omnideconv

omnideconv

• Next-generation deconvolution of the tumor microenvironment with omnideconv
  Merotto L, Dietrich A, Markus List M, Finotello F.
  Methods in Cell Biology, Immuno-oncology and Immunotherapy (2025).


• Benchmarking second-generation methods for cell-type deconvolution of transcriptomic data
  Dietrich A*, Merotto L*, Pelz K, Eder B*, Zackl C, Reinisch K, Edenhofer F, Marini F, Sturm G, List M✝, Finotello F✝.
  bioRxiv (preprint).

spacedeconv

• spacedeconv: deconvolution of tissue architecture from spatial transcriptomic
  Zackl C, Zopoglou M, Stauffer R, Ausserhofer M, Ijsselsteijn ME, Sturm, G, de Miranda NFdCC, Finotello F.
  Research Square (preprint)

immunedeconv

• Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology
  Sturm G, Finotello F, Petitprez F, Zhang JD, Baumbach J, Fridman WH, List M, Aneichyk T.
  Bioinformatics, 35(14):i436-i445 (2019).


• Immunedeconv: an R package for unified access to computational methods for estimating immune cell fractions from bulk RNA-sequencing data
  Sturm G, Finotello F, List M.
  Methods Mol Biol, 2120:223-232 (2020).


• Making mouse transcriptomics deconvolution accessible with immunedeconv
  Merotto L, Sturm G, Dietrich A, List M, Finotello F.
  Bioinformatics Advances, 4(1):vbae032 (2024).

SimBu

• SimBu: bias-aware simulation of bulk RNA-seq data with variable cell-type composition
  Dietrich A, Sturm G, Merotto L, Marini F, Finotello F, List M.
  Bioinformatics, 38(Suppl_2):ii141-ii147 (2022).

Deconvolution reviews and chapters

• Computational genomics tools for dissecting tumour-immune cell interactions
  Hackl H, Charoentong P, Finotello F, Trajanoski Z.
  Nat Rev Genet, 17(8):441-58 (2016).


• Quantifying tumor-infiltrating immune cells from transcriptomics data
  Finotello F, Trajanoski Z.
  Cancer Immunol Immunother, 67(7):1031-1040 (2018).


• Next-generation computational tools for interrogating cancer immunity
  Finotello F, Rieder D, Hackl H, Trajanoski Z.
  Nat Rev Genet, 20(12):724-746 (2019).


• Deconvoluting tumor-infiltrating immune cells from RNA-seq data using quanTIseq
  Plattner C, Finotello F, Rieder D.
  Methods Enzymol, 636:261-285 (2020).


• In silico cell-type deconvolution methods in cancer immunotherapy
  Sturm G, Finotello F, List M.
  Methods Mol Biol, 2120:213-222 (2020).


• Machine learning for deciphering cell heterogeneity and gene regulation
  Scherer M, Schmidt F, Lazareva O, Walter J, Baumbach J, Schulz MH, List M.
  Nat Comput Sci, 1, 183–191 (2021).


• Next-generation deconvolution of transcriptomic data to investigate the tumor microenvironment
  Merotto L, Zopoglou M, Zackl C, Finotello F.
  Int Rev Cell Mol Biol, 382:103-143 (2024).

Other publications

• Molecular and pharmacological modulators of the tumor immune contexture revealed by deconvolution of RNA-seq data
  Finotello F, Mayer C, Plattner C, Laschober G, Rieder D, Hackl H, Krogsdam A, Loncova Z, Posch W, Wilflingseder D, Sopper S, Ijsselsteijn M, Brouwer TP, Johnson D, Xu Y, Wang Y, Sanders ME, Estrada MV, Ericsson-Gonzalez P, Charoentong P, Balko J, de Miranda NFDCC, Trajanoski Z.
  Genome Med, 11(1):34 (2019).


• Blood transcriptomics analysis offers insights into variant-specific immune response to SARS-CoV-2
  Hoffmann M, Willruth LL, Dietrich A, Lee HK, Knabl L, Trummer N, Baumbach J, Furth PA, Hennighausen L, List M.
  Sci Rep, 14, 2808 (2024).