Code
remotes:: install_github("jr-leary7/SCISSORS")scRNA-seq Reclustering with SCISSORS
Introduction
In this tutorial we’ll walk through a basic single cell analysis, with a focus on fine-tuning clustering results using the SCISSORS package, which I wrote during my time at UNC Chapel Hill.
Libraries
If you haven’t already, install the development version (currently v1.2.0) of SCISSORS from the GitHub repository.
Next, we’ll load the packages we need to process our single cell data, recluster the cells, and visualize the results.
Code
library(dplyr) # data manipulation
library(Seurat) # scRNA-seq tools
library(ggplot2) # plot utilities
library(SCISSORS) # scRNA-seq reclustering
library(paletteer) # color palettes
library(patchwork) # plot combination
library(SeuratData) # datasets Data
We’ll load in the well-known PBMC3k data from 10X Genomics, which is often used in example workflows such as the Seurat clustering vignette and the scanpy vignette. If you haven’t already downloaded the dataset, this function will download the raw data for you and load it into your R session.
Code
pbmc <- LoadData("pbmc3k")Analysis
Preprocessing
We’ll do some minor quality-control checking first by filtering out cells with a high percentage of mitochondrial reads or very low or high numbers of detected genes.
Code
pbmc <- PercentageFeatureSet(pbmc,
pattern = "^MT-",
col.name = "percent_MT")
pbmc <- pbmc[, pbmc$nFeature_RNA >= 200 & pbmc$nFeature_RNA <= 2500 & pbmc$percent_MT <= 10]We’ll process the raw counts in the usual fashion: QC, normalization, identification of highly variable genes (HVGs), linear & non-linear dimension reduction, and a broad clustering that will (hopefully) capture our major celltypes. When computing the shared nearest-neighbor (SNN) graph, we use the heuristic \(k = \sqrt{n}\) for the number of nearest-neighbors to consider for each cell. This ensures that the clustering will be broad i.e., a smaller number of large clusters will be returned instead of a larger number of small clusters.
Code
pbmc <- NormalizeData(pbmc,
normalization.method = "LogNormalize",
verbose = FALSE) %>%
FindVariableFeatures(selection.method = "vst",
nfeatures = 3000,
verbose = FALSE) %>%
CellCycleScoring(s.features = cc.genes.updated.2019$s.genes,
g2m.features = cc.genes.updated.2019$g2m.genes,
set.ident = FALSE) %>%
AddMetaData(metadata = c(.$S.Score - .$G2M.Score), col.name = "CC_difference") %>%
ScaleData(vars.to.regress = "CC_difference", verbose = FALSE) %>%
RunPCA(features = VariableFeatures(.),
npcs = 50,
verbose = FALSE,
seed.use = 312) %>%
RunUMAP(reduction = "pca",
dims = 1:20,
n.components = 2,
metric = "cosine",
seed.use = 312,
verbose = FALSE) %>%
FindNeighbors(reduction = "pca",
dims = 1:20,
k.param = sqrt(ncol(.)),
nn.method = "annoy",
annoy.metric = "cosine",
verbose = FALSE) %>%
FindClusters(resolution = 0.3,
random.seed = 312,
verbose = FALSE)Let’s visualize the principal components. Notable genes in PC 1 include MALAT1, high abundance of which is a common artifact of 10X-sequenced data. PC 2 seems to separate NK cells (NKG7, GZMB) and myeloid cells (HLA-DRA, CD79A). PC 3 is composed of variation that could originate from platelets (PPBP). PCs 4-6 look like they separate several types of monocytic, T, NK, and dendritic cells.
Code
DimHeatmap(pbmc,
reduction = "pca",
dims = 1:6,
nfeatures = 15,
combine = TRUE)
We visualize the Louvain clustering via a UMAP plot. We see 5 major clusters, which we’ll annotate next.
Code
DimPlot(pbmc, pt.size = 1) +
scale_color_paletteer_d("ggsci::nrc_npg") +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
Broad Annotations
First we identify CD8+ T-cells via CD8A, and CD4+ T-cells with IL7R. Lastly, FCGR3A (aka CD16) is specific to CD16+ monocytes. We can combine the plots using the excellent patchwork package.
Code
p1 <- FeaturePlot(pbmc, features = "CD8A", pt.size = 1) +
scale_color_gradientn(colours = paletteer_d("wesanderson::Zissou1")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p2 <- FeaturePlot(pbmc, features = "IL7R", pt.size = 1) +
scale_color_gradientn(colours = paletteer_d("wesanderson::Zissou1")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p1 / p2
Next, we use HLA-DRA to broadly identify monocytic cells, and FCGR3A (aka CD16) to single out the CD16+ monocytes.
Code
p1 <- FeaturePlot(pbmc, features = "HLA-DRA", pt.size = 1) +
scale_color_gradientn(colours = paletteer_d("wesanderson::Zissou1")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p2 <- FeaturePlot(pbmc, features = "FCGR3A", pt.size = 1) +
scale_color_gradientn(colours = paletteer_d("wesanderson::Zissou1")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
p1 / p2
Lastly, abundance of MS4A1 points out a cluster of B cells.
Code
FeaturePlot(pbmc, features = "MS4A1", pt.size = 1) +
scale_color_gradientn(colours = paletteer_d("wesanderson::Zissou1")) +
labs(x = "UMAP 1", y = "UMAP 2") +
theme(axis.ticks = element_blank(),
axis.text = element_blank())
We’ll add broad celltype labels to our object’s metadata.
Code
pbmc@meta.data <- mutate(pbmc@meta.data,
broad_celltype = case_when(seurat_clusters == 0 ~ "CD4+ T",
seurat_clusters == 1 ~ "Monocyte",
seurat_clusters == 2 ~ "CD8+ T",
seurat_clusters == 3 ~ "B",
seurat_clusters == 4 ~ "CD16+ Monocyte",
TRUE ~ NA_character_),
broad_celltype = factor(broad_celltype, levels = c("CD4+ T",
"Monocyte",
"CD8+ T",
"B",
"CD16+ Monocyte")))And visualize the results.
Code
DimPlot(pbmc, pt.size = 1, group.by = "broad_celltype") +
scale_color_paletteer_d("ggsci::nrc_npg") +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "Broad Celltype") +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_blank())
Reclustering
From the plot above, there appears to be some visible subgroups in the monocyte cluster. With that being said - I would generally be very cautious about using UMAPs alone to define heterogeneous groups. In general, I would suggest using something like silhouette score distributions, other clustering statistics, or biological knowledge to determine subclustering targets. We can do this below using ComputeSilhouetteScores(), which returns a silhouette score for each individual cell. Visualizing the results can help us identify which clusters are “poor” fits. For more information, check out the Wikipedia article on clustering scores.
Code
sil_scores <- ComputeSilhouetteScores(pbmc, avg = FALSE)We can see that the B cell and CD16+ monocyte clusters seem to be well-fit, but the other clusters are less so. We’ll focus on the other monocyte cluster, as it seems to have the highest variance.
Code
sil_scores %>%
left_join(distinct(pbmc@meta.data, seurat_clusters, broad_celltype),
by = c("Cluster" = "seurat_clusters")) %>%
ggplot(aes(x = broad_celltype, y = Score, fill = broad_celltype)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("ggsci::nrc_npg") +
labs(y = "Silhouette Score", fill = "Broad Celltype") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
Monocytes
Code
mono_reclust <- ReclusterCells(pbmc,
which.clust = 1,
use.parallel = FALSE,
n.HVG = 3000,
n.PC = 15,
k.vals = c(20, 30, 40),
resolution.vals = c(.2, .3, .4),
random.seed = 312)[1] "Reclustering cells in cluster 1 using k = 20 & resolution = 0.2; S = 0.419"Let’s check out the UMAP embedding:
Code
DimPlot(mono_reclust) +
scale_color_paletteer_d("MetBrewer::Egypt") +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "Subcluster") +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_blank())
Highly-specific abundance of FCER1A allows us to identify the dendritic cells in cluster 2.
Code
data.frame(exp = mono_reclust@assays$RNA@data["FCER1A", ],
label = mono_reclust$seurat_clusters) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "FCER1A", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
Both cluster 0 & cluster 1 seem to be CD14+, and cluster 1 appears to have slightly higher (but still low) abundance of FCGR3A.
Code
p1 <- data.frame(exp = mono_reclust@assays$RNA@data["CD14", ],
label = mono_reclust$seurat_clusters) %>%
filter(label %in% c(0, 1)) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "CD14", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
p2 <- data.frame(exp = mono_reclust@assays$RNA@data["FCGR3A", ],
label = mono_reclust$seurat_clusters) %>%
filter(label %in% c(0, 1)) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "FCGR3A", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
p1 / p2
From Kapellos et al (2019), we know that intermediate monocytes have high abundance of CD14, low but non-zero abundance of CD16 (which again is denoted FCGR3A in this dataset), and can be identified through higher abundance of other markers like HLA-DPB1 and CD74 in comparison to CD14+ monocytes. With all this information, we’ll conclude that cluster 0 is likely composed of CD14+ monocytes and cluster 1 of intermediate monocytes.
Code
p1 <- data.frame(exp = mono_reclust@assays$RNA@data["HLA-DQB1", ],
label = mono_reclust$seurat_clusters) %>%
filter(label %in% c(0, 1)) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "HLA-DQB1", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
p2 <- data.frame(exp = mono_reclust@assays$RNA@data["CD74", ],
label = mono_reclust$seurat_clusters) %>%
filter(label %in% c(0, 1)) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "CD74", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
p1 / p2
Lastly, we can tell that cluster 3 is composed of platelets thanks to high abundance of PPBP.
Code
data.frame(exp = mono_reclust@assays$RNA@data["PPBP", ],
label = mono_reclust$seurat_clusters) %>%
ggplot(aes(x = label, y = exp, fill = label)) +
geom_violin(scale = "width",
color = "black",
draw_quantiles = 0.5,
size = 0.75) +
scale_fill_paletteer_d("MetBrewer::Egypt") +
labs(y = "PPBP", fill = "Subcluster") +
theme_classic(base_size = 14) +
theme(panel.grid.major.y = element_line(),
axis.title.x = element_blank())
We can add the new subcluster labels back in to our original object using IntegrateSubclusters(). We also add labels to the original object reflecting the subcluster annotations.
Code
pbmc <- IntegrateSubclusters(pbmc, reclust.results = mono_reclust)
pbmc@meta.data <- mutate(pbmc@meta.data,
celltype = case_when(seurat_clusters == 0 ~ "CD4+ T",
seurat_clusters == 1 ~ "Platelet",
seurat_clusters == 2 ~ "CD8+ T",
seurat_clusters == 3 ~ "B",
seurat_clusters == 4 ~ "CD16+ Monocyte",
seurat_clusters == 5 ~ "CD14+ Monocyte",
seurat_clusters == 6 ~ "Intermediate Monocyte",
seurat_clusters == 7 ~ "Dendritic Cell",
TRUE ~ NA_character_))Here’s the final celltype annotations on our original UMAP embedding.
Code
DimPlot(pbmc, group.by = "celltype", pt.size = 1) +
scale_color_paletteer_d("ggsci::default_nejm") +
labs(x = "UMAP 1",
y = "UMAP 2",
color = "Celltype") +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
plot.title = element_blank())
Conclusions
We were able to use SCISSORS to estimate a biologically meaningful subclustering that is to some degree supported by canonical marker genes from the literature. I’ll note that while these monocyte subtype annotations might be useful, we didn’t analyze the entire dataset; those familiar with the PBMC3k dataset will know that what we labelled the CD8+ T cell cluster actually also contains NK cells. This analysis is not meant to be exhaustive or final, and serves mostly to show how and why SCISSORS is used. Please reach out to me with questions on the package via email, or by opening an issueon the GitHub repository.
Session Info
Code
sessioninfo::session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.2.1 (2022-06-23)
os macOS Big Sur ... 10.16
system x86_64, darwin17.0
ui X11
language (EN)
collate en_US.UTF-8
ctype en_US.UTF-8
tz America/New_York
date 2023-11-02
pandoc 2.19.2 @ /usr/local/bin/ (via rmarkdown)
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sessioninfo 1.2.2 2021-12-06 [1] CRAN (R 4.2.0)
Seurat * 4.1.1 2022-05-02 [1] CRAN (R 4.2.0)
SeuratData * 0.2.2 2022-08-29 [1] Github (satijalab/seurat-data@d6a8ce6)
SeuratObject * 4.1.0 2022-05-01 [1] CRAN (R 4.2.0)
shiny 1.7.2 2022-07-19 [1] CRAN (R 4.2.0)
sp * 1.5-0 2022-06-05 [1] CRAN (R 4.2.0)
spatstat.core 2.4-4 2022-05-18 [1] CRAN (R 4.2.0)
spatstat.data 3.0-0 2022-10-21 [1] CRAN (R 4.2.0)
spatstat.geom 3.0-3 2022-10-25 [1] CRAN (R 4.2.0)
spatstat.random 3.0-1 2022-11-03 [1] CRAN (R 4.2.0)
spatstat.sparse 3.0-0 2022-10-21 [1] CRAN (R 4.2.0)
spatstat.utils 3.0-1 2022-10-19 [1] CRAN (R 4.2.0)
stringi 1.7.8 2022-07-11 [1] CRAN (R 4.2.0)
stringr 1.4.1 2022-08-20 [1] CRAN (R 4.2.0)
survival 3.4-0 2022-08-09 [1] CRAN (R 4.2.0)
tensor 1.5 2012-05-05 [1] CRAN (R 4.2.0)
tibble 3.1.8 2022-07-22 [1] CRAN (R 4.2.0)
tidyr 1.2.0 2022-02-01 [1] CRAN (R 4.2.0)
tidyselect 1.1.2 2022-02-21 [1] CRAN (R 4.2.0)
utf8 1.2.2 2021-07-24 [1] CRAN (R 4.2.0)
uwot 0.1.16 2023-06-29 [1] CRAN (R 4.2.0)
vctrs 0.6.3 2023-06-14 [1] CRAN (R 4.2.0)
viridisLite 0.4.1 2022-08-22 [1] CRAN (R 4.2.0)
withr 2.5.0 2022-03-03 [1] CRAN (R 4.2.0)
xfun 0.32 2022-08-10 [1] CRAN (R 4.2.0)
XML 3.99-0.10 2022-06-09 [1] CRAN (R 4.2.0)
xml2 1.3.3 2021-11-30 [1] CRAN (R 4.2.0)
xtable 1.8-4 2019-04-21 [1] CRAN (R 4.2.0)
XVector 0.36.0 2022-04-26 [1] Bioconductor
yaml 2.3.5 2022-02-21 [1] CRAN (R 4.2.0)
zlibbioc 1.42.0 2022-04-26 [1] Bioconductor
zoo 1.8-10 2022-04-15 [1] CRAN (R 4.2.0)
[1] /Library/Frameworks/R.framework/Versions/4.2/Resources/library
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