vignettes/hashing_vignette.Rmd
hashing_vignette.Rmd
Developed in collaboration with the Technology Innovation Group at NYGC, Cell Hashing uses oligo-tagged antibodies against ubiquitously expressed surface proteins to place a “sample barcode” on each single cell, enabling different samples to be multiplexed together and run in a single experiment. For more information, please refer to this paper.
This vignette will give a brief demonstration on how to work with data produced with Cell Hashing in Seurat. Applied to two datasets, we can successfully demultiplex cells to their the original sample-of-origin, and identify cross-sample doublets.
HTODemux()
implements the following procedure:Load packages
Read in data
# Load in the UMI matrix
pbmc.umis <- readRDS("../data/pbmc_umi_mtx.rds")
# For generating a hashtag count matrix from FASTQ files, please refer to
# https://github.com/Hoohm/CITE-seq-Count. Load in the HTO count matrix
pbmc.htos <- readRDS("../data/pbmc_hto_mtx.rds")
# Select cell barcodes detected by both RNA and HTO In the example datasets we have already
# filtered the cells for you, but perform this step for clarity.
joint.bcs <- intersect(colnames(pbmc.umis), colnames(pbmc.htos))
# Subset RNA and HTO counts by joint cell barcodes
pbmc.umis <- pbmc.umis[, joint.bcs]
pbmc.htos <- as.matrix(pbmc.htos[, joint.bcs])
# Confirm that the HTO have the correct names
rownames(pbmc.htos)
## [1] "HTO_A" "HTO_B" "HTO_C" "HTO_D" "HTO_E" "HTO_F" "HTO_G" "HTO_H"
Setup Seurat object and add in the HTO data
# Setup Seurat object
pbmc.hashtag <- CreateSeuratObject(counts = pbmc.umis)
# Normalize RNA data with log normalization
pbmc.hashtag <- NormalizeData(pbmc.hashtag)
# Find and scale variable features
pbmc.hashtag <- FindVariableFeatures(pbmc.hashtag, selection.method = "mean.var.plot")
pbmc.hashtag <- ScaleData(pbmc.hashtag, features = VariableFeatures(pbmc.hashtag))
You can read more about working with multi-modal data here
# Add HTO data as a new assay independent from RNA
pbmc.hashtag[["HTO"]] <- CreateAssayObject(counts = pbmc.htos)
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
pbmc.hashtag <- NormalizeData(pbmc.hashtag, assay = "HTO", normalization.method = "CLR")
Here we use the Seurat function HTODemux()
to assign single cells back to their sample origins.
# If you have a very large dataset we suggest using k_function = 'clara'. This is a k-medoid
# clustering function for large applications You can also play with additional parameters (see
# documentation for HTODemux()) to adjust the threshold for classification Here we are using the
# default settings
pbmc.hashtag <- HTODemux(pbmc.hashtag, assay = "HTO", positive.quantile = 0.99)
Output from running HTODemux()
is saved in the object metadata. We can visualize how many cells are classified as singlets, doublets and negative/ambiguous cells.
# Global classification results
table(pbmc.hashtag$HTO_classification.global)
##
## Doublet Negative Singlet
## 2598 346 13972
Visualize enrichment for selected HTOs with ridge plots
# Group cells based on the max HTO signal
Idents(pbmc.hashtag) <- "HTO_maxID"
RidgePlot(pbmc.hashtag, assay = "HTO", features = rownames(pbmc.hashtag[["HTO"]])[1:2], ncol = 2)
Visualize pairs of HTO signals to confirm mutual exclusivity in singlets
FeatureScatter(pbmc.hashtag, feature1 = "hto_HTO-A", feature2 = "hto_HTO-B")
Compare number of UMIs for singlets, doublets and negative cells
Idents(pbmc.hashtag) <- "HTO_classification.global"
VlnPlot(pbmc.hashtag, features = "nCount_RNA", pt.size = 0.1, log = TRUE)
Generate a two dimensional tSNE embedding for HTOs.Here we are grouping cells by singlets and doublets for simplicity.
# First, we will remove negative cells from the object
pbmc.hashtag.subset <- subset(pbmc.hashtag, idents = "Negative", invert = TRUE)
# Calculate a tSNE embedding of the HTO data
DefaultAssay(pbmc.hashtag.subset) <- "HTO"
pbmc.hashtag.subset <- ScaleData(pbmc.hashtag.subset, features = rownames(pbmc.hashtag.subset),
verbose = FALSE)
pbmc.hashtag.subset <- RunPCA(pbmc.hashtag.subset, features = rownames(pbmc.hashtag.subset), approx = FALSE)
pbmc.hashtag.subset <- RunTSNE(pbmc.hashtag.subset, dims = 1:8, perplexity = 100)
DimPlot(pbmc.hashtag.subset)
# You can also visualize the more detailed classification result by running Idents(object) <-
# 'HTO_classification' before plotting. Here, you can see that each of the small clouds on the
# tSNE plot corresponds to one of the 28 possible doublet combinations.
Create an HTO heatmap, based on Figure 1C in the Cell Hashing paper.
# To increase the efficiency of plotting, you can subsample cells using the num.cells argument
HTOHeatmap(pbmc.hashtag, assay = "HTO", ncells = 5000)
Cluster and visualize cells using the usual scRNA-seq workflow, and examine for the potential presence of batch effects.
# Extract the singlets
pbmc.singlet <- subset(pbmc.hashtag, idents = "Singlet")
# Select the top 1000 most variable features
pbmc.singlet <- FindVariableFeatures(pbmc.singlet, selection.method = "mean.var.plot")
# Scaling RNA data, we only scale the variable features here for efficiency
pbmc.singlet <- ScaleData(pbmc.singlet, features = VariableFeatures(pbmc.singlet))
# Run PCA
pbmc.singlet <- RunPCA(pbmc.singlet, features = VariableFeatures(pbmc.singlet))
# We select the top 10 PCs for clustering and tSNE based on PCElbowPlot
pbmc.singlet <- FindNeighbors(pbmc.singlet, reduction = "pca", dims = 1:10)
pbmc.singlet <- FindClusters(pbmc.singlet, resolution = 0.6, verbose = FALSE)
pbmc.singlet <- RunTSNE(pbmc.singlet, reduction = "pca", dims = 1:10)
# Projecting singlet identities on TSNE visualization
DimPlot(pbmc.singlet, group.by = "HTO_classification")
# Read in UMI count matrix for RNA
hto12.umis <- readRDS("../data/hto12_umi_mtx.rds")
# Read in HTO count matrix
hto12.htos <- readRDS("../data/hto12_hto_mtx.rds")
# Select cell barcodes detected in both RNA and HTO
cells.use <- intersect(rownames(hto12.htos), colnames(hto12.umis))
# Create Seurat object and add HTO data
hto12 <- CreateSeuratObject(counts = hto12.umis[, cells.use], min.features = 300)
hto12[["HTO"]] <- CreateAssayObject(counts = t(x = hto12.htos[colnames(hto12), 1:12]))
# Normalize data
hto12 <- NormalizeData(hto12)
hto12 <- NormalizeData(hto12, assay = "HTO", normalization.method = "CLR")
hto12 <- HTODemux(hto12, assay = "HTO", positive.quantile = 0.99)
Distribution of selected HTOs grouped by classification, displayed by ridge plots
Visualize HTO signals in a heatmap
HTOHeatmap(hto12, assay = "HTO")
# Remove the negative cells
hto12 <- subset(hto12, idents = "Negative", invert = TRUE)
# Run PCA on most variable features
hto12 <- FindVariableFeatures(hto12, selection.method = "mean.var.plot")
hto12 <- ScaleData(hto12, features = VariableFeatures(hto12))
hto12 <- RunPCA(hto12)
hto12 <- RunTSNE(hto12, dims = 1:5, perplexity = 100)
DimPlot(hto12) + NoLegend()
Session Info
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04 LTS
##
## Matrix products: default
## BLAS/LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.8.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=C
## [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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] SeuratObject_4.0.0 Seurat_4.0.0
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-0 deldir_0.2-9
## [4] ellipsis_0.3.1 ggridges_0.5.3 rprojroot_2.0.2
## [7] fs_1.5.0 spatstat.data_1.7-0 farver_2.0.3
## [10] leiden_0.3.7 listenv_0.8.0 ggrepel_0.9.1
## [13] codetools_0.2-16 splines_4.0.3 cachem_1.0.3
## [16] knitr_1.31 polyclip_1.10-0 jsonlite_1.7.2
## [19] ica_1.0-2 cluster_2.1.0 png_0.1-7
## [22] uwot_0.1.10 shiny_1.6.0 sctransform_0.3.2
## [25] compiler_4.0.3 httr_1.4.2 assertthat_0.2.1
## [28] Matrix_1.2-18 fastmap_1.1.0 lazyeval_0.2.2
## [31] later_1.1.0.1 formatR_1.7 htmltools_0.5.1.1
## [34] tools_4.0.3 igraph_1.2.6 gtable_0.3.0
## [37] glue_1.4.2 RANN_2.6.1 reshape2_1.4.4
## [40] dplyr_1.0.4 Rcpp_1.0.6 spatstat_1.64-1
## [43] scattermore_0.7 pkgdown_1.6.1 vctrs_0.3.6
## [46] nlme_3.1-149 lmtest_0.9-38 xfun_0.20
## [49] stringr_1.4.0 globals_0.14.0 mime_0.9
## [52] miniUI_0.1.1.1 lifecycle_0.2.0 irlba_2.3.3
## [55] goftest_1.2-2 future_1.21.0 MASS_7.3-53
## [58] zoo_1.8-8 scales_1.1.1 ragg_0.4.1
## [61] promises_1.1.1 spatstat.utils_2.0-0 parallel_4.0.3
## [64] RColorBrewer_1.1-2 yaml_2.2.1 memoise_2.0.0
## [67] reticulate_1.18 pbapply_1.4-3 gridExtra_2.3
## [70] ggplot2_3.3.3 rpart_4.1-15 stringi_1.5.3
## [73] highr_0.8 desc_1.2.0 rlang_0.4.10
## [76] pkgconfig_2.0.3 systemfonts_1.0.0 matrixStats_0.58.0
## [79] evaluate_0.14 lattice_0.20-41 tensor_1.5
## [82] ROCR_1.0-11 purrr_0.3.4 labeling_0.4.2
## [85] patchwork_1.1.1 htmlwidgets_1.5.3 cowplot_1.1.1
## [88] tidyselect_1.1.0 parallelly_1.23.0 RcppAnnoy_0.0.18
## [91] plyr_1.8.6 magrittr_2.0.1 R6_2.5.0
## [94] generics_0.1.0 DBI_1.1.1 withr_2.4.1
## [97] mgcv_1.8-33 pillar_1.4.7 fitdistrplus_1.1-3
## [100] abind_1.4-5 survival_3.2-7 tibble_3.0.6
## [103] future.apply_1.7.0 crayon_1.4.0 KernSmooth_2.23-17
## [106] plotly_4.9.3 rmarkdown_2.6 grid_4.0.3
## [109] data.table_1.13.6 digest_0.6.27 xtable_1.8-4
## [112] tidyr_1.1.2 httpuv_1.5.5 textshaping_0.2.1
## [115] munsell_0.5.0 viridisLite_0.3.0