Integrative missing value estimation for microarray data

Background: Missing value estimation is an important preprocessing step in microarray analysis. Although several methods have been developed to solve this problem, their performance is unsatisfactory for datasets with high rates of missing data, high measurement noise, or limited numbers of samples. In fact, more than 80% of the time-series datasets in Stanford Microarray Database contain less than eight samples. Results: We present the integrative Missing Value Estimation method (iMISS) by incorporating information from multiple reference microarray datasets to improve missing value estimation. For each gene with missing data, we derive a consistent neighbor-gene list by taking reference data sets into consideration. To determine whether the given reference data sets are sufficiently informative for integration, we use a submatrix imputation approach. Our experiments showed that iMISS can significantly and consistently improve the accuracy of the state-of-the-art Local Least Square (...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Jianjun Hu, Haifeng Li, Michael S. Waterman, Xianghong Jasmine Zhou
• Keywords: BMCBI 2006, Microarray Datasets, Reference Microarray Datasets, Value Estimation