Avi Srivastava
Avi Srivastava
Assistant Professor, The Wistar Institute
Verified email at - Homepage
Cited by
Cited by
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
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
Single-cell chromatin state analysis with Signac
T Stuart, A Srivastava, S Madad, CA Lareau, R Satija
Nature methods 18 (11), 1333-1341, 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
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
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
Multimodal single-cell chromatin analysis with Signac
T Stuart, A Srivastava, C Lareau, R Satija
BioRxiv, 2020.11. 09.373613, 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
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
Nonparametric expression analysis using inferential replicate counts
A Zhu, A Srivastava, JG Ibrahim, R Patro, MI Love
Nucleic Acids Research, 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
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
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
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
Improved data-driven likelihood factorizations for transcript abundance estimation
M Zakeri, A Srivastava, F Almodaresi, R Patro
Bioinformatics 33 (14), i142-i151, 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
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
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
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
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
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