Hirak Sarkar
Hirak Sarkar
Ludwig Scholar at Princeton University
Verified email at - Homepage
Cited by
Cited by
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
Alignment and mapping methodology influence transcript abundance estimation
A Srivastava, L Malik, H Sarkar, M Zakeri, F Almodaresi, C Soneson, ...
Genome biology 21, 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
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
Voronoi game on graphs
S Bandyapadhyay, A Banik, S Das, H Sarkar
Theoretical Computer Science 562, 270-282, 2015
Dissecting the immune suppressive human prostate tumor microenvironment via integrated single-cell and spatial transcriptomic analyses
T Hirz, S Mei, H Sarkar, Y Kfoury, S Wu, BM Verhoeven, AO Subtelny, ...
Nature Communications 14 (1), 663, 2023
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
Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes
T Gao, R Soldatov, H Sarkar, A Kurkiewicz, E Biederstedt, PR Loh, ...
Nature Biotechnology 41 (3), 417-426, 2023
Towards selective-alignment: Bridging the accuracy gap between alignment-based and alignment-free transcript quantification
H Sarkar, M Zakeri, L Malik, R Patro
Proceedings of the 2018 ACM International Conference on Bioinformatics …, 2018
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
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
arXiv preprint arXiv:1604.03250, 2016
Social media attributions in the context of water crisis
R Sarkar, H Sarkar, S Mahinder, AR KhudaBukhsh
arXiv preprint arXiv:2001.01697, 2020
Airpart: interpretable statistical models for analyzing allelic imbalance in single-cell datasets
W Mu, H Sarkar, A Srivastava, K Choi, R Patro, MI Love
Bioinformatics 38 (10), 2773-2780, 2022
Epigenetic regulation during cancer transitions across 11 tumour types
NV Terekhanova, A Karpova, WW Liang, A Strzalkowski, S Chen, Y Li, ...
Nature 623 (7986), 432-441, 2023
Haplotype-enhanced inference of somatic copy number profiles from single-cell transcriptomes
T Gao, R Soldatov, H Sarkar, A Kurkiewicz, E Biederstedt, PR Loh, ...
bioRxiv, 2022.02. 07.479314, 2022
refine. bio: A resource of uniformly processed publicly available gene expression datasets
CS Greene, D Hu, RWW Jones, S Liu, DS Mejia, R Patro, SR Piccolo, ...
Google Scholar, 2023
Quark enables semi-reference-based compression of RNA-seq data
H Sarkar, R Patro
Bioinformatics 33 (21), 3380-3386, 2017
Compression of quantification uncertainty for scRNA-seq counts
S Van Buren, H Sarkar, A Srivastava, NU Rashid, R Patro, MI Love
Bioinformatics 37 (12), 1699-1707, 2021
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