Integrative single-cell analysis

By Tim Stuart, Rahul Satija. Nature reviews genetics, 2019

1,320 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9a12e9b29da9f3dcaaf02c0e5c8d69aee94c7112

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

1. Oliver Stegle
2. John C. Marioni
3. Jonathan S. Weissman
4. Fabian J Theis
5. Rahul Satija

In Molecular Biology

1. John C. Marioni
2. R. Argelaguet
3. Oliver Stegle
4. Anna S. E. Cuomo
5. Sohrab P. Shah
6. Eric J. Wagner
7. Doron Lipson
8. Jonathan S. Weissman

Citations in the corpus

• Best practices for single-cell analysis across modalities (2023, neutral): “Moreover, although computational best practices and tool recommendations have previously been outlined for scRNA-seq 2,5,6,17 , they are either outdated or incomplete.”
• Harnessing multimodal data integration to advance precision oncology (2021, background): “As experimental and computational techniques advance, these data will more completely characterize the molecular state of patients’ disease 71,72 , yet they still only capture a fraction of the informative data.”
• Unsupervised removal of systematic background noise from droplet-based single-cell experiments using CellBender (2019, background): “The count matrix is the starting point for downstream analyses such as batch correction, clustering, and differential expression [3, 4].”
• Mapping information-rich genotype-phenotype landscapes with genome-scale Perturb-seq (2021, positive): “In parallel, dramatic improvements in molecular phenotyping now allow for single-cell readouts of epigenetic, transcriptomic, proteomic, and imaging information (Stuart and Satija, 2019).”
• MOFA+: a statistical framework for comprehensive integration of multi-modal single-cell data (2020, background): “Consequently, there is a need for integrative computational frameworks that can robustly and systematically interrogate the data generated in order to reveal the underlying sources of variation [26].”
• Computational principles and challenges in single-cell data integration (2021, background): “In particular, a key challenge in the analysis of single-cell multimodal data is to devise efficient computational strategies to integrate different data modalities 8,12–15 .”