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

, Jacob Schreiber, (Lead / Corresponding author), Carles Boix (Lead / Corresponding author), Jin wook Lee, Hongyang Li, Yuanfang Guan, Chun-Chieh Chang, Jen-Chien Chang, Alex Hawkins-Hooker, Bernhard Schölkopf, Gabriele Schweikert, Mateo Rojas Carulla, Arif Canakoglu, Francesco Guzzo, Luca Nanni, Marco Masseroli, Mark James Carman, Pietro Pinoli, Chenyang Hong, Kevin Y. YipJeffrey P. Spence, Sanjit Singh Batra, Yun S. Song, Shaun Mahony, Zheng Zhang, Wuwei Tan, Yang Shen, Yuanfei Sun, Minyi Shi, Jessika Adrian, Richard Sandstrom, Nina Farrell, Jessica Halow, Kristen Lee, Lixia Jiang, Xinqiong Yang, Charles Epstein, J. Seth Strattan, Bradley Bernstein, Michael Snyder, Manolis Kellis, William Stafford, Anshul Kundaje

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)
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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.
Original languageEnglish
Article number79
Number of pages22
JournalGenome Biology
Volume24
Issue number1
DOIs
Publication statusPublished - 18 Apr 2023

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