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The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

  • ENCODE Imputation Challenge Participants
  • Jacob M. Schreiber (Creator)
  • Carles A. Boix (Creator)
  • Jin Wook Lee (Creator)
  • Hongyang Li (Creator)
  • Yuanfang Guan (Creator)
  • Chun-Chieh Chang (Creator)
  • Jen-Chien Chang (Creator)
  • Alex Hawkins-Hooker (Creator)
  • Bernhard Schölkopf (Creator)
  • Gabriele Schweikert (Creator)
  • Mateo Rojas-Carulla (Creator)
  • Arif Canakoglu (Creator)
  • Francesco Guzzo (Creator)
  • Luca Nanni (Creator)
  • Marco Masseroli (Creator)
  • Mark James Carman (Creator)
  • Pietro Pinoli (Creator)
  • Chenyang Hong (Creator)
  • Kevin Y. Yip (Creator)
  • Jefrey P. Spence (Stanford University) (Creator)
  • Sanjit Singh Batra (Creator)
  • Yun S. Song (Creator)
  • Shaun Mahony (Creator)
  • Zheng Zhang (Creator)
  • Wuwei Tan (Creator)
  • Yang Shen (Creator)
  • Yuanfei Sun (Creator)
  • Minyi Shi (Creator)
  • Jessika Adrian (Creator)
  • Richard S. Sandstrom (Creator)
  • Nina P. Farrell (Creator)
  • Jessica M. Halow (Creator)
  • Kristen A. Lee (Creator)
  • Lixia Jiang (Creator)
  • Xinqiong Yang (Creator)
  • Charles B. Epstein (Creator)
  • J. Seth Strattan (Creator)
  • Bradley E. Bernstein (Creator)
  • Michael Paul Snyder (Creator)
  • Manolis Kellis (Creator)
  • William S. Noble (Creator)
  • Anshul Bharat Kundaje (Creator)

Dataset

Description

Abstract A promising alternative to comprehensively performing genomics experiments is to, instead, perform a subset of experiments and use computational methods to impute the remainder. However, identifying the best imputation methods and what measures meaningfully evaluate performance are open questions. We address these questions by comprehensively analyzing 23 methods from the ENCODE Imputation Challenge. We find that imputation evaluations are challenging and confounded by distributional shifts from differences in data collection and processing over time, the amount of available data, and redundancy among performance measures. Our analyses suggest simple steps for overcoming these issues and promising directions for more robust research.
Date made available12 Feb 2025
Publisherfigshare

Keywords

  • epigenomic profiles abstract
  • encode imputation challenge
  • cell type imputation
  • use computational methods
  • best imputation methods
  • imputation evaluations
  • robust research
  • promising directions
  • promising alternative
  • distributional shifts
  • data collection
  • critical assessment
  • available data

Contact

  • The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

    Schreiber,, J. M. (Lead / Corresponding author), Boix, C. A. (Lead / Corresponding author), Lee, J. W., Li, H., Guan, Y., Chang, C.-C., Chang, J.-C., Hawkins-Hooker, A., Schölkopf, B., Schweikert, G., Carulla, M. R., Canakoglu, A., Guzzo, F., Nanni, L., Masseroli, M., Carman, M. J., Pinoli, P., Hong, C., Yip, K. Y. & Spence, J. P. & 23 others, Batra, S. S., Song, Y. S., Mahony, S., Zhang, Z., Tan, W., Shen, Y., Sun, Y., Shi, M., Adrian, J., Sandstrom, R. S., Farrell, N. P., Halow, J., Lee, K., Jiang, L., Yang, X., Epstein, C. B., Strattan, J. S., Bernstein, B. E., Snyder, M. P., Kellis, M., Noble, W. S., Kundaje, A. B. & ENCODE Imputation Challenge Participants, 18 Apr 2023, In: Genome Biology. 24, 1, 22 p., 79.

    Research output: Contribution to journal › Article › peer-review

    Open Access
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    12   Link opens in a new tab Citations (Scopus)
    157 Downloads (Pure)

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