The Ability of Different Imputation Methods to Preserve the Significant Genes and Pathways in Cancer

Overview

Journal Genomics Proteomics Bioinformatics

Specialty Biology

Date 2017 Dec 17

PMID 29247873

Citations 4

Authors

Rosa Aghdam

Taban Baghfalaki

Pegah Khosravi

Elnaz Saberi Ansari

Affiliations

Soon will be listed here.

Abstract

Deciphering important genes and pathways from incomplete gene expression data could facilitate a better understanding of cancer. Different imputation methods can be applied to estimate the missing values. In our study, we evaluated various imputation methods for their performance in preserving significant genes and pathways. In the first step, 5% genes are considered in random for two types of ignorable and non-ignorable missingness mechanisms with various missing rates. Next, 10 well-known imputation methods were applied to the complete datasets. The significance analysis of microarrays (SAM) method was applied to detect the significant genes in rectal and lung cancers to showcase the utility of imputation approaches in preserving significant genes. To determine the impact of different imputation methods on the identification of important genes, the chi-squared test was used to compare the proportions of overlaps between significant genes detected from original data and those detected from the imputed datasets. Additionally, the significant genes are tested for their enrichment in important pathways, using the ConsensusPathDB. Our results showed that almost all the significant genes and pathways of the original dataset can be detected in all imputed datasets, indicating that there is no significant difference in the performance of various imputation methods tested. The source code and selected datasets are available on http://profiles.bs.ipm.ir/softwares/imputation_methods/.

Citing Articles

Removing the association of random gene sets and survival time in cancers with positive random bias using fixed-point gene set.

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Significant random signatures reveals new biomarker for breast cancer.

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