Author

Kun-Jiang Feng

Stanford UniversityORCID 0000-0001-5763-300XUnclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Genomics· 2 Genetic & Genomic Medicine· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 3 Organism:human· 2 Assay:ATAC-seq· 1 Assay:bulk RNA-seq· 1 Research Focus:cell type annotation· 1 Research Focus:clustering· 1 Research Focus:fine-mapping· 1 Research Focus:GWAS / association mapping· 1

Frequent Co-Authors

Akindele Olupelumi AdebiyiiD· 1 shared Alex A RodrigueziD· 1 shared Amy C. JusticeiD· 1 shared Anqi WangiD· 1 shared Anshul Bharat KundajeiD· 1 shared Barbara Elizabeth EngelhardtiD· 1 shared Ben Adusei· 1 shared Bianca DumitrascuiD· 1 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 17 mutual co-author links

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1 shared paperGenetic & Genomic Medicine

Akindele Olupelumi AdebiyiORCID

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3 papers

Recent·Most cited

GenomicsbioRxivPreprintNov 4, 2023

Transcriptomics and chromatin accessibility in multiple African population samples

Marianne K. DeGorter, Pagé C. Goddard, Emre Karakoç…·Stephen B. Montgomery

The paper identifies novel transcripts, eQTLs, and caQTLs in African populations and fine-maps GWAS signals for complex diseases.

13 citations

humanbulk RNA-seq

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Genetic & Genomic MedicinemedRxivPreprintOct 27, 2023

Genome-wide association study of prostate-specific antigen levels in 392,522 men identifies new loci and improves cross-ancestry prediction

Thomas J. Hoffmann, Rebecca E. Graff, Ravi Madduri…·John S. Witte

This study identifies 447 new genetic variants associated with prostate-specific antigen levels across multiple ancestries and shows improved cross-ancestry prediction of PSA, with implications for personalized prostate cancer screening.

3 citations

humanGWAS summary statistics

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GenomicsbioRxivPreprintAug 7, 2018

GT-TS: Experimental design for maximizing cell type discovery in single-cell data

Bianca Dumitrascu, Kun-Jiang Feng, Barbara Elizabeth Engelhardt

GT-TS is a computational method that optimizes cell type discovery in multi-tissue scRNA-seq by iteratively planning data collection.

6 citations

scRNA-seqstatistical model

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