The SeqKnn Package January 13, 2005 Version 1.0.0 1.0.0 Date 2005-1-3 Title Sequential KNN imputation method Version 1.0.0 1.0.0 Author Ki-Yeol Kim and Gwan-Su Yi, Cbio lab., Information and Communications University Maintainer Ki-Yeol Kim Ki-Yeol Kim Depends R (>= 2.0.0) Description This function estimates missing values sequentially from the gene that had least missing rate in microarray data Maintainer Ki-Yeol Kim Ki-Yeol Kim License free for non-commercial use. URL http://rosetteer.icu.ac.kr
Description This function estimates missing values sequentially from the gene that has least missing rate in microarray data, using weighted mean of k nearest neighbors. This function requires ’nnmiss’. Usage SeqKNN(data, k) 1
2
khan05
Arguments data
matrix or dataframe, 1 row corresponds to 1 gene, 1 column to 1 sample,colnames and rownames can be used
k
number of nearest neighbors
Details ’SeqKNN’ separates the dataset into incomplete and complete set that has or has not missing values respectively. The genes in incomplete set are imputed by the order of missing rate. Missing value is filled by the weighted mean value of corresponding column of the nearest neighbor genes in complete set. Once all missing values in a gene are imputed, the imputed gene is moved into the complete set and used for the imputation of the rest of genes in incomplete set. In this process, all missing values in one gene can be imputed simultaneously from the selected neighbor genes in complete set. This reduces execution time from previously developed KNN method that selects nearest neighbors for each imputation. Author(s) Ki-Yeol Kim and Gwan-Su Yi References Ki-Yeol Kim, Byoung-Jin Kim, Gwan-Su Yi (2004.Oct.26) "Reuse of imputed data in microarray analysis increases imputation efficiency", BMC Bioinformatics 5:160. Examples ## Not run: data(khan05) imputedData
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