We introduce CIBERSORT, a method for characterizing cell composition of complex tissues from their gene expression profiles. When applied to enumeration of hematopoietic subsets in RNA mixtures from fresh, frozen and fixed tissues, including solid tumors, CIBERSORT outperformed other methods with respect to noise, unknown mixture content and closely related cell types. CIBERSORT should enable large-scale analysis of RNA mixtures for cellular biomarkers and therapeutic targets (http://cibersort.stanford.edu/).

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We are grateful to H. Maecker, M. Davis, R. Levy and the Stanford Human Immune Monitoring Center for assistance with this study. This work was supported by grants from the Doris Duke Charitable Foundation (A.A.A.), the Damon Runyon Cancer Research Foundation (A.A.A.), the B&J Cardan Oncology Research Fund (A.A.A.), the Ludwig Institute for Cancer Research (A.A.A. and M.D.), US National Institutes of Health (NIH) grant U01 CA154969 (A.J.G., W.F., Y.X., C.D.H. and M.D.), NIH grant U19 AI090019, NIH grant PHS NRSA 5T32 CA09302-35 (A.M.N.), US Department of Defense grant W81XWH-12-1-0498 (A.M.N.) and a grant from the Siebel Stem Cell Institute and the Thomas and Stacey Siebel Foundation (A.M.N.).

Author information

Author notes

    • Michael R Green

    Present address: Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, Nebraska, USA.

    • Aaron M Newman
    •  & Chih Long Liu

    These authors contributed equally to this work.


  1. Institute for Stem Cell Biology and Regenerative Medicine, Stanford University, Stanford, California, USA.

    • Aaron M Newman
    • , Chih Long Liu
    • , Maximilian Diehn
    •  & Ash A Alizadeh
  2. Department of Medicine, Division of Oncology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.

    • Aaron M Newman
    • , Chih Long Liu
    • , Michael R Green
    •  & Ash A Alizadeh
  3. Center for Cancer Systems Biology, Stanford University, Stanford, California, USA.

    • Michael R Green
    • , Andrew J Gentles
    •  & Ash A Alizadeh
  4. Department of Radiology, Stanford University, Stanford, California, USA.

    • Andrew J Gentles
  5. Department of Radiation Oncology, Stanford University, Stanford, California, USA.

    • Weiguo Feng
    •  & Maximilian Diehn
  6. Department of Cardiothoracic Surgery, Division of Thoracic Surgery, Stanford University, Stanford, California, USA.

    • Yue Xu
    •  & Chuong D Hoang
  7. Stanford Cancer Institute, Stanford University, Stanford, California, USA.

    • Maximilian Diehn
    •  & Ash A Alizadeh
  8. Department of Medicine, Division of Hematology, Stanford Cancer Institute, Stanford University, Stanford, California, USA.

    • Ash A Alizadeh


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A.M.N. and A.A.A. conceived of CIBERSORT, developed strategies for related experiments, analyzed the data and wrote the paper. A.M.N. developed and implemented CIBERSORT. C.L.L. implemented web infrastructure and wrote the paper. M.R.G. performed flow cytometry and gene expression profiling of leukocytes from human tonsils and peripheral blood. A.J.G. assisted in the conceptual development of CIBERSORT. W.F., Y.X., C.D.H. and M.D. assisted in the collection and analysis of lung tissue. All authors discussed the results and implications and commented on the manuscript at all stages.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ash A Alizadeh.

Integrated supplementary information

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Supplementary Figures 1–14, Supplementary Note, Supplementary Results and Supplementary Discussion

Excel files

  1. 1.

    Supplementary Table 1

    Leukocyte signature matrix (LM22). Details of LM22, including gene expression matrix and source data.

  2. 2.

    Supplementary Table 2

    Validation of LM22 on external datasets of purified leukocyte subsets. Analysis of external GEP datasets consisting of distinct leukocyte subsets.

  3. 3.

    Supplementary Table 3

    Feature comparison of GEP deconvolution methods analyzed in this work. Table comparing key features of GEP deconvolution approaches.

  4. 4.

    Supplementary Table 4

    Comparative analysis of GEP deconvolution methods. Performance comparison of CIBERSORT, RLR, PERT, LLSR, and QP on both complex and idealized mixtures.

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