westerlund is a toolkit for single-cell RNA-sequencing analysis built on top of Seurat providing functions to quickly and comprehensively assess clustering results. Inspired by many publications and prior packages (highlighted below), westerlund enables data sketching, cluster bootstrapping, automatic and assisted resolution scanning, and metric visualizations.
westerlund utilizes many ideas and functions developed and published by wonderful researchers cited here and within functions. Please cite original and derivative work if possible and deemed appropriate. If you come across work within this package that is derivative but not appropriately cited, please contact us right away so we can include that documentation.
Why westerlund ?
The functions within westerlund provide additional critical functionality for common single-cell analyses that have been implemented in a range of outstanding publications. ✔️ indicate completed implementation. Functions include:
- ✔️ Supporting functions based on the recommendations by the authors of pipeComp,
- Leiden clustering implemented in the
leidenbasealgorithm by the Trapnell Lab,
- Interpretable resolution and k-nearest neighbors scanning for determining the optimal cluster granularity utilizing the
futureframework,
- ✔️ Heuristic metrics to determine appropriate PCs to utilize for further dimensionality reduction and clustering,
- Cluster robustness measures including SVM classification and adjusted Rand index random seed and subset sampling,
- Functions to quickly assess cluster significance based on DE genes and merge clusters based on size and/or user flagging,
- Gene list scoring using scaled expression instead of normalized expression as utilized by Smillie et al.,
- Bayesian classifiers as implemented by Zeymour et al.,
- Gene specificity index calculations as proposed by Tosches et al.,
- Max-to-second expression ratios as utilized by Zilions et al.,
- Cell proportion modeling as proposed by Valentine Svensson along with standard Wilcoxon rank-sum and Fisher’s exact tests,
- ✔️ Ambient RNA modeling as utilized in Smillie et al.
These functions are plug and play with Seurat objects and can enable easy expansion of available scRNA-seq analytical tools.
Examples
library(tidyverse)
library(Seurat)
library(SeuratData)
library(westerlund)
#SeuratData::InstallData("pbmc3k")
#SeuratData::InstalledData()
object <- SeuratData::LoadData("pbmc3k")
object <- UpdateSeuratObject(object)
object$seurat_annotations <- as.character(object$seurat_annotations)
object$seurat_annotations[is.na(object$seurat_annotations)] <- "Unassigned"
Idents(object) <- "seurat_annotations"
object <- ReduceDims(seurat.obj = object, n.var.features = 3000, n.pcs = 50, remove.genes = NULL)
object <- Seurat::RunUMAP(object = object, reduction = "pca", reduction.name = "pca_umap",
dims = 1:object@misc$pca_metrics$tp_pc, seed.use = 1, verbose = FALSE)
# plotting
plot <- DimPlot(object, reduction = "pca_umap", group.by = "seurat_annotations", pt.size = 1, shuffle = TRUE, label = FALSE, label.size = 6, repel = TRUE)
plot + theme_classic(base_size = 18) +
#NoLegend() +
RemoveAxes() + RemoveBackgrounds(outline = FALSE) + SpaceAxesTitles() +
colorspace::scale_color_discrete_qualitative("Dark 3") +
labs(x = "UMAP1", y = "UMAP2", title = "")
object$cell_type <- object$seurat_annotations
object$cell_lineage <- recode(object$cell_type, `CD14+ Mono` = "Myeloid", DC = "Myeloid", `FCGR3A+ Mono` = "Myeloid",
`CD8 T` = "TNK", `Memory CD4 T` = "TNK", `Naive CD4 T` = "TNK", NK = "TNK")
DimPlot(object, reduction = "pca_umap", group.by = "cell_lineage", pt.size = 1, shuffle = TRUE, label = FALSE, label.size = 6, repel = TRUE)
# marker identification
markers <- DefineMarkers(object, "seurat_annotations")
object@misc$markers <- markers
# module scoring
Bcell_markers <- markers %>% filter(group == "B") %>% top_n(100, auc) %>% pull(feature)
object <- AddModuleScoreScaled(object, list(Bcell_markers), name = "SMS")
object <- AddModuleScore(object, list(Bcell_markers), name = "MS", nbin = 20, ctrl = 100)
#object@meta.data[, grep("^MS|^SMS", colnames(object[[]]))] <- NULL
colnames(object@meta.data)[grep("^MS", colnames(object[[]]))] <- "B_unscaled_score"
colnames(object@meta.data)[grep("^SMS", colnames(object[[]]))] <- "B_scaled_score"
FeatureScatter(object, "B_unscaled_score", "B_scaled_score", span = T, smooth = F)
# save object
usethis::use_data(object, overwrite = TRUE)
saveRDS(object, "data/pbmc3k_processed.rds")
anno_df <- object[[]] %>% select(cell_lineage, cell_type) %>% distinct()
anno <- anno_df$cell_type
names(anno) <- anno_df$cell_lineage
input_data <- GetAssayData(object, slot = "counts")
TP10K <- sweep(input_data, 2, object$nCount_RNA, '/')*10000
contam <- DetectAmbientRNA(data = TP10K,
lineages = object$cell_lineage,
types = object$cell_type,
batches = object$orig.ident,
anno = NULL)
tnk_model <- contam$lineage_models$TNK
p <- PlotAmbientRNA(u1 = tnk_model$u1, u2 = tnk_model$u2, coefs = tnk_model$coefs,
residuals = tnk_model$residuals, fit.cutoff = 2, genes_fit = tnk_model$lab.fit,
label_n = 10)
p
gene <- "AIF1"
object <- schex::make_hexbin(object, nbins = sqrt(ncol(object)), dimension_reduction = "pca_umap")
schex::plot_hexbin_gene(object, type = "data", gene = gene, action = "median") +
labs(x = "UMAP1", y = "UMAP2", title = gene) +
scale_fill_continuous(low = "gray75",
high = colorspace::sequential_hcl(palette = "Reds", n = 5)[1], name = "Median\nlog(TP10K+1)") +
theme_classic(base_size = 18) + RemoveAxes() + RemoveBackgrounds() + SpaceAxesTitles()
FeaturePlot(object, gene, cols = c("gray75", colorspace::sequential_hcl(palette = "Reds", n = 5)[1]), order = TRUE) +
RemoveAxes() + RemoveBackgrounds(outline = FALSE) + SpaceAxesTitles() + labs(x = "UMAP1", y = "UMAP2")
OutlinedDotPlot(object, features = tnk_model$lab.con[1:10], cols = c("gray80", "black")) +
coord_flip() + RotatedAxis() + labs(x = "Type", y = "Gene") + SpaceAxesTitles() + RemoveBackgrounds(outline = TRUE)
