We would like to integrate QC into the DCTA for hra-pop v1.1. We met with https://dong.umn.edu/josh-bartz today and discussed how to best use the QC metrics from HRApop v1.0 (run after the fact for the paper) BETWEEN the download and the annotation phase.
Full notes are at https://docs.google.com/document/d/1limSllUBYbc2rG9NRR2EgBdl5vIBzQwlREr7U6KwFsQ/edit?tab=t.0#heading=h.6qgqpwl52b7f
@bherr2 said we will run all scripts through https://github.com/hubmapconsortium/hra-workflows-runner/blob/main/scripts/11-download.sh and output a QC ZIP file with one folder per datasets. Then @andreasbueckle will make plots and reports from it.
Metrics needed:
Dataset level:
Cell level:
pct_counts_mito (%mitochondrial genes)total_counts (total gene counts)total_genes_by_counts (number of genes with positive counts in a cell)
We would like to integrate QC into the DCTA for hra-pop v1.1. We met with https://dong.umn.edu/josh-bartz today and discussed how to best use the QC metrics from HRApop v1.0 (run after the fact for the paper) BETWEEN the download and the annotation phase.
Full notes are at https://docs.google.com/document/d/1limSllUBYbc2rG9NRR2EgBdl5vIBzQwlREr7U6KwFsQ/edit?tab=t.0#heading=h.6qgqpwl52b7f
@bherr2 said we will run all scripts through https://github.com/hubmapconsortium/hra-workflows-runner/blob/main/scripts/11-download.sh and output a QC ZIP file with one folder per datasets. Then @andreasbueckle will make plots and reports from it.
Metrics needed:
Dataset level:
Cell level:
pct_counts_mito(%mitochondrial genes)total_counts(total gene counts)total_genes_by_counts(number of genes with positive counts in a cell)