Pilot3: dropkick analysis
Christina Azodi
2022-03-01
Last updated: 2022-03-01
Checks: 5 2
Knit directory: BAUH_2020_MND-single-cell/analysis/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9eb9fcc | cazodi | 2022-03-01 | add rmd for dropkick and dropletqc |
| html | 9eb9fcc | cazodi | 2022-03-01 | add rmd for dropkick and dropletqc |
dropkick cellcalling
Dropkick is a fully automated software for QC and filtering scRNA-seq data with a focus on excluding ambient barcodes and recovering only real cells. By assigning training labels based on predictive global heuristics, dropkick learns a gene-based representation of real cells and ambient noise, calculating a cell probability score for each barcode (where a higher score means more likly a cell). Unlike EmptyDroplet, it does not set heuristic minimum thresholds for counts per cell.
Example results from the dropkick manuscript
Here is a QC summary and the results from applying dropkick to a pan T-cell dataset. Ideally, you want few genes with low dropout (if the gene is present in barcodes, it is very likely ambient):
(left) a profile of total counts (black trace) and genes (green points) detected per ranked barcode, with the percentage of mitochondrial (red) and ambient (blue) reads for each barcode included to denote quality along dataset profile. (right) The dropout rate per gene ranked where top ranked genes are present in all barcodes (i.e. likely ambient). Ambient genes are identified by dropkick and used to calculate ambient percentage in the left figure.
For the cell probability score you want to see a clear separation between barcodes with a low (ambient) vs high (cell) score.
The percent ambient counts versus arcsinh-transformed genes detected per barcode, with histogram distributions plotted on margins. Initial dropkick thresholds defining the training set are shown as dashed vertical lines. Each point (barcode) is colored by its final dropkick score after model fitting.
Reminder: pilot 2 results
In our pilot 2 study, there were over 1,000 genes had a dropout rate near zero! Indicating very high ambient read levels.
(QC: pilot 2)
Dropkick also had a difficult time separating ambient from cells.
Results: pilot2
iPSCs (Capture 5)
QC: Capture 5 (iPSCs)
Results: Capture 5 (iPSCs)
Summary: Clear separation between barcodes with high and low ratio of genes to counts, with many confident cell calls, but still a wide tail, suggesting many barcodes are difficult to classify.
Number of cells called:
bc_c5 <- scan(paste0(ipsc_dir, "Capture5-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt"),
what="character")
message("Capture 5: ", length(bc_c5))Capture 5: 40466MNs (Captures 1-4)
QC Plots
QC: Capture 1 (MNs)
QC: Capture 2 (MNs)
QC: Capture 3 (MNs)
QC: Capture 4 (MNs)
Results Plots
In all four MN captures, most barcodes were given a low dropkick score (<0.2).
Results: Capture 1 (MNs)
Results: Capture 2 (MNs)
Results: Capture 3 (MNs)
Results: Capture 4 (MNs)
bc_c1 <- scan(paste0(mn_dir, "Capture1-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt"),
what="character")
message("Capture 1: ", length(bc_c1))Capture 1: 3586bc_c2 <- scan(paste0(mn_dir, "Capture2-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt"),
what="character")
message("Capture 2: ", length(bc_c2))Capture 2: 3207bc_c3 <- scan(paste0(mn_dir, "Capture3-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt"),
what="character")
message("Capture 3: ", length(bc_c3))Capture 3: 2960bc_c4 <- scan(paste0(mn_dir, "Capture4-GEX/raw_feature_bc_matrix_dropkick_barcodes.txt"),
what="character")
message("Capture 4: ", length(bc_c4))Capture 4: 2793Compare ambient genes
amb1 <- fread(paste0(mn_dir, "Capture1-GEX/raw_feature_bc_matrix_dropkick_ambient-features.txt"),
sep=",")
amb2 <- fread(paste0(mn_dir, "Capture2-GEX/raw_feature_bc_matrix_dropkick_ambient-features.txt"),
sep=",")
amb3 <- fread(paste0(mn_dir, "Capture3-GEX/raw_feature_bc_matrix_dropkick_ambient-features.txt"),
sep=",")
amb4 <- fread(paste0(mn_dir, "Capture4-GEX/raw_feature_bc_matrix_dropkick_ambient-features.txt"),
sep=",")
amb5 <- fread(paste0(ipsc_dir, "Capture5-GEX/raw_feature_bc_matrix_dropkick_ambient-features.txt"),
sep=",")
geneList <- list(Capture1 = amb1$gene_ids,
Capture2 = amb2$gene_ids,
Capture3 = amb3$gene_ids,
Capture4 = amb4$gene_ids)
ggvenn(geneList, stroke_size = 0.5, set_name_size = 4, text_size = 3,
fill_color = c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")) 
Venn diagram of overlap in top 100 ambient genes for each MN capture.
| Version | Author | Date |
|---|---|---|
| 9eb9fcc | cazodi | 2022-03-01 |
geneList2 <- list(MN_Captures = unique(c(amb1$gene_ids, amb2$gene_ids,
amb3$gene_ids, amb4$gene_ids)),
Capture5 = amb5$gene_ids)
ggvenn(geneList2, stroke_size = 0.5, set_name_size = 4, text_size = 3,
fill_color = c("#0073C2FF", "#EFC000FF", "#868686FF", "#CD534CFF")) 
Venn diagram of overlap in top 100 ambient genes from iPSCs with top 100 ambient genes from all 4 MN captures combined.
| Version | Author | Date |
|---|---|---|
| 9eb9fcc | cazodi | 2022-03-01 |
sessionInfo()R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux 8.5 (Ootpa)
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.12.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] grid stats graphics grDevices utils datasets methods
[8] base
other attached packages:
[1] ComplexHeatmap_2.10.0 ggvenn_0.1.9 ggplot2_3.3.5
[4] data.table_1.14.2 tidyr_1.1.4 dplyr_1.0.7
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