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Hirak Sarkar

Showing results (1-10 of 27) with videos related to

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Bioinformatics (Oxford, England)|October 28, 2017
Quark enables semi-reference-based compression of RNA-seq dataHirak Sarkar, Rob Patro
Bioinformatics (Oxford, England)|September 13, 2019
Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read levelHirak Sarkar, Avi Srivastava, Rob Patro
Biorxiv : the Preprint Server for Biology|March 3, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platformsHongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Genome Research|November 17, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platformsHongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Bioinformatics (Oxford, England)|July 14, 2020
A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantificationAvi Srivastava, Laraib Malik, Hirak Sarkar, et al.
Bioinformatics (Oxford, England)|June 17, 2016
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomesAvi Srivastava, Hirak Sarkar, Nitish Gupta, et al.
Bioinformatics (Oxford, England)|June 29, 2018
A space and time-efficient index for the compacted colored de Bruijn graphFatemeh Almodaresi, Hirak Sarkar, Avi Srivastava, et al.
Bioinformatics (Oxford, England)|June 28, 2024
A count-based model for delineating cell-cell interactions in spatial transcriptomics dataHirak Sarkar, Uthsav Chitra, Julian Gold, et al.
Genes & Development|November 4, 2024
Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challengesHirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, et al.
Nature Methods|March 12, 2022
Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq dataDongze He, Mohsen Zakeri, Hirak Sarkar, et al.
Pageof 3

Showing results (1-10 of 27) with videos related to

Sort By:
Pageof 3
Bioinformatics (Oxford, England)|October 28, 2017
Quark enables semi-reference-based compression of RNA-seq dataHirak Sarkar, Rob Patro
Bioinformatics (Oxford, England)|September 13, 2019
Minnow: a principled framework for rapid simulation of dscRNA-seq data at the read levelHirak Sarkar, Avi Srivastava, Rob Patro
Biorxiv : the Preprint Server for Biology|March 3, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platformsHongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Genome Research|November 17, 2025
Joint imputation and deconvolution of gene expression across spatial transcriptomics platformsHongyu Zheng, Hirak Sarkar, Benjamin J Raphael
Bioinformatics (Oxford, England)|July 14, 2020
A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantificationAvi Srivastava, Laraib Malik, Hirak Sarkar, et al.
Bioinformatics (Oxford, England)|June 17, 2016
RapMap: a rapid, sensitive and accurate tool for mapping RNA-seq reads to transcriptomesAvi Srivastava, Hirak Sarkar, Nitish Gupta, et al.
Bioinformatics (Oxford, England)|June 29, 2018
A space and time-efficient index for the compacted colored de Bruijn graphFatemeh Almodaresi, Hirak Sarkar, Avi Srivastava, et al.
Bioinformatics (Oxford, England)|June 28, 2024
A count-based model for delineating cell-cell interactions in spatial transcriptomics dataHirak Sarkar, Uthsav Chitra, Julian Gold, et al.
Genes & Development|November 4, 2024
Deciphering normal and cancer stem cell niches by spatial transcriptomics: opportunities and challengesHirak Sarkar, Eunmi Lee, Sereno L Lopez-Darwin, et al.
Nature Methods|March 12, 2022
Alevin-fry unlocks rapid, accurate and memory-frugal quantification of single-cell RNA-seq dataDongze He, Mohsen Zakeri, Hirak Sarkar, et al.
Pageof 3