Changelog

Source: NEWS.md

smartid 0.99.0

  • Added a NEWS.md file to track changes to the package.

smartid 0.99.1

  • Ready for submission to Bioconductor.

smartid 0.99.2

smartid 0.99.3

  • Bump R version dependency to >= 4.4 and add details for TF, IDF, IAE functions.

smartid 0.99.4

  • Add details for TF, IDF, IAE functions.

smartid 0.99.5

  • Update scale_mgm() function adding pooled SD option, add details for scale function.

smartid 1.1.1

  • Update cal_score() function to convert input sparse matrix into dense matrix.

smartid 1.1.2

  • Update marker selection functions to fix wrong names of marker list.

smartid 1.3.1

  • Update top_markers function to allow batch correction for glm method.

smartid 1.3.2

  • Update batch param in top_markers function.

smartid 1.7.1

  • Add bioRxiv citation.

smartid 1.7.2

  • Fix dplyr defunct.

smartid 1.7.3

  • Major memory and performance optimization for cal_score() and top_markers(). On a 20,000 gene x 100,000 cell sparse input peak memory drops from roughly 100 GB to a few GB, and top_markers() with the default gaussian() family runs in seconds instead of hours.
  • Breaking change: cal_score() no longer stores the intermediate tf, idf and iae matrices in metadata() by default. Callers that relied on metadata(se)$tf / $idf / $iae (introduced in v1.1.1) must now pass return.intermediate = TRUE. When the flag is TRUE, the stored idf / iae for labelled methods (prob, rf) are now compact G x K matrices (columns = unique labels); expand with md$idf[, as.character(label)] to recover the legacy per-cell form.
  • Internal refactor: labelled idf_prob, idf_rf, iae_prob and iae_rf helpers now return a compact G x K matrix. cal_score() composes the final score through per-group column-block multiplication, avoiding the materialisation of full G x N intermediates.
  • cal_score() no longer forces dense conversion of dgCMatrix inputs; the score assay stays sparse throughout the pipeline when the input is sparse.
  • tf(), idf_hdb(), iae_hdb() and all IAE helpers now preserve dgCMatrix sparsity by routing column scaling through Matrix::Diagonal and replacing the densifying x[x < 0] <- 0 pattern with pmax0_offset().
  • top_markers_glm() has a vectorised closed-form least-squares fast path for the default gaussian() + identity link; non-gaussian families or rank-deficient designs automatically fall back to the legacy per-gene glm() loop with no behaviour change.
  • top_markers_abs() aggregates directly on the scored matrix via sparseMatrixStats::rowMeans2 / rowMedians / rowMads, removing the intermediate wide data.frame that previously reached tens of GB.
  • scale_mgm() caches per-group column indices and collapses the two-step (expr - mgm) / (sds + 1e-8) into a single broadcast.
  • The multi = TRUE branch of the labelled IDF/IAE helpers switched from an O(G * K^2) apply() to an O(G * K) top-1 + top-2 trick via the new rowwise_notin_max() helper.
  • New inst/bench/benchmark_smartid.R micro-benchmark script; new tests/testthat/test-numerical-equivalence.R pins cal_score() and top_markers() outputs to a frozen pre-refactor snapshot at 1e-10 tolerance.
  • No new dependencies; the refactor relies entirely on Matrix, sparseMatrixStats and base R.