feat(cell_metrics): move per-cell gene metrics into the cell summary, drop report re-derivation - #36
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Compute the annotated/novel read counts and gene read-count bins once in
cell_metrics.py (both modes) and store them as cell-summary columns, then
have the report read them instead of re-loading and re-deriving from the
classification. Plot outputs are unchanged.
- Add Annotated_genes_reads / Novel_genes_reads and gene bin counts
{anno,novel}_gene_reads_bin_{1,2_5,6_9,10plus} to the summary in reads
and isoforms modes. Store raw COUNTS, not percentages: the report derives
all three views (grouped annotated/novel, all-genes, annotated-only).
- Rewire SQANTI-sc_report.R to read these columns and delete its own
re-derivations: the isoforms CB/FL explosion (an OOM source), the
reads-mode groupby, and the expand_isoform_counts / gene_bin_label
helpers. Removes the isoforms report-side memory blowup and the redundant
reprocessing in both modes.
- Retire the old {anno,novel}_bin*_perc columns: never read by the report
and mis-binned (2-4/5-9) vs the plots' boundaries (2-5/6-9).
The gene columns added in the previous commit used the _reads suffix in both modes. Align them with the tool's existing mode-dependent convention (Reads_in_cell vs Transcripts_in_cell): keep _reads in reads mode, and in isoforms mode use _transcripts, since the per-cell counter there is transcript-level (sum of FL).
CarlosBlancoGo
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Jul 16, 2026
…que-isoform counts (#37) Replace the 4 gene abundance bins (1 / 2-5 / 6-9 / >=10) with 11 gapless half-open bins (0,1]..(9,10],(10,inf). The old boundaries had gaps between each value so non-integer abundances silently fell through and the gene vanished from the panel. These float non-integer abundances can come from non-exact isoform quantification approaches like EM or deterministic approaches to resolve ambiguities. Integer counts bin as before. - Add anno_gene_isoforms_bin_* (isoforms mode): distinct models per gene per cell, from the COO group sizes in the existing sparse pass, so it costs no extra explosion. - Fix the gene-bin percentage, which used ifelse(sum(n) > 0, 100*n/sum(n), 0) in a mutate grouped by CB. ifelse() returns a result the length of its test; the test is scalar here, so it yielded only bin_1's percentage and dplyr recycled it across every bin. Introduced in 6f1f8de (#36), so main is affected. Now computed in wide form (rowSums over a cells x nbin matrix, then stacked) -- order-safe, no grouped ifelse. - Figures: drop grouped Known/Novel violins. Annotated-only in both modes with labels adapting to integer vs fractional quantification; rebuild Unique Isoform Count plot. One shared build_gene_bin_violin() helper. - Stop computing novel-gene bin columns; no plot consumes them. Co-authored-by: CarlosBlancoGo <carblan@NGC224>
CarlosBlancoGo
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Jul 20, 2026
…cating rows (#42) The per-cluster length-distribution violins re-derived their data by exploding the classification CB/FL lists to per-(transcript, cell) rows and then replicating each row FL times (rep(seq_len(n), times = FL)) so every read became a literal row. In isoforms mode the replicated frame is the number of reads -- the same per-read explosion that #35, #36, #40 and #41 removed elsewhere by moving metrics off the report's on-the-fly re-derivation. Aggregate instead to per-(transcript, cluster) with weight = sum(FL), and drive the violin with the native geom_violin weight aesthetic, plus a weighted mean and a weighted five-number box summary computed from a small weighted-quantile helper. This reproduces the FL-replicated distribution exactly at a fraction of the rows: on the test set the weighted mean matches the replicated mean to full precision and the quantiles to within interpolation noise (invisible on the log-length axis); the rendered violins are indistinguishable in both modes. The weighted-quantile helper also handles clusters/category subsets with fewer than two points (a single transcript collapses to a degenerate box) so sparse per-category panels no longer abort the whole length-by-cluster block via the outer tryCatch.
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Summary
cell_metrics.py._perccolumns.Details
Reads_in_cell/Transcripts_in_cell):Annotated_genes_reads,Novel_genes_reads,{anno,novel}_gene_reads_bin_{1,2_5,6_9,10plus}_transcriptsSQANTI-sc_report.Rnow always reads the columns; deletedexpand_isoform_counts, bothgenes_by_cbpaths, andgene_bin_label.{anno,novel}_bin*_perccolumns were never read by the report and used boundaries (2-4 / 5-9) that disagreed with the plotted boundaries (2-5 / 6-9). Replaced by the counts columns with correct boundaries.Out of scope