Title |
MODMatcher: Multi-Omics Data Matcher for Integrative Genomic Analysis
|
---|---|
Published in |
PLoS Computational Biology, August 2014
|
DOI | 10.1371/journal.pcbi.1003790 |
Pubmed ID | |
Authors |
Seungyeul Yoo, Tao Huang, Joshua D. Campbell, Eunjee Lee, Zhidong Tu, Mark W. Geraci, Charles A. Powell, Eric E. Schadt, Avrum Spira, Jun Zhu |
Abstract |
Errors in sample annotation or labeling often occur in large-scale genetic or genomic studies and are difficult to avoid completely during data generation and management. For integrative genomic studies, it is critical to identify and correct these errors. Different types of genetic and genomic data are inter-connected by cis-regulations. On that basis, we developed a computational approach, Multi-Omics Data Matcher (MODMatcher), to identify and correct sample labeling errors in multiple types of molecular data, which can be used in further integrative analysis. Our results indicate that inspection of sample annotation and labeling error is an indispensable data quality assurance step. Applied to a large lung genomic study, MODMatcher increased statistically significant genetic associations and genomic correlations by more than two-fold. In a simulation study, MODMatcher provided more robust results by using three types of omics data than two types of omics data. We further demonstrate that MODMatcher can be broadly applied to large genomic data sets containing multiple types of omics data, such as The Cancer Genome Atlas (TCGA) data sets. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 11 | 55% |
United Kingdom | 2 | 10% |
India | 1 | 5% |
France | 1 | 5% |
Montenegro | 1 | 5% |
Unknown | 4 | 20% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Scientists | 11 | 55% |
Members of the public | 9 | 45% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 6% |
Turkey | 1 | 1% |
Germany | 1 | 1% |
Argentina | 1 | 1% |
Unknown | 75 | 90% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 28 | 34% |
Student > Ph. D. Student | 17 | 20% |
Student > Bachelor | 7 | 8% |
Professor | 5 | 6% |
Student > Doctoral Student | 4 | 5% |
Other | 13 | 16% |
Unknown | 9 | 11% |
Readers by discipline | Count | As % |
---|---|---|
Agricultural and Biological Sciences | 38 | 46% |
Biochemistry, Genetics and Molecular Biology | 13 | 16% |
Medicine and Dentistry | 10 | 12% |
Computer Science | 5 | 6% |
Neuroscience | 2 | 2% |
Other | 3 | 4% |
Unknown | 12 | 14% |