0.1.1

[0.1.1]

Fixed

• LFC no longer excludes genes whose arm means differ by less than
  thres_diff, which is now only the floor before the logarithm. The filter
  selected on the effect estimate: it dropped the pairs with p-values near 1
  (about 10% of expressed pairs on a Perturb-seq negative control), so BH over
  the remaining pairs was anti-conservative.
• Size factors (comp_size_factor, used by fit_gcate, LFC(offset=True)
  and gcate_lfc_batch) take the median of ratios over genes with mean count
  of at least min_mean=2 instead of every gene. In sparse single-cell data
  the old factors captured about a third of the depth variation; the rest
  shifted all highly expressed genes of an arm together, so their null
  statistics were too spread. On a Replogle negative control (30 fake
  perturbations cut from the controls) this cuts false discoveries from 114
  to 14, and the null SD for genes above 5 counts per cell from 1.32 to
  1.08. min_mean=0 restores the old factors; method='libsize' uses total
  counts. A cell with no counts in those genes gets its total count, rescaled
  to the other cells' factors, instead of a factor near the median.
• comp_size_factor(method='scale') returns one factor per row (total count
  over lib_size); it returned one value per column.
• fit_gcate now uses an offset array. The fit reads its offset from
  size_factor, which was set only for offset=True, so a supplied array was
  replaced by ones everywhere except the dispersion estimate.
• LFC shows its per-treatment progress bar only with verbose=True.

Added

• select_propensity_factors chooses the propensity covariates per
  treatment: for a treatment whose weights concentrate (treated ESS < 0.5,
  overlap < 0.3 or AUC > 0.9) it drops the covariates most imbalanced between
  that treatment and the controls until its support recovers, and returns a
  drop_by_treatment mapping for refit_propensity_scores. A treatment that
  already meets the target keeps every covariate; when the target cannot be
  reached, on_infeasible keeps the closest fit ('best', default), drops
  every candidate ('all') or none ('none').
• import causarray warns when it runs in an Intel (x86_64) Python translated
  by Rosetta 2 on an Apple Silicon Mac, where its numerical code runs several
  times slower (LFC on a 5,000-cell subsample: 159 s against 32-57 s
  natively). The README shows how to create a native environment.

Changed

• crispyx >= 0.1.6 is required. With latent factors in the outcome model,
  crispyx 0.1.5's structured GLM fitter was about 2.5 times slower than
  0.1.6's (same estimates): 159 s against 62 s for LFC on 5,000 cells, 3,000
  genes and 10 treatments.
• LFC(usevar='unequal') is the by-arm Welch variance again (0.1.0 made it an
  alias of 'pooled'). It is conservative for small case-control designs: on
  85 SEA-AD donors, permuted disease labels gave 0-1 false discoveries with
  null SD 0.6-0.9, against SD 1.1-1.8 under 'pooled'. 'pooled' stays the
  default; 'unequal' over-inflates the standard errors of rare treatments.