ArticleSIAM journal on mathematics of data science2022
Biwhitening Reveals the Rank of a Count Matrix.
Article in SIAM journal on mathematics of data science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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Who cites it
9 citing papers in PubMed, 19 citations in OpenAlex.
- Embeddings from language models are good learners for single-cell data analysis.Patterns (New York, N.Y.) · 2026Article
- CellMentor: cell-type aware dimensionality reduction for single-cell RNA-sequencing data.Nature communications · 2025Article
- scELMo: Embeddings from Language Models are Good Learners for Single-cell Data Analysis.bioRxiv : the preprint server for biology · 2025Article
- The Dyson equalizer: adaptive noise stabilization for low-rank signal detection and recovery.Information and inference : a journal of the IMA · 2025Article
- Principled PCA separates signal from noise in omics count data.bioRxiv : the preprint server for biology · 2025Article
- Principled and interpretable alignability testing and integration of single-cell data.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Causal identification of single-cell experimental perturbation effects with CINEMA-OT.Nature methods · 2023Article
- Dimensionality and Ramping: Signatures of Sentence Integration in the Dynamics of Brains and Deep Language Models.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2023Article
- Optimally Weighted PCA for High-Dimensional Heteroscedastic Data.SIAM journal on mathematics of data science · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Estimating the rank of a corrupted data matrix is an important task in data analysis, most notably for choosing the number of components in PCA. Significant progress on this task was achieved using random matrix theory by characterizing the spectral properties of large noise matrices. However, utilizing such tools is not straightforward when the data matrix consists of count random variables, e.g., Poisson, in which case the noise can be heteroskedastic with an unknown variance in each entry. In this work, we focus on a Poisson random matrix with independent entries and propose a simple procedure, termed
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.