Author

Evan Biederstedt

Harvard University

Listed by name: these papers carry no ORCID for this author, so different people with the same name may be mixed together.

Biology Disciplines

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Bioinformatics· 4

Signature Assays, Organisms & Methods

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Organism:human· 3 Assay:scRNA-seq· 3 Approach:statistical model· 3 Research Focus:clustering· 2 Research Focus:gene regulatory network inference· 2 Research Focus:benchmarking· 1 Research Focus:cell type annotation· 1 Research Focus:cell-cell communication· 1

Frequent Co-Authors

Peter V. KharchenkoiD· 3 shared Adam KurkiewicziD· 1 shared Alexandra–Chloé VillaniiD· 1 shared Chun YeiD· 1 shared David S. FischeriD· 1 shared Fabian Joachim TheisiD· 1 shared Felix FischeriD· 1 shared Hirak SarkariD· 1 shared

Activity by Year

2022-2024 · peak 2/yr

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10 top collaborators · 13 mutual co-author links

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3 shared papersBioinformatics

Peter V. KharchenkoORCID

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4 papers

Recent·Most cited

Tensor decomposition reveals coordinated multicellular patterns of transcriptional variation that distinguish and stratify disease individuals

Jonathan Mitchel, M. Grace Gordon, Richard K. Perez, Evan Biederstedt, Raymund Bueno, Chun Ye, Peter V. Kharchenko

This paper introduces scITD, a method to identify multicellular gene expression patterns that distinguish disease individuals, showing its application in SLE and COVID-19 datasets.

Nature Biotechnology · Sep 23, 2024Peer-reviewedBioinformatics

Gene panel selection for targeted spatial transcriptomics

Yida Zhang, Viktor Petukhov, Evan Biederstedt, Richard A. Que, Kun Zhang, Peter V. Kharchenko

This paper introduces gpsFISH, a method for selecting gene panels in spatial transcriptomics that outperforms existing methods by accounting for platform effects and incorporating cell type hierarchies.

Genome biology · Jan 25, 2024Peer-reviewedBioinformatics

Scaling cross-tissue single-cell annotation models

Felix Fischer, David S. Fischer, Evan Biederstedt, Alexandra–Chloé Villani, Fabian Joachim Theis

This paper introduces scTab, a deep learning model for cross-tissue cell type annotation in single-cell RNA-seq, showing improved performance over linear models and demonstrating scalability with larger datasets.

bioRxiv · Oct 7, 2023 · PreprintBioinformatics

Haplotype-enhanced inference of somatic copy number profiles from single-cell transcriptomes

Teng Gao, Ruslan Soldatov, Hirak Sarkar, Adam Kurkiewicz, Evan Biederstedt, Po‐Ru Loh, Peter V. Kharchenko

This paper introduces Numbat, a method that uses haplotype information to improve detection of copy number variations from scRNA-seq data, revealing subclonal complexity in cancer.

Nature Biotechnology · Sep 26, 2022Peer-reviewedBioinformatics