| LLSimpute {BNArray} | R Documentation |
Impute missing values using local least squares algorithm.
LLSimpute(df.all, df.ori, n.changed)
df.all |
an object of data.frame which contains whole microarray data |
df.ori |
an object of data.frame which contains microarray data
of differentially expressed genes |
n.changed |
the number of differentially expressed genes |
LLSimpute employs local least squares algorithm to impute missing values in microarray data.
Local least squares algorithm estimates missing values in a gene express vector according to
the vector's most similar complete express vectors. The missing values are computed as the linear
combination of the corresponding data in the nearest neighbor genes. Normally the coefficients could
be computed but in rare cases when one gene misses too many values in total experiments, they take values
NA. Therefore, after calling LLSimpute, we have to FinalImpute function to
ensure a complete database for constructing Bayesian networks.
A data.frame object is returned with imputed data.
Xiaohui Chen cxh1984@interchange.ubc.ca,
Kaida Ning aprilnkd@hotmail.com,
Ming Chen mchen@zju.edu.cn.
Hyunsoo Kim, Gene H. Golub and Haesum Park (2005) Missing value estimation for DNA microarray gene expression data: local least squares imputation, Bioinformatcs, 21, 187-198
## Not run:
library(BNArray)
data(total.data)
setwd("/home")
##SHOULD call LLSimpute first
ori.compact<-LLSimpute(total.data$df.all,total.data$df.ori,total.data$n.changed)## End(Not run)