Author

Svitlana Tyekucheva

Harvard UniversityORCID 0000-0002-3119-6507Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Assay:bulk RNA-seq· 2 Research Focus:batch correction / integration· 1 Approach:benchmark study· 1 Research Focus:benchmarking· 1 Research Focus:database / resource· 1 Research Focus:deconvolution· 1 Research Focus:dimensionality reduction· 1

Frequent Co-Authors

Massimo F. LodaiD· 2 shared Alessia VignoliiD· 1 shared Alex C. SoupiriD· 1 shared Anni S. Halkola· 1 shared Bo LiiD· 1 shared Brendon Price· 1 shared Brooke L. FridleyiD· 1 shared Chiamaka Jessica OkekeiD· 1 shared

Activity by Year

2018-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 15 mutual co-author links

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2 shared papersBioinformatics

Massimo F. LodaORCID

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Author profile →

3 papers

Recent·Most cited

KODAMA enables self-guided weakly supervised learning in spatial transcriptomics

Ebtesam A. Abdel-Shafy, Moussa Kassim, Alessia Vignoli, Farag Mamdouh, Svitlana Tyekucheva, Dalia Ahmed, Dupe Ojo, Brendon Price, Fabio Socciarelli, Nancy Paola Duarte-Delgado, Martin Ocharo, Chiamaka Jessica Okeke, Luca Triboli, David A. MacIntyre, Massimo F. Loda, , Stefano Cacciatore

KODAMA improves spatial transcriptomics analysis by reducing dimensionality while preserving spatial information, showing high accuracy across multiple datasets.

bioRxiv · May 28, 2025 · PreprintBioinformatics

curatedPCaData: Integration of clinical, genomic, and signature features in a curated and harmonized prostate cancer data resource

Teemu D. Laajala, Varsha Sreekanth, Alex C. Soupir, Jordan H. Creed, Anni S. Halkola, Federico C. F. Calboli, Kalaimathy Singaravelu, Michael V. Orman, Christelle Colin, Travis A. Gerke, Brooke L. Fridley, Svitlana Tyekucheva, James C. Costello

curatedPCaData harmonizes prostate cancer datasets to enable meta-analysis and clinical insights across multiple cohorts.

Scientific Data · Jul 5, 2023Peer-reviewedBioinformatics

Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration

Zeya Wang, Shaolong Cao, Jeffrey S. Morris, Jaeil Ahn, Rongjie Liu, Svitlana Tyekucheva, Fan Gao, Bo Li, Wei Lü, Ximing Tang, Ignacio Ivan Wistuba, Michaela Bowden, Lorelei Ann Mucci, Massimo F. Loda, Giovanni Parmigiani, , Wenyi Wang

DeMixT improves transcriptomic deconvolution by estimating component proportions and expression profiles in heterogeneous samples, showing high accuracy in validation and TCGA data.

iScience · Nov 1, 2018Peer-reviewedBioinformatics