Recent technological advancements have enabled the profiling of a large number of
genome-wide features in individual cells. However, single-cell data present unique …

Cancer is the quintessential complex disease. As technologies evolve faster each day, we
are able to quantify the different layers of biological elements that contribute to the …

The data output from microbiome research is growing at an accelerating rate, yet mining the
data quickly and efficiently remains difficult. There is still a lack of an effective data structure …

With the rapid adoption of high-throughput omic approaches to analyze biological samples
such as genomics, transcriptomics, proteomics, and metabolomics, each analysis can …

Summary anndata is a Python package for handling annotated data matrices in memory and
on disk (github. com/theislab/anndata), positioned between pandas and xarray. anndata …

Subpopulation identification, usually via some form of unsupervised clustering, is a
fundamental step in the analysis of many single-cell RNA-seq data sets. This has motivated …

Advances in multi-omics have led to an explosion of multimodal datasets to address
questions from basic biology to translation. While these data provide novel opportunities for …

Generating insight from high-dimensional data matrices typically works through training
models that annotate observations and variables via low-dimensional representations. In …

Bioinformatics pipelines are essential in the analysis of genomic and transcriptomic data
generated by next-generation sequencing (NGS). Recent guidelines emphasize the need …

PURPOSE Investigations of the molecular basis for the development, progression, and
treatment of cancer increasingly use complementary genomic assays to gather multiomic …