V. Proserpio

Molecular Biology · 2013–2016

A researcher in the Science corpus: 4 papers; trusts 1 researcher and is trusted by 9 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 53 citations into it; #5,544 of 17,773 by trust here, #2,800 by citations

Whom V. Proserpio trusts overall

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

1. Simon Anders

Who trusts V. Proserpio overall

1. Cole Trapnell
2. Byungjin Hwang
3. Tallulah S. Andrews

Papers from V. Proserpio's perspective

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

1. Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells
2. Using control genes to correct for unwanted variation in microarray data. (Biostatistics)
3. Accounting for technical noise in single-cell RNA-seq experiments (Nature Methods)
4. HTSeq—a Python framework to work with high-throughput sequencing data (bioRxiv)
5. Scikit-learn: Machine Learning in Python
6. Quantifying E. coli proteome and transcriptome with single-molecule sensitivity in single cells (Science)
7. An Introduction to Variable and Feature Selection
8. A Bayesian Framework to Account for Complex Non-Genetic Factors in Gene Expression Levels Greatly Increases Power in eQTL Studies
9. Imaging individual mRNA molecules using multiple singly labeled probes (Nature Methods)
10. Computational and analytical challenges in single-cell transcriptomics (Nature reviews genetics)

Their papers

• Accounting for technical noise in single-cell RNA-seq experiments (2013) — 973 citations
• Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells (2015) — 1,153 citations
• T cell fate and clonality inference from single cell transcriptomes (2016) — 414 citations
• Computational assignment of cell-cycle stage from single-cell transcriptome data. (2015) — 393 citations