An overview:
flowchart LR;
subgraph preprocess
cr_raw[(cellranger raw)]-.->eD[\emptyDrops\];
dnbe_raw[(dnbelab raw)]-.->eD;
cr_raw-->cb[\cellBender\];
dnbe_raw-->cb;
eD-.->scb[\scrublet\];
cb-->scb;
scb-->cr_filter[(cellranger filtered)];
scb-->dnbe_filter[(dnbelab filtered)]
end
subgraph integrate
cr_filter-->scVI[\scVI\]
dnbe_filter-->scVI
end
subgraph downstream
scVI-->scanpy["`cell type
features analysis
variance analysis`"]
end
/upstream_stats: reports from cellranger count and dnbelab run
/h5_input: h5 matrices from calling cell with EmptyDrops or Cellbender + cellranger count or dnbelab run
/h5_analysis: general qc before and after scrublet
with the same parameters for EmptyDrops,
- dnbelab retain double the number of nuclei
- but those more retained nuclei are with less gene numbers
Are these more retained nuclei are good biological samplings from the tissue?
in terms of cell type identity first
This lead to our next step:
- clustering and viriance analysis
- cell type markers
- integrate matrices from both platforms with scVI