Xiang Zhou1, Hua Chai1, Huiying Zhao2, Ching-Hsing Luo1, Yuedong Yang1,3. 1. School of Data and Computer Science, Sun Yat-sen University, 132 East Waihuan Road, Guangzhou 510006, China. 2. Sun Yat-sen Memorial Hospital, Sun Yat-sen University, 107 Yan Jiang West Road, Guangzhou 510120, China. 3. Key Laboratory of Machine Intelligence and Advanced Computing (Sun Yat-sen University), Ministry of Education, 132 East Waihuan Road, Guangzhou 510006, China.
Abstract
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets. RESULTS: Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7-11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. CONCLUSIONS: TDimpute is an effective method for RNA-seq imputation with limited training samples.
BACKGROUND: Gene expression plays a key intermediate role in linking molecular features at the DNA level and phenotype. However, owing to various limitations in experiments, the RNA-seq data are missing in many samples while there exist high-quality of DNA methylation data. Because DNA methylation is an important epigenetic modification to regulate gene expression, it can be used to predict RNA-seq data. For this purpose, many methods have been developed. A common limitation of these methods is that they mainly focus on a single cancer dataset and do not fully utilize information from large pan-cancer datasets. RESULTS: Here, we have developed a novel method to impute missing gene expression data from DNA methylation data through a transfer learning-based neural network, namely, TDimpute. In the method, the pan-cancer dataset from The Cancer Genome Atlas (TCGA) was utilized for training a general model, which was then fine-tuned on the specific cancer dataset. By testing on 16 cancer datasets, we found that our method significantly outperforms other state-of-the-art methods in imputation accuracy with a 7-11% improvement under different missing rates. The imputed gene expression was further proved to be useful for downstream analyses, including the identification of both methylation-driving and prognosis-related genes, clustering analysis, and survival analysis on the TCGA dataset. More importantly, our method was indicated to be useful for general purposes by an independent test on the Wilms tumor dataset from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) project. CONCLUSIONS: TDimpute is an effective method for RNA-seq imputation with limited training samples.
Authors: John Vivian; Arjun Arkal Rao; Frank Austin Nothaft; Christopher Ketchum; Joel Armstrong; Adam Novak; Jacob Pfeil; Jake Narkizian; Alden D Deran; Audrey Musselman-Brown; Hannes Schmidt; Peter Amstutz; Brian Craft; Mary Goldman; Kate Rosenbloom; Melissa Cline; Brian O'Connor; Megan Hanna; Chet Birger; W James Kent; David A Patterson; Anthony D Joseph; Jingchun Zhu; Sasha Zaranek; Gad Getz; David Haussler; Benedict Paten Journal: Nat Biotechnol Date: 2017-04-11 Impact factor: 54.908
Authors: Katherine A Hoadley; Christina Yau; Denise M Wolf; Andrew D Cherniack; David Tamborero; Sam Ng; Max D M Leiserson; Beifang Niu; Michael D McLellan; Vladislav Uzunangelov; Jiashan Zhang; Cyriac Kandoth; Rehan Akbani; Hui Shen; Larsson Omberg; Andy Chu; Adam A Margolin; Laura J Van't Veer; Nuria Lopez-Bigas; Peter W Laird; Benjamin J Raphael; Li Ding; A Gordon Robertson; Lauren A Byers; Gordon B Mills; John N Weinstein; Carter Van Waes; Zhong Chen; Eric A Collisson; Christopher C Benz; Charles M Perou; Joshua M Stuart Journal: Cell Date: 2014-08-07 Impact factor: 41.582
Authors: Safoora Yousefi; Fatemeh Amrollahi; Mohamed Amgad; Chengliang Dong; Joshua E Lewis; Congzheng Song; David A Gutman; Sameer H Halani; Jose Enrique Velazquez Vega; Daniel J Brat; Lee A D Cooper Journal: Sci Rep Date: 2017-09-15 Impact factor: 4.379