Project
Bioconductor for Analysis and Comprehension of the Human Cell Atlas
Focus
Methods Focus
Project Summary
This network’s collaborative effort enables the large community of users for the open source software R / Bioconductor to effectively access the Human Cell Atlas for statistical analysis, visualization, and novel scientific discovery.
Results & Resources
This group published 13 preprints describing 12 software packages that enable access to the Human Cell Atlas data, introduce new capabilities for emerging technologies, and implement novel high-throughput statistical and genomic analysis routines for scalable analytics and data visualization. These programs are available to the worldwide community of 850,000+ users with four additional packages under active development.
Papers:
- Impact of Data Preprocessing on Integrative Matrix Factorization of Single Cell Data
- mbkmeans: fast clustering for single-cell data using mini-batch k-means
- PsiNorm: a scalable normalization for single-cell RNA-seq data
- SpatialExperiment: infrastructure for spatially resolved transcriptomics data in R using Bioconductor
- Comprehensive characterization of single-cell full-length isoforms in human and mouse with long-read sequencing
- A Systematic Evaluation of Single-cell RNA-sequencing Imputation Methods
- minicore: Fast scRNA-seq clustering with various distances
- Universal prediction of cell cycle position using transfer learning
- spatialLIBD: an R/Bioconductor package to visualize spatially-resolved transcriptomics data
- Long-read-tools.org: an interactive catalog of analysis methods for long-read sequencing data
- The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools
- Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
- A spatially resolved brain region- and cell type-specific isoform atlas of the postnatal mouse brain
- Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles
Software:
Investigators
Co-Principal Investigators
Aedin Culhane, PhD
Greg Finak, PhD
Kasper Hansen, PhD
Stephanie Hicks, PhD
Wolfgang Huber, PhD
Martin Morgan, PhD
Davide Risso, PhD
Matthew Ritchie, PhD