Author

Dan D. Erdmann-Pham

University of California, Berkeley

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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Genomics· 3 Bioinformatics· 1

Signature Assays, Organisms & Methods

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Approach:statistical model· 4 Assay:bulk RNA-seq· 2 Research Focus:differential expression· 2 Research Focus:gene regulatory network inference· 2 Approach:Bayesian inference· 1 Research Focus:benchmarking· 1 Research Focus:deconvolution· 1 Assay:Hi-C· 1

Frequent Co-Authors

Yun S. SongiD· 4 shared Jonathan D. FischeriD· 2 shared Alan Hunter ShainiD· 1 shared Alan J. AwiD· 1 shared Boris C. BastianiD· 1 shared Daniel S. RokhsariD· 1 shared Dirk F. HockemeyeriD· 1 shared Fanding Zhou· 1 shared

Activity by Year

2020-2025 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 27 mutual co-author links

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4 shared papersGenomics

Yun S. SongORCID

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4 papers

Recent·Most cited

Robust and Adaptive Non-Parametric Tests for Detecting General Distributional Shifts in Gene Expression

Fanding Zhou, Alan J. Aw, Dan D. Erdmann-Pham, Jonathan D. Fischer, Yun S. Song

This paper introduces QRscore, a non-parametric test for detecting distributional shifts in gene expression, showing it outperforms existing methods in detecting variance and mean shifts.

Cell Reports Methods · Sep 1, 2025Peer-reviewedBioinformatics

Tracing cancer evolution and heterogeneity using Hi-C

Dan D. Erdmann-Pham, Sanjit Singh Batra, Timothy K. Turkalo, James Durbin, Marco Blanchette, Iwei Yeh, Alan Hunter Shain, Boris C. Bastian, Yun S. Song, Daniel S. Rokhsar, Dirk F. Hockemeyer

The paper develops HiDENSEC to analyze Hi-C data in heterogeneous cancer samples, identifying chromosomal rearrangements and their impact on melanoma progression.

Nature Communications · Nov 6, 2023Peer-reviewedGenomics

A likelihood-based deconvolution of bulk gene expression data using single-cell references

Dan D. Erdmann-Pham, Jonathan D. Fischer, Justin Hong, Yun S. Song

This paper introduces RNA-Sieve, a likelihood-based method to deconvolve bulk RNA-seq data using single-cell references, providing accurate cell type proportion estimates and confidence intervals.

Genome Research · Oct 1, 2021Peer-reviewedGenomics

The key parameters that govern translation efficiency

Dan D. Erdmann-Pham, Khanh Dao Duc, Yun S. Song

The paper develops a stochastic model of translation dynamics to identify key parameters governing translation efficiency and validates findings with ribosome profiling data from yeast.

Cell Systems · Jan 15, 2020Peer-reviewedGenomics