Some improvements in the QC plotting and LIANA for large datasets#304
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nictru wants to merge 21 commits into
Open
Some improvements in the QC plotting and LIANA for large datasets#304nictru wants to merge 21 commits into
nictru wants to merge 21 commits into
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Expose a plot toggle that draws pre-filter metric histograms with median and threshold lines, enabled in quality control and wired into MultiQC.
Use colour for lower versus upper bounds and linestyle for MAD versus absolute thresholds, plotting and labelling every configured bound.
Check post-filter AnnData with nf-anndata in the quality control subworkflow, skipping validation during stub runs.
Stack green and red bars using the same cell-retention mask as the filter steps so removed cells are visible in each metric plot.
Collect per-stage sample sizes in the quality control subworkflow via countCells() and remove the unused nf-core getsize module.
Expose liana_max_cells, subsample strategy/seed, and liana_n_perms so large runs can cap cells and reduce permutation cost without skipping LIANA entirely.
Emit one PNG and MultiQC entry per sample instead of eight separate metric plots.
…exist. Reuse filter-style mito annotation and pass symbol column and mito gene list from quality control.
Each panel now marks cells green or red using only that metric's thresholds so plots match individual filter criteria.
Pre-unify QC and filter modules use var_names when symbol_col is none so gene symbols are not read from a missing column.
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Absolute mitochondrial filtering is off unless a sample sets a value below 100; MAD mito QC is unchanged.
Drop file-path format so the Zenodo tarball URL validates alongside local model paths.
Mirror base_condition_col so replicate identifiers can be read from a custom obs column on base-only runs while samplesheet paths still use unified donor.
Deep flattening split each four-tuple into separate emissions and broke multiMap argument binding per cell type.
Use an explicit design matrix, sample IDs, TMM normalization, and integer coef indices instead of formula-based fit and string contrasts.
Pass formula first and metadata second to match edgePython 0.2.6 model_matrix(formula, data).
Fit ~ donor + condition only when donors span multiple conditions so PyDESeq2 and edgePython avoid singular designs.
Rebuild Wave containers and conda lock files for the updated bioconda environment.
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