Missing Values Estimation in Microarray Data with Partial Least Squares Regression
نویسندگان
چکیده
Microarray data usually contain missing values, thus estimating these missing values is an important preprocessing step. This paper proposes an estimation method of missing values based on Partial Least Squares (PLS) regression. The method is feasible for microarray data, because of the characteristics of PLS regression. We compared our method with three methods, including ROWaverage, KNNimpute and LLSimpute, on different data and various missing probabilities. The experimental results show that the proposed method is accurate and robust for estimating missing values.
منابع مشابه
Collateral Missing Value Estimation: Robust Missing Value Estimation for Consequent Microarray Data Processing
Microarrays have unique ability to probe thousands of genes at a time that makes it a useful tool for variety of applications, ranging from diagnosis to drug discovery. However, data generated by microarrays often contains multiple missing gene expressions that affect the subsequent analysis, as most of the times these missing values are ignored. In this paper we have analyzed how accurate esti...
متن کاملEvaluation of Missing Value Estimation for Microarray Data
Microarray gene expression data contains missing values (MVs). However, some methods for downstream analyses, including some prediction tools, require a complete expression data matrix. Current methods for estimating the MVs include sample mean and K-nearest neighbors (KNN). Whether the accuracy of estimation (imputation) methods depends on the actual gene expression has not been thoroughly inv...
متن کاملMissing value estimation for DNA microarray gene expression data: local least squares imputation
MOTIVATION Gene expression data often contain missing expression values. Effective missing value estimation methods are needed since many algorithms for gene expression data analysis require a complete matrix of gene array values. In this paper, imputation methods based on the least squares formulation are proposed to estimate missing values in the gene expression data, which exploit local simi...
متن کاملWeighted Local Least Squares Imputation Method for Missing Value Estimation
Missing values often exist in the data of gene expression microarray experiments. A number of methods such as the Row Average (RA) method, KNNimpute algorithm and SVDimpute algorithm have been proposed to estimate the missing values. Recently, Kim et al. proposed a Local Least Squares Imputation (LLSI) method for estimating the missing values. In this paper, we propose a Weighted Local Least Sq...
متن کاملEstimation of Regression Models with Equi-correlated Responses When Some Observations on the Response Variable Are Missing
The present article deals with the problem of estimation of parameters in a linear regression model when some data on response variable is missing and the responses are equicorrelated. The ordinary least squares and optimal homogeneous predictors are employed to nd the imputed values of missing observations. Their eeciency properties are analyzed using the small disturbances asymptotic theory. ...
متن کامل