| Integrated analysis of multimodal single-cell data Y Hao, S Hao, E Andersen-Nissen, WM Mauck III, S Zheng, A Butler, ... Cell 184 (13), 3573-3587. e29, 2021 | 6627 | 2021 |
| Bioconda: sustainable and comprehensive software distribution for the life sciences. B Grüning, R Dale, A Sjödin, BA Chapman, J Rowe, CH Tomkins-Tinch, ... Nature methods 15 (7), 475, 2018 | 797 | 2018 |
| Single-cell chromatin state analysis with Signac T Stuart, A Srivastava, S Madad, CA Lareau, R Satija Nature methods 18 (11), 1333-1341, 2021 | 621 | 2021 |
| Dictionary learning for integrative, multimodal and scalable single-cell analysis Y Hao, T Stuart, MH Kowalski, S Choudhary, P Hoffman, A Hartman, ... Nature biotechnology 42 (2), 293-304, 2024 | 200 | 2024 |
| Best practices for single-cell analysis across modalities L Heumos, AC Schaar, C Lance, A Litinetskaya, F Drost, L Zappia, ... Nature Reviews Genetics 24 (8), 550-572, 2023 | 198 | 2023 |
| Alevin efficiently estimates accurate gene abundances from dscRNA-seq data A Srivastava, L Malik, T Smith, I Sudbery, R Patro Genome biology 20, 1-16, 2019 | 195 | 2019 |
| Multimodal single-cell chromatin analysis with Signac T Stuart, A Srivastava, C Lareau, R Satija BioRxiv, 2020.11. 09.373613, 2020 | 138 | 2020 |
| Alignment and mapping methodology influence transcript abundance estimation A Srivastava, L Malik, H Sarkar, M Zakeri, F Almodaresi, C Soneson, ... Genome Biology 21 (1), 1-29, 2020 | 135 | 2020 |
| RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomes A Srivastava, H Sarkar, N Gupta, R Patro Bioinformatics 32 (12), i192-i200, 2016 | 134 | 2016 |
| Nonparametric expression analysis using inferential replicate counts A Zhu, A Srivastava, JG Ibrahim, R Patro, MI Love Nucleic Acids Research, 2019 | 77 | 2019 |
| A space and time-efficient index for the compacted colored de Bruijn graph F Almodaresi, H Sarkar, A Srivastava, R Patro Bioinformatics 34 (13), i169-i177, 2018 | 75 | 2018 |
| Characterizing cellular heterogeneity in chromatin state with scCUT&Tag-pro B Zhang, A Srivastava, E Mimitou, T Stuart, I Raimondi, Y Hao, P Smibert, ... Nature Biotechnology, 1-11, 2022 | 54 | 2022 |
| Preprocessing choices affect RNA velocity results for droplet scRNA-seq data C Soneson, A Srivastava, R Patro, MB Stadler PLOS Computational Biology 17 (1), e1008585, 2021 | 45 | 2021 |
| Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq data D He, M Zakeri, H Sarkar, C Soneson, A Srivastava, R Patro Nature Methods 19 (3), 316-322, 2022 | 42 | 2022 |
| Improved data-driven likelihood factorizations for transcript abundance estimation M Zakeri, A Srivastava, F Almodaresi, R Patro Bioinformatics 33 (14), i142-i151, 2017 | 31 | 2017 |
| Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read level H Sarkar, A Srivastava, R Patro Bioinformatics 35 (14), i136-i144, 2019 | 19 | 2019 |
| Terminus enables the discovery of data-driven, robust transcript groups from RNA-seq data H Sarkar, A Srivastava, HC Bravo, MI Love, R Patro Bioinformatics 36 (Supplement_1), i102-i110, 2020 | 13 | 2020 |
| Texture-based medical image retrieval in compressed domain using compressive sensing K Yadav, A Srivastava, A Mittal, MA Ansari International journal of bioinformatics research and applications 10 (2 …, 2014 | 12 | 2014 |
| A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification A Srivastava, L Malik, H Sarkar, R Patro Bioinformatics 36 (Supplement_1), i292-i299, 2020 | 11 | 2020 |
| Accurate, Fast and Lightweight Clustering of de novo Transcriptomes using Fragment Equivalence Classes A Srivastava, H Sarkar, L Malik, R Patro RECOMB-seq preprint arXiv:1604.03250, 2016 | 10 | 2016 |
Rahul SatijaCore Faculty Member, New York Genome Center. Department of Biology, NYUVerified email at nygenome.org
