#17 · Tools & infrastructure
New computational methods for single-cell, spatial, and bulk transcriptomic analysis
The dominant thread across these items is methodological: computational tools that govern how transcriptomic data are preprocessed, quality-controlled, harmonized, and interpreted. Lense uses a language model to select single-cell preprocessing by comparing low-dimensional representation plots across pipeline outputs, while QCatch generates interactive QC reports for alevin-fry/simpleaf quantification. geneSync introduces hierarchical gene symbol harmonization before RNA-seq cross-cohort integration. Several papers add analytic capacity: MUSTARD for trajectory-guided reduction in multi-sample scRNA-seq, REACTOR for regulon activity comparison, PyFgsea for rolling-window enrichment along trajectories, and DEGAS for flagging high-risk cells in spatial data. Novelty lies mainly in automation, scope, and statistical framing rather than in new biological findings, though AXOLOTL and Nested-WGCNA address bulk RNA-seq and the WSP model addresses group effects on spatial expression parameters.
Preprints
A layered standards framework for integrating single-cell and spatial omics data into brain cell atlases
Layered standards framework for BICAN brain atlases: assay-agnostic modeling, harmonized metadata, and extensible cell-type taxonomy for single-cell and…
Building computational benchmarks: an Omnibenchmark reimplementation of a single-cell preprocessing pipeline evaluation
Reimplements a single-cell RNA-seq preprocessing evaluation as a reproducible Omnibenchmark, comparing five pipelines across three datasets and noting…
Building an open ecosystem for molecular neuroimaging: standards and tools from the OpenNeuroPET initiative
OpenNeuroPET builds PET-BIDS standards and open tools (PET2BIDS, ezBIDS, BIDSCoin) for reproducible, shared molecular neuroimaging data.
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