truecell 1.1.0

Fourteen pull requests since 1.0.0. Backwards compatible — nothing removed or
renamed — so the additions are safe, and the reasons to upgrade are the
corrections.

Fixed — these are live in 1.0.0 today

The categorical palette repeated colours, so two clusters could render identically	#86
vln_plot drew the wrong violin three ways — bandwidth, trim, and points	#89
aggregate_expression(return_object=True) left raw sums in data where Seurat log-normalizes (14 against 6.98)	#93
_get_expression_matrix returned the wrong layer when asked for scale.data, and labelled the right one with the wrong features	#93
The pbmc3k annotated UMAP captioned the platelet cluster "DC"	#98
feature_plot alone rasterised its points, and did so unconditionally	#86

If you run pseudobulk, read a violin, or trust a cluster colour, 1.0.0 is giving
you something wrong.

Added

• average_expression, mirroring Seurat's AverageExpression — the
  back-transformed per-group mean, distinct from aggregate_expression's sums.
• find_clusters accepts several resolutions and writes a
  {graph}_res.{r} column per resolution, as FindClusters does. Choosing a
  resolution means comparing a few.
• split_by on dim_plot, feature_plot and vln_plot.
• A theme layer — set_theme, theme_context, get_theme, reset_theme.
• ridge_plot warns when an explicit figsize cannot fit the groups.

Performance

find_markers computes its min_pct and logfc_threshold masks on the sparse
matrix and densifies only the survivors, instead of building a dense
genes × cells array per group first. Marker tables are byte-identical:

dataset (clusters)	time	peak memory
ifnb (15)	25.37s → 8.03s	9420 MB → 3580 MB
pbmc8k (9)	12.71s → 6.32s	9253 MB → 2406 MB
thp1 (7)	45.52s → 23.87s	10620 MB → 9428 MB

A cross-language benchmark suite against R Seurat landed alongside it —
tutorials/benchmark/PERFORMANCE.md.

Verification

• add_module_score is now verified against Seurat as an equality, not a
  correlation. At nbin=1 with ctrl = pool size, sample(n, n) is a
  permutation, so the control set is forced and the two tools agree to
  6.66e-15 across 20,729 cells — against 1.8e-01 between two R seeds.
• The guided tutorial scans four resolutions, each scored against Seurat.
  Cluster counts match exactly at 0.4, 0.8 and 1.2; the 8-vs-9 split is specific
  to 0.5.
• CI now runs the PBMC 3k tutorials against real data, with a skip counted
  as a failure. The 1.0.0 mislabelling above shipped under a guard that was
  correct and had never executed.
• Float64 round-trips fixed across every tutorial's R↔Python handoff.

Install

pip install --upgrade truecell

Full detail in CHANGELOG.md.