Cell hashing test quality control
Katharina Hembach
5/14/2021
Last updated: 2021-05-14
Checks: 7 0
Knit directory: neural_scRNAseq/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | d03901f | khembach | 2021-05-14 | preprocessing and quality control of HTO test experiment |
Load packages
library(DropletUtils)
library(BiocParallel)
library(ggplot2)
library(scater)
library(readxl)
library(Seurat)
library(scales)
library(viridis)
library(dplyr)Importing CellRanger output and metadata
fs <- file.path("data", "cell_hashing_test",
"CellRangerCount_57443_2021-05-12--11-37-28", "HashTag_test",
"filtered_feature_bc_matrix.h5")
names(fs) <- "cell_hashing_test"
sce_raw <- read10xCounts(samples = fs)
# rename colnames and dimnames
rowData(sce_raw)$Type <- NULL
names(rowData(sce_raw)) <- c("ensembl_id", "symbol")
names(colData(sce_raw)) <- c("sample_id", "barcode")
sce_raw$sample_id <- factor(sce_raw$sample_id)
# load metadata
meta <- read_excel(file.path("data", "cell_hashing_test", "SampleName_feature_ref_MHP.xlsm"))
m <- match(meta$name, rowData(sce_raw)$symbol)
## separate gene counts from HTO counts
rowData(sce_raw) %>% tailDataFrame with 6 rows and 2 columns
ensembl_id symbol
<character> <character>
ENSG00000276017 ENSG00000276017 AC007325.1
ENSG00000278817 ENSG00000278817 AC007325.4
ENSG00000277196 ENSG00000277196 AC007325.2
B0253 B0253 Hashtag3
B0254 B0254 Hashtag4
B0257 B0257 Hashtag7sce <- sce_raw[-m,]
dimnames(sce) <- list(with(rowData(sce), paste(ensembl_id, symbol, sep = ".")),
with(colData(sce), paste(barcode, sample_id, sep = ".")))Quality control
We compute cell-level QC.
# remove empty rows
sce <- sce[rowSums(counts(sce) > 0) > 0, ]
dim(sce)[1] 18824 17631(mito <- grep("MT-", rownames(sce), value = TRUE)) [1] "ENSG00000210049.MT-TF" "ENSG00000211459.MT-RNR1"
[3] "ENSG00000210077.MT-TV" "ENSG00000210082.MT-RNR2"
[5] "ENSG00000209082.MT-TL1" "ENSG00000198888.MT-ND1"
[7] "ENSG00000210100.MT-TI" "ENSG00000210107.MT-TQ"
[9] "ENSG00000210112.MT-TM" "ENSG00000198763.MT-ND2"
[11] "ENSG00000210117.MT-TW" "ENSG00000210127.MT-TA"
[13] "ENSG00000210135.MT-TN" "ENSG00000210140.MT-TC"
[15] "ENSG00000210144.MT-TY" "ENSG00000198804.MT-CO1"
[17] "ENSG00000210151.MT-TS1" "ENSG00000210154.MT-TD"
[19] "ENSG00000198712.MT-CO2" "ENSG00000210156.MT-TK"
[21] "ENSG00000228253.MT-ATP8" "ENSG00000198899.MT-ATP6"
[23] "ENSG00000198938.MT-CO3" "ENSG00000210164.MT-TG"
[25] "ENSG00000198840.MT-ND3" "ENSG00000210174.MT-TR"
[27] "ENSG00000212907.MT-ND4L" "ENSG00000198886.MT-ND4"
[29] "ENSG00000210176.MT-TH" "ENSG00000210184.MT-TS2"
[31] "ENSG00000210191.MT-TL2" "ENSG00000198786.MT-ND5"
[33] "ENSG00000198695.MT-ND6" "ENSG00000210194.MT-TE"
[35] "ENSG00000198727.MT-CYB" "ENSG00000210195.MT-TT"
[37] "ENSG00000210196.MT-TP" sce <- addPerCellQC(sce, subsets = list(Mt = mito))
# we compute the fraction of mitochondrial genes and the logit of it
sce$subsets_Mt_fraction <- (sce$subsets_Mt_percent + 0.001) /100
sce$subsets_Mt_fraction_logit <- qlogis(sce$subsets_Mt_fraction + 0.001)
# library size
summary(sce$sum) Min. 1st Qu. Median Mean 3rd Qu. Max.
500 642 862 5216 5818 96619 # number of detected genes per cell
summary(sce$detected) Min. 1st Qu. Median Mean 3rd Qu. Max.
302 491 609 1650 2540 9081 # percentage of counts that come from mitochondrial genes:
summary(sce$subsets_Mt_percent) Min. 1st Qu. Median Mean 3rd Qu. Max.
0.01666 3.86824 7.70896 9.93049 13.29026 87.60776 Diagnostic plots
The number of counts per cell:
plotColData(sce, x = "sample_id", y = "sum") + scale_y_log10()
The number of genes:
plotColData(sce, x = "sample_id", y = "detected") + scale_y_log10() 
The percentage of mitochondrial genes:
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent")
We plot the total number of counts against the number of detected genes and color by the fraction of mitochondrial genes:
cd <- data.frame(colData(sce))
ggplot(cd, aes(x = sum, y = detected, color = subsets_Mt_fraction)) +
geom_point(alpha = 0.7) +
geom_density_2d(color = "grey", bins = 6) +
scale_x_log10() +
scale_y_log10() +
facet_wrap(~sample_id) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab("sum of counts") +
ylab("number of detected genes") +
labs(color = "mitochondrial fraction") +
scale_color_viridis(trans = "logit", breaks = c(0.01, 0.1, 0.25, 0.5, 0.75))
We plot the total number of counts against the mitochondrial content. Well-behaved cells should have many expressed genes and a low fraction of mitochondrial genes. High mitochondrial content indicates empty or damaged cells.
ggplot(cd, aes(x = sum, y = subsets_Mt_fraction)) +
geom_point(color = "darkgrey", alpha = 0.3) +
geom_density_2d(color = "lightblue") +
scale_x_log10() +
scale_y_continuous(trans = 'logit',
breaks = c(0.01, 0.05, 0.1, 0.2, 0.5, 0.75)) +
facet_wrap(~sample_id) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
xlab("sum of counts") +
ylab("logit(mitochondrial fraction)")
Identification of outlier cells
Based on the QC metrics, we now identify outlier cells:
cols <- c("sum", "detected", "subsets_Mt_percent")
log <- c(TRUE, TRUE, FALSE)
type <- c("lower", "lower", "higher")
drop_cols <- paste0(cols, "_drop")
for (i in seq_along(cols))
colData(sce)[[drop_cols[i]]] <- isOutlier(sce[[cols[i]]],
nmads = 1, type = type[i], log = log[i], batch = sce$sample_id)
# Overlap of outlier cells from two metrics
sapply(drop_cols, function(i)
sapply(drop_cols, function(j)
sum(sce[[i]] & sce[[j]]))) sum_drop detected_drop subsets_Mt_percent_drop
sum_drop 0 0 0
detected_drop 0 53 49
subsets_Mt_percent_drop 0 49 3900colData(sce)$discard <- rowSums(data.frame(colData(sce)[,drop_cols])) > 0
table(colData(sce)$discard)
FALSE TRUE
13727 3904 ## Plot the metrics and highlight the discarded cells
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
## we manually filter filter the cells with less than 2000 UMIs
colData(sce)$manual_discard_sum <- colData(sce)$sum < 1000
## filter the cells with less than 800 detected genes
colData(sce)$manual_discard_detected <- colData(sce)$detected < 800
## highlight all manually discarded cells
colData(sce)$manual_discard <- colData(sce)$manual_discard_sum |
colData(sce)$manual_discard_detected
plotColData(sce, x = "sample_id", y = "sum", colour_by = "manual_discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "detected", colour_by = "manual_discard") +
scale_y_log10()
## highlight all discarded cells
colData(sce)$discard <- colData(sce)$manual_discard |
colData(sce)$discard
plotColData(sce, x = "sample_id", y = "detected", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "sum", colour_by = "discard") +
scale_y_log10()
plotColData(sce, x = "sample_id", y = "subsets_Mt_percent",
colour_by = "discard")
table(colData(sce)$discard)
FALSE TRUE
5542 12089 We discard the outlier cells
dim(sce)[1] 18824 17631sce_filtered <- sce[,!sce$discard]
dim(sce_filtered)[1] 18824 5542Create Seurat object and split gene and HTO counts
## convert DDelayedMatrix to dgCMatrix for import into Seurat object
counts <- as(counts(sce_filtered, withDimnames = FALSE), "dgCMatrix")
colnames(counts) <- colnames(counts(sce_filtered))
rownames(counts) <- rownames(counts(sce_filtered))
so <- CreateSeuratObject(
counts = counts,
meta.data = data.frame(colData(sce_filtered)),
project = "cell_hashing_test")Warning: Feature names cannot have underscores ('_'), replacing with dashes
('-')## add HTO data as independent assay
hto_counts <- as(counts(sce_raw, withDimnames = FALSE)[m,with(colData(sce),
paste(barcode, sample_id, sep = ".")) %in%
colnames(sce_filtered)], "dgCMatrix")
colnames(hto_counts) <- colnames(sce_filtered)
rownames(hto_counts) <- rownames(sce_raw)[m]
so[["HTO"]] <- CreateAssayObject(counts = hto_counts)Data normalization
DefaultAssay(so) <- "RNA"
# Normalize RNA data with log normalization
so <- NormalizeData(so)
# Find and scale variable features
so <- FindVariableFeatures(so, selection.method = "mean.var.plot")
so <- ScaleData(so, features = VariableFeatures(so))
# Normalize HTO data, here we use centered log-ratio (CLR) transformation
so <- NormalizeData(so, assay = "HTO", normalization.method = "CLR")Demultiplex cells based on HTO enrichment
so <- HTODemux(so, assay = "HTO", positive.quantile = 0.99)Visualize results
# Global classification results
table(so$HTO_classification.global)
Doublet Negative Singlet
656 2119 2767 # Group cells based on the max HTO signal
Idents(so) <- "HTO_maxID"
# Group cells based on the max HTO signal
RidgePlot(so, assay = "HTO", features = rownames(so[["HTO"]]), ncol = 3)
# Visualize pairs of HTO signals to check mutual exclusivity in singlets
DefaultAssay(object = so) <- "HTO"
FeatureScatter(so, feature1 = "B0253", feature2 = "B0254")
FeatureScatter(so, feature1 = "B0257", feature2 = "B0254")
FeatureScatter(so, feature1 = "B0253", feature2 = "B0257")
## compare number of UMIs for singlet's, doublets and negative cells
Idents(so) <- "HTO_classification.global"
VlnPlot(so, features = "nCount_HTO", pt.size = 0.1, log = TRUE)
VlnPlot(so, features = "nCount_RNA", pt.size = 0.1, log = TRUE)
## tSNE for HTOs
# First, we will remove negative cells from the object
subs <- subset(so, idents = "Negative", invert = TRUE)
subs$HTO_classification %>% table.
B0253 B0253_B0254 B0253_B0257 B0254 B0254_B0257 B0257
830 236 198 939 222 998 # Calculate a tSNE embedding of the HTO data
DefaultAssay(subs) <- "HTO"
subs <- ScaleData(subs, features = rownames(subs),
verbose = FALSE)
subs <- RunPCA(subs, features = rownames(subs), approx = FALSE)Warning in print.DimReduc(x = reduction.data, dims = ndims.print, nfeatures =
nfeatures.print): Only 3 dimensions have been computed.Warning: Requested number is larger than the number of available items (3).
Setting to 3.
Warning: Requested number is larger than the number of available items (3).
Setting to 3.
Warning: Requested number is larger than the number of available items (3).
Setting to 3.subs <- RunTSNE(subs, dims = 1:3, perplexity = 100)
Idents(subs) <- "HTO_classification.global"
DimPlot(subs)
Idents(subs) <- 'HTO_classification'
DimPlot(subs)
# HTO heatmap
HTOHeatmap(so, assay = "HTO", ncells = 5000)
Cluster based on gene counts and visualize cells
DefaultAssay(so) <- "RNA"
# Extract the singlets
subs <- subset(so, idents = "Singlet")
subs$HTO_classification %>% table.
B0253 B0254 B0257
830 939 998 # Select the top 1000 most variable features
subs <- FindVariableFeatures(subs, selection.method = "mean.var.plot")
# Scaling RNA data, we only scale the variable features here for efficiency
subs <- ScaleData(subs, features = VariableFeatures(subs))
# Run PCA
subs <- RunPCA(subs, features = VariableFeatures(subs))
# We select the top 10 PCs for clustering and tSNE based on PCElbowPlot
subs <- FindNeighbors(subs, reduction = "pca", dims = 1:10)
subs <- FindClusters(subs, resolution = 0.6, verbose = FALSE)
subs <- RunTSNE(subs, reduction = "pca", dims = 1:10)
subs <- RunUMAP(subs, reduction = "pca", dims = 1:10)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 session# Projecting singlet identities on TSNE visualization
DimPlot(subs, group.by = "HTO_classification", reduction = "tsne")
DimPlot(subs, group.by = "HTO_classification", reduction = "umap")
Save data to RDS
saveRDS(sce, file.path("output", "CH-test-01-preprocessing.rds"))
sessionInfo()R version 4.0.5 (2021-03-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-4.0.5/lib/libRblas.so
LAPACK: /usr/local/R/R-4.0.5/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] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] dplyr_1.0.2 viridis_0.5.1
[3] viridisLite_0.3.0 scales_1.1.1
[5] SeuratObject_4.0.1 Seurat_4.0.1
[7] readxl_1.3.1 scater_1.16.2
[9] ggplot2_3.3.2 BiocParallel_1.22.0
[11] DropletUtils_1.8.0 SingleCellExperiment_1.10.1
[13] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
[15] matrixStats_0.56.0 Biobase_2.48.0
[17] GenomicRanges_1.40.0 GenomeInfoDb_1.24.2
[19] IRanges_2.22.2 S4Vectors_0.26.1
[21] BiocGenerics_0.34.0 workflowr_1.6.2
loaded via a namespace (and not attached):
[1] backports_1.1.9 plyr_1.8.6
[3] igraph_1.2.5 lazyeval_0.2.2
[5] splines_4.0.5 listenv_0.8.0
[7] scattermore_0.7 digest_0.6.25
[9] htmltools_0.5.0 magrittr_1.5
[11] tensor_1.5 cluster_2.1.0
[13] ROCR_1.0-11 limma_3.44.3
[15] globals_0.12.5 R.utils_2.9.2
[17] spatstat.sparse_2.0-0 colorspace_1.4-1
[19] rappdirs_0.3.1 ggrepel_0.8.2
[21] xfun_0.15 crayon_1.3.4
[23] RCurl_1.98-1.3 jsonlite_1.7.2
[25] spatstat.data_2.1-0 survival_3.2-3
[27] zoo_1.8-8 glue_1.4.2
[29] polyclip_1.10-0 gtable_0.3.0
[31] zlibbioc_1.34.0 XVector_0.28.0
[33] leiden_0.3.3 BiocSingular_1.4.0
[35] Rhdf5lib_1.10.0 future.apply_1.6.0
[37] HDF5Array_1.16.1 abind_1.4-5
[39] edgeR_3.30.3 miniUI_0.1.1.1
[41] Rcpp_1.0.5 isoband_0.2.2
[43] xtable_1.8-4 reticulate_1.16
[45] spatstat.core_2.1-2 dqrng_0.2.1
[47] rsvd_1.0.3 htmlwidgets_1.5.1
[49] httr_1.4.2 RColorBrewer_1.1-2
[51] ellipsis_0.3.1 ica_1.0-2
[53] farver_2.0.3 pkgconfig_2.0.3
[55] R.methodsS3_1.8.0 uwot_0.1.10
[57] deldir_0.2-10 locfit_1.5-9.4
[59] labeling_0.3 tidyselect_1.1.0
[61] rlang_0.4.10 reshape2_1.4.4
[63] later_1.1.0.1 munsell_0.5.0
[65] cellranger_1.1.0 tools_4.0.5
[67] generics_0.0.2 ggridges_0.5.2
[69] evaluate_0.14 stringr_1.4.0
[71] fastmap_1.0.1 goftest_1.2-2
[73] yaml_2.2.1 knitr_1.29
[75] fs_1.5.0 fitdistrplus_1.1-1
[77] purrr_0.3.4 RANN_2.6.1
[79] nlme_3.1-148 pbapply_1.4-2
[81] future_1.17.0 whisker_0.4
[83] mime_0.9 R.oo_1.23.0
[85] compiler_4.0.5 beeswarm_0.2.3
[87] plotly_4.9.2.1 png_0.1-7
[89] spatstat.utils_2.1-0 tibble_3.0.3
[91] stringi_1.4.6 RSpectra_0.16-0
[93] lattice_0.20-41 Matrix_1.3-3
[95] vctrs_0.3.4 pillar_1.4.6
[97] lifecycle_1.0.0 spatstat.geom_2.1-0
[99] lmtest_0.9-37 RcppAnnoy_0.0.18
[101] BiocNeighbors_1.6.0 data.table_1.12.8
[103] cowplot_1.0.0 bitops_1.0-6
[105] irlba_2.3.3 httpuv_1.5.4
[107] patchwork_1.0.1 R6_2.4.1
[109] promises_1.1.1 KernSmooth_2.23-17
[111] gridExtra_2.3 vipor_0.4.5
[113] codetools_0.2-16 MASS_7.3-51.6
[115] rhdf5_2.32.2 rprojroot_1.3-2
[117] withr_2.4.1 sctransform_0.3.2
[119] GenomeInfoDbData_1.2.3 mgcv_1.8-31
[121] beachmat_2.4.0 rpart_4.1-15
[123] grid_4.0.5 tidyr_1.1.0
[125] rmarkdown_2.3 DelayedMatrixStats_1.10.1
[127] Rtsne_0.15 git2r_0.27.1
[129] shiny_1.5.0 ggbeeswarm_0.6.0