Reference-free Deconvolution

Reference-Free Deconvolution is a computational approach in cell-type deconvolution that infers cell-type proportions from bulk gene expression data without relying on pre-existing reference profiles of pure cell types, distinguishing itself from reference-based methods by leveraging internal data structure or statistical assumptions to identify latent cellular signatures.

18

Publications

1.1K

Citations

118

Authors

79

Institutions

Publications per year

2017–2026

15

Authors

118

  1. MS
    Michael Scherer

    Heidelberg University, Germany

    3 publications · 159 citations

    3

    h-index

  2. EA
    E. Andrés Houseman

    Harvard University, United States

    3 publications · 333 citations

    3

    h-index

  3. JF
    Jean Fan

    Harvard University, United States

    2 publications · 252 citations

    2

    h-index

  4. ML
    Molly L. Kile

    Oregon State University, United States

    2 publications · 292 citations

    2

    h-index

  5. JW
    Jörn Walter

    Saarland University, Germany

    2 publications · 118 citations

    2

    h-index

1–5 of 118

Venues

  1. Genome biology

    2 publications · 128 citations

    2

    h-index

  2. BMC Bioinformatics

    2 publications · 307 citations

    2

    h-index

  3. bioRxiv (Cold Spring Harbor Laboratory)

    Repository · 2 publications · 41 citations

    2

    h-index

  4. Bioinformatics

    2 publications · 59 citations

    2

    h-index

  5. Briefings in Bioinformatics

    1 publications · 16 citations

    1

    h-index

1–5