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Changelog

Source: NEWS.md

tinydenseR 0.0.3.0001

Framework overview

tinydenseR is a clustering-independent framework for sample-level modeling of single-cell data. It constructs a landmark-by-sample fuzzy density matrix from UMAP-derived cell–landmark connection strengths and supports four downstream analysis modes within a single analytical scaffold:

  1. Differential cell-state density analysis — landmark-level linear modeling of density shifts across conditions
  2. pePC (partial-effect Principal Component projections) — supervised quantitative sample embedding that isolates variation attributable to a specified effect
  3. plsD (graph-diffused, density contrast-aligned partial least squares Decomposition) — multivariate feature interpretation identifying expression programs aligned with density contrasts
  4. Pseudobulk differential expression — manifold-informed, connection- strength-weighted pseudobulk aggregation with design and marker modes

S4 TDRObj class

The data container is a formal S4 class with 12 slots, providing a structured representation for landmark-based single-cell analysis objects. A backward-compatible $ accessor preserves legacy syntax (obj$pbDE, obj$markerDE, etc.) while all internal code uses @-access.

Multi-backend support

All analysis and plot functions dispatch via S3 generics with methods for multiple input formats:

Backend Format
Seurat v4, v5 (on-disk or in-memory)
SingleCellExperiment On-disk or in-memory
H5AnnData On-disk .h5ad files (via anndataR / rhdf5 + BPCells)
BPCells IterableMatrix (on-disk)
dgCMatrix Sparse matrix (in-memory)
DelayedMatrix Converted to BPCells for on-disk efficiency
flowSet / cytoset Flow, mass, and spectral cytometry (flowWorkspace)
  • RunTDR(): Unified entry point that sets up a TDRObj from any supported container.
  • GetTDR() / SetTDR(): Extract / embed the TDRObj within a container.
  • S3 methods follow a three-step pattern: extract → compute on TDRObj → re-embed, so all functions work identically on any backend.

Manifold-informed pseudobulk DE

get.pbDE() supports two analysis modes:

Mode Purpose
design Pseudobulk DE across experimental conditions
marker Marker identification comparing cell populations within samples

Pseudobulk aggregation uses connection-strength-weighted sums over landmark neighborhoods rather than hard cluster or cell-type membership, yielding a manifold-informed pseudobulk representation while retaining samples as the unit of inference.

plsD: graph-diffused partial least squares decomposition

get.plsD() identifies graph-smoothed expression programs maximally aligned with a fitted density contrast. Key properties:

  • Sparse-implicit NIPALS PLS1 implementation (no dense intermediates)
  • Concordance-filtered and raw loadings
  • Post-hoc sign alignment with density contrast
  • Designed for interpretation and hypothesis generation, not formal inference

celltyping: multi-solution support

celltyping() accepts two input modes:

Mode Input type Semantics
A Named list Cluster-to-label mapping
B Named character vector Per-cell labels for the full dataset

Multiple named celltyping solutions can coexist. Switch active solutions with set_active_celltyping() and list available solutions with list_celltyping_solutions().

Reclustering and multi-resolution workflows

recluster() re-runs Leiden community detection with new parameters and refreshes all clustering-dependent downstream slots. Multiple clustering solutions can be stored and switched via set_active_clustering().

New features

  • get.embedding(): Sample-level embeddings (PCA, diffusion-map trajectory, and pePC) from the landmark-by-sample density matrix.
  • get.cellmap(): Accessor for per-cell mapping data by sample and slot.
  • sim_trajectory_tdr: Bundled simulated scRNA-seq dataset with condition-dependent differential abundance for examples.
  • simulate_DA_data() / simulate_DE_data(): Generate synthetic cytometry datasets for benchmarking.

Vignettes

  • Clinical scRNA-seq analysis: End-to-end workflow with on-disk data.
  • Clinical cytometry analysis: Flow cytometry with longitudinal design.
  • Multi-backend workflows: Demonstrates all supported input formats.
  • Package architecture: Object structure and analysis pipeline overview.

tinydenseR 0.0.2.0001

On-disk caching for get.map()

get.map() supports on-disk caching (.cache.on.disk = TRUE, default), serializing large per-sample data to disk to reduce peak RAM usage for large datasets. All downstream functions read from cache transparently.

Cache management: tdr_cache_info(), tdr_cache_validate(), tdr_cache_cleanup().