Abstract: The aim of the present invention is to estimate EDR directions in a single index model composed of a large number of variables with simple calculations without using the inverse matrix of the variance-covariance matrix and principle component analysis. Data conversion means 21 receives, from an input device 3, data to be analyzed, the data composed of sets of response variables and explanatory variables, standardizes the explanatory variables, and sends them to slice average calculating means 22. The slice average calculating means 22 divides the data into two slices with reference to the median of the response variables to calculate the mean vector of the explanatory variables on a slice basis. The calculated mean vectors are sent to EDR direction calculating means 23. The EDR direction calculating means 23 calculates the difference between the mean vectors for respective slices to estimate an EDR direction.

Abstract: A gene expression state estimating system according to the present invention includes an input device 1 for inputting microarray data, a program-controlled data analyzer 2, and an output device 3. The data analyzer 2 has parameter estimating means 21 and 22 for estimating distributed parameters for each component of a mixed normal distribution and a mixing ratio parameter using gene expression level data given from the input device 1, and posterior probability calculating means 23 for calculating the posterior probabilities of gene expression in each channel using each of the estimated parameters. The calculated posterior probabilities are outputted to the output device 3.

Abstract: In a clustering apparatus comprising an input unit (1) supplied with a dataset including a plurality of samples, a data processing unit (4) for processing the samples to classify each sample into a class, and an output unit (3) for producing a processing result representative of classification, a parameter memory (51) in a memory unit (5) memorizes a target parameter obtained from past experiment. A parameter estimating section (24) of the data processing unit estimates a clustering parameter by the use of the target parameter memorized in the parameter memory. An unidentifiable sample detecting section (25) of the data processing unit detects a sample as an unidentifiable sample if posterior probabilities calculated for the sample by a probability density function produced by the clustering parameter estimated by the parameter estimating section are smaller than a predetermined value.

Abstract: The aim of the present invention is to estimate EDR directions in a single index model composed of a large number of variables with simple calculations without using the inverse matrix of the variance-covariance matrix and principle component analysis. Data conversion means 21 receives, from an input device 3, data to be analyzed, the data composed of sets of response variables and explanatory variables, standardizes the explanatory variables, and sends them to slice average calculating means 22. The slice average calculating means 22 divides the data into two slices with reference to the median of the response variables to calculate the mean vector of the explanatory variables on a slice basis. The calculated mean vectors are sent to EDR direction calculating means 23. The EDR direction calculating means 23 calculates the difference between the mean vectors for respective slices to estimate an EDR direction.

Abstract: More precise correction of global and local distortions of microarray data and correction of measurement errors caused by a difference in sensitivity between fluorescent dyes. A data standardization unit for a first process inputs gene expression intensity data from an input device, standardizes the gene expression intensity data by using grid-by-grid order statistics on the assumption that most genes are in a non-expression state, and outputs the standardized gene expression intensity data. A spot-position-based correction unit for a second process estimates a distortion depending on a spot position on a grid by grid basis by a nonparametric smoothing method and outputs gene expression intensity data whose distortion depending on the spot position has been corrected.