Modified least-variant set normalization for miRNA microarray

cDNA Microarray Data Normalization

Supplementary Report: Normalization of oligonucleotide ar- rays based on the least-variant set of genes

Figure S1: Plot of the t-statistic versus the log standard-error for RMA expression values of the Golden-Spike data normalized using different methods. All normalization were performed after summarization of probe intensities. The FC1-based normalizations are ideal, and in real nonspike-in studies are not possible. LVS-normalization is closest to the FC1-based normalization. The others show neg...

Impact of normalization on miRNA microarray expression profiling.

Profiling miRNA levels in cells with miRNA microarrays is becoming a widely used technique. Although normalization methods for mRNA gene expression arrays are well established, miRNA array normalization has so far not been investigated in detail. In this study we investigate the impact of normalization on data generated with the Agilent miRNA array platform. We have developed a method to select...

Normalization of Microarray Data

DNA microarrays are a powerful technology for analysis of gene expression levels within cells. Both the cDNA and oligonucleotide technologies for microarrays are fairly young and are prone to a broad collection of errors and inaccuracies [KYML02]. In this paper we briefly discuss some sources of error and detail a variety of methods for normalizing the data with respect to some of these error s...

Automatically Extracting Variant-Normalization Pairs for Japanese Text Normalization

Social media texts, such as tweets from Twitter, contain many types of nonstandard tokens, and the number of normalization approaches for handling such noisy text has been increasing. We present a method for automatically extracting pairs of a variant word and its normal form from unsegmented text on the basis of a pair-wise similarity approach. We incorporated the acquired variant-normalizatio...