Quantile Normalization

Quantile Normalization is a statistical preprocessing technique used in omics data analysis that belongs to the class of normalization methods, distinguished by its approach of making the distribution of intensities across samples identical by forcing the quantiles of each sample’s intensity distribution to be equal.

220

Publications

18.3K

Citations

769

Authors

426

Institutions

Publications per year

2017–2026

70

Authors

769

  1. RA
    Rafael A. Irizarry

    Johns Hopkins University, United States

    6 publications · 1K citations

    6

    h-index

  2. SC
    Stephanie C. Hicks

    Johns Hopkins University, United States

    6 publications · 359 citations

    6

    h-index

  3. RJ
    Rong Jiang

    Tongji University, China

    4 publications · 83 citations

    4

    h-index

  4. KD
    Kasper D. Hansen

    Johns Hopkins University, United States

    4 publications · 2.5K citations

    4

    h-index

  5. KY
    Keming Yu

    Brunel University of London, United Kingdom

    3 publications · 99 citations

    3

    h-index

1–5 of 769

Venues

  1. BMC Bioinformatics

    14 publications · 2.8K citations

    13

    h-index

  2. Computational Statistics & Data Analysis

    CSDA · 11 publications · 366 citations

    11

    h-index

  3. Bioinformatics

    9 publications · 2.4K citations

    9

    h-index

  4. Statistics & Probability Letters

    6 publications · 167 citations

    6

    h-index

  5. Computational Statistics

    4 publications · 100 citations

    4

    h-index

1–5