BAUH: MND scRNA-seq pilot 2: dropkick
C.B. Azodi
2021-09-27
Last updated: 2021-09-27
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Knit directory: BAUH_2020_MND-single-cell/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 9fa3ac2 | cazodi | 2021-08-06 | dropkick figures |
| html | 9fa3ac2 | cazodi | 2021-08-06 | dropkick figures |
dropkick cellcalling
Dropkick is a fully automated software tool for quality control and filtering scRNA-seq data with a focus on excluding ambient barcodes and recovering only real cells. By automatically determining dataset-specific 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. Unlike EmptyDroplet, it does not set heuristic minimum thresholds for counts per cell.
Here is an example of dropkick results on a pan T-cell dataset from the manuscript:
(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.
Now here are the results from the pilot #2 data (unfixed):
(same as above)
A major difference is the large number of genes in our data that have a 0% or close to 0% dropout rate. Genes with such low dropout are highly likely to be ambient noise! In our dataset, nearly the first 1000 genes have an dropout rate less than the most ambient gene in the example data!
dropkick results
Dropkick then learns a cell model and assigns each barcode a dropkick score, where a high score indicates a likely cell. The figure below shows 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.
dropkick results
Compared to the example results from the manuscript below, we see that there is a less well defined boundary between cells and empty barcodes, which we expected based on the initial knee plots.
example dropkick results
Dropkick called 11,019 barcodes as cells from the unfixed experiment, nearly twice as many as called by EmptyDroplets (n=5,695)
We also ran dropkick on the fixed cells, which returned only 107 cells, even less than EmptyDroplet (n=609). Given we expected the fixed cells to be low quality, this seems promising.
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