Reuse of imputed data in microarray analysis increases imputation efficiency

BACKGROUND: The imputation of missing values is necessary for the efficient use of DNA microarray data, because many clustering algorithms and some statistical analysis require a complete data set. A few imputation methods for DNA microarray data have been introduced, but the efficiency of the methods was low and the validity of imputed values in these methods had not been fully checked. RESULTS: We developed a new cluster-based imputation method called sequential K-nearest neighbor (SKNN) method. This imputes the missing values sequentially from the gene having least missing values, and uses the imputed values for the later imputation. Although it uses the imputed values, the efficiency of this new method is greatly improved in its accuracy and computational complexity over the conventional KNN-based method and other methods based on maximum likelihood estimation. The performance of SKNN was in particular higher than other imputation methods for the data with high missing rates and large number of experiments. Application of Expectation Maximization (EM) to the SKNN method improved the accuracy, but increased computational time proportional to the number of iterations. The Multiple Imputation (MI) method, which is well known but not applied previously to microarray data, showed a similarly high accuracy as the SKNN method, with slightly higher dependency on the types of data sets. CONCLUSIONS: Sequential reuse of imputed data in KNN-based imputation greatly increases the efficiency of imputation. The SKNN method should be practically useful to save the data of some microarray experiments which have high amounts of missing entries. The SKNN method generates reliable imputed values which can be used for further cluster-based analysis of microarray data

Controllable modification of transport properties of single-walled carbon nanotube field effect transistors with in situ Al decoration

We use an in situ Al decoration technique to control the transport characteristics of single-walled carbon nanotube field effect transistors (SWNT-FETs). Al nanoparticle decoration in a high vacuum caused the devices to change from p -type to n -type FETs, and subsequent exposure to the ambient atmosphere induced a gradual recovery of p -type character. In comparison with the bare SWNT-FETs under high vacuum, the channel-open devices with decorated Al particles exhibited reduced current under ambient conditions. However, selective Al decoration only at the contact resulted in an improved p -type current in ambient air.open182