependent gene expression
Using pseudotime / cell, one can model gene expression along the differentiation process. This alleviates the need to study gene expression per cell, and allows one to focus on process-related effects (e.g. gene expression during a developmental trajectory).
- Try to do so for few markers of spermatogonia, spermatocytes and spermatids.
R
genes <- c('ID4', 'SYCP3', 'DMC1', 'ACR', 'PRM1', 'PGK2')
fitExprs <- logcounts(germcells[genes, ]) |> # ----------------------------------- Get norm. counts for genes of interest
as.matrix() |>
t() |>
as_tibble() |>
mutate( # ----------------------------------------------------------------- Add information for each cell
cellID = colnames(germcells),
annotation = factor(germcells$annotation, c("Spermatogonia", "Spermatocytes", "Early spermatids", "Late spermatids")),
pseudotime = germcells$pseudotime
) |>
pivot_longer(contains(genes), names_to = 'gene', values_to = 'obs_expr') |> # - Pivot in "long" tidy format
mutate(gene = factor(gene, genes)) |>
group_by(gene) |> # ------------------------------------------------------- Group rows by genes
nest(.key = 'data') |> # -------------------------------------------------- For each gene, extract the subtable into a column named data
mutate(
gamModel = map(data, ~mgcv::gam(obs_expr ~ s(pseudotime, bs = "cs"), data = .)),
gamFitted_expr = map(gamModel, predict) # ------------------------------ For each gene, fit the expression values ~ pseudotime with a GAM
) |>
dplyr::select(-ends_with('Model')) |>
unnest(c(data, ends_with('_expr'))) # -------------------------------------- Unnest all the modelled expressions
p <- ggplot(fitExprs) +
geom_point(aes(x = pseudotime, y = obs_expr, col = annotation), alpha = 0.5) +
geom_line(aes(x = pseudotime, y = gamFitted_expr), col = 'white', size = 2, alpha = 0.5) +
geom_line(aes(x = pseudotime, y = gamFitted_expr), col = '#af2d0c', size = 1) +
theme_bw() +
facet_grid(gene~., scales = 'free') +
labs(y = 'logcounts') +
ggtitle('Fitted models of pseudotime-dependent gene expression')13.3 Ordering trajectory with RNA velocity
As we saw earlier, TI does not necessarily know which direction is right for a given trajectory. This orientation can be sometimes refined using RNA velocity. Let’s see whether RNA velocity helps orientating our spermatocyte differentiation lineage trajectory here.
- Read
velociraptordocumentation. What do you need to compute RNA velocity scores in R? - Import
splicedandunsplicedcounts computed withvelocytoin R. - Try and compute RNA velocity.
R
## - Import entire GSE112013 dataset with spliced/unspliced counts
full_GSE112013_counts <- LoomExperiment::import('Share/Guo_testis/Guo_testis_full-counts.loom')
## - Filter `germcells` genes and cells to only retain those present in `full_GSE112013_counts`
germcells <- germcells[
rowData(germcells)$gene_id %in% rowData(full_GSE112013_counts)$Accession,
germcells$Barcode %in% full_GSE112013_counts$Barcode
]
## - Reorder rows of `full_GSE112013_counts_germcells` to match those of `germcells`
full_GSE112013_counts_germcells <- full_GSE112013_counts[match(rowData(germcells)$gene_id, rowData(full_GSE112013_counts)$Accession), match(germcells$Barcode, full_GSE112013_counts$Barcode)]
dim(germcells)
dim(full_GSE112013_counts_germcells)
## - Add spliced/unspliced counts to germcells
assay(germcells, 'spliced', withDimnames=FALSE) <- assay(full_GSE112013_counts_germcells, 'spliced')
assay(germcells, 'unspliced', withDimnames=FALSE) <- assay(full_GSE112013_counts_germcells, 'unspliced')
## - Run velociraptor
velo_out <- velociraptor::scvelo(
germcells,
assay.X = "counts",
subset.row = rowData(germcells)$isHVG,
use.dimred = "corrected"
)
velo_out- Embed the velocity field in tSNE scRNAseq embedding and plot the RNA velocity field on top of tSNE projection. Conclude.
R
embedded_velo <- velociraptor::embedVelocity(reducedDim(germcells, "TSNE"), velo_out)
head(embedded_velo)
grid_df <- velociraptor::gridVectors(reducedDim(germcells, "TSNE"), embedded_velo, resolution = 30, scale = TRUE)
head(grid_df)
p <- scater::plotReducedDim(germcells, 'TSNE', colour_by = "annotation", point_alpha = 0.5) +
geom_segment(
data = grid_df,
mapping = aes(x = start.1, y = start.2, xend = end.1, yend = end.2),
arrow = arrow(length = unit(0.05, "inches"), type = "closed")
)13.4 Session info
─ Session info ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────
setting value
version R version 4.3.0 (2023-04-21)
os macOS Monterey 12.5.1
system aarch64, darwin20
ui X11
language (EN)
collate en_GB.UTF-8
ctype en_GB.UTF-8
tz Europe/Paris
date 2023-06-08
pandoc 2.19.2 @ /opt/homebrew/bin/ (via rmarkdown)
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