Technical Variation Analysis

Technical Variation Analysis is a methodological framework in single-cell proteomics that systematically identifies, quantifies, and distinguishes experimental artifacts and measurement noise from biological signals to ensure data reliability and reproducibility.

184

Publications

32.6K

Citations

1.1K

Authors

479

Institutions

Publications per year

2017–2026

72

Authors

1.1K

  1. SC
    Stephanie C. Hicks

    Johns Hopkins University, United States

    6 publications · 993 citations

    6

    h-index

  2. RA
    Rafael A. Irizarry

    Johns Hopkins University, United States

    6 publications · 993 citations

    6

    h-index

  3. DA
    David A. Knowles

    Stanford University, United States

    3 publications · 455 citations

    3

    h-index

  4. JQ
    John Quackenbush

    Dana-Farber Cancer Institute, United States

    3 publications · 281 citations

    3

    h-index

  5. IM
    Inge Mertens

    KU Leuven, Belgium

    3 publications · 101 citations

    3

    h-index

1–5 of 1.1K

Venues

  1. BMC Bioinformatics

    11 publications · 1.4K citations

    11

    h-index

  2. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 12 publications · 382 citations

    11

    h-index

  3. PROTEOMICS

    9 publications · 777 citations

    9

    h-index

  4. PLoS ONE

    8 publications · 327 citations

    8

    h-index

  5. Bioinformatics

    8 publications · 860 citations

    8

    h-index

1–5