Missing value imputation for epistatic MAPs |
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Authors: | Colm Ryan Derek Greene Gerard Cagney Pádraig Cunningham |
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Institution: | (1) School of Computer Science and Informatics, University College Dublin, Dublin, Ireland;(2) Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Dublin, Ireland |
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Abstract: | Background Epistatic miniarray profiling (E-MAPs) is a high-throughput approach capable of quantifying aggravating or alleviating genetic
interactions between gene pairs. The datasets resulting from E-MAP experiments typically take the form of a symmetric pairwise
matrix of interaction scores. These datasets have a significant number of missing values - up to 35% - that can reduce the
effectiveness of some data analysis techniques and prevent the use of others. An effective method for imputing interactions
would therefore increase the types of possible analysis, as well as increase the potential to identify novel functional interactions
between gene pairs. Several methods have been developed to handle missing values in microarray data, but it is unclear how
applicable these methods are to E-MAP data because of their pairwise nature and the significantly larger number of missing
values. Here we evaluate four alternative imputation strategies, three local (Nearest neighbor-based) and one global (PCA-based),
that have been modified to work with symmetric pairwise data. |
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