Stephanie C. Hicks

Molecular Biology · 2017–2020

A researcher in the Science corpus: 6 papers; trusts 13 researchers and is trusted by 4 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 310 citations into it; #9,760 of 17,773 by trust here, #6,893 by citations
• Biophysics — 66 citations into it
• Computer Vision and Pattern Recognition — 16 citations into it
• Statistics and Probability — 16 citations into it; #8,807 of 12,932 by trust here, #5,857 by citations
• Representation & Self-Supervised Learning — 12 citations into it
• Computational Mechanics — 6 citations into it

Whom Stephanie C. Hicks trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Valentine Svensson
2. Alan Agresti
3. Rahul Satija
4. Leland McInnes
5. Davide Risso
6. Christoph Hafemeister
7. Aaron T. L. Lun
8. Aleksandra A. Kolodziejczyk
9. Michael Collins
10. Davis J. McCarthy

Who trusts Stephanie C. Hicks overall

1. F. W. Townes
2. Christoph Hafemeister
3. Dmitry Kobak

Papers from Stephanie C. Hicks's perspective

Ordered by how strongly the researchers Stephanie C. Hicks trusts endorse each paper. Same corpus, a different order for every researcher.

1. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets
2. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
3. Droplet barcoding for single cell transcriptomics applied to embryonic stem cells (Cell)
4. Visualizing Data using t-SNE
5. MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data (Genome Biology)
6. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data (Bioinform.)
7. Accounting for technical noise in single-cell RNA-seq experiments (Nature Methods)
8. Quantitative single-cell RNA-seq with unique molecular identifiers (Nature Methods)
9. Data exploration, quality control and testing in single-cell qPCR-based gene expression experiments (Bioinform.)
10. Validation of noise models for single-cell transcriptomics (Nature Methods)

Their papers

• Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019) — 457 citations
• Missing Data and Technical Variability in Single-Cell RNA-Sequencing Experiments (2017) — 92 citations
• Eleven grand challenges in single-cell data science (2020) — 1,198 citations
• Orchestrating single-cell analysis with Bioconductor (2019) — 840 citations
• Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex (2020) — 1,067 citations
• A practical guide to methods controlling false discoveries in computational biology (2018) — 388 citations