or k-means cluster analysis (Panel C), which reveals underlying patterns that can help identify subgroups in the data set. In microarray analysis, typically genes are sought that show patterns of ...

Resampling-based methods can address this last point, but results indicate that most clusterings in microarray data sets are unlikely to reflect reproducible patterns or patterns in the overall ...

Biologically relevant patterns of expression are typically ... Finally, as standards for publication of microarray experiments evolve, reporting data transformations is becoming as important ...

Microarrays are wonderful tools for seeing global patterns of gene expression under different conditions. Plenty of software exists to make sense of the data, but what if you want to correlate ...

Microarray technology ... The focus of Zhang's group is to look at automated detection of patterns and to devise rules for interpreting that data, using various types of analyses, including a ...

Methods: TM4: a free, open-source system for microarray data management and analysis was used in order to identify expression patterns of interest in our cohort of renal cell carcinomas (RCC) (n=80).

Subtype-specific patterns were identifiable ... Xiang for data analysis support and generation of the Gene Expression Omnibus microarray data set. We are grateful to Dr Jeff Lawrence, Karen Yu, James ...

We validated the model with data from an independent cohort ... microarray training set was validated in the microarray testing set, and the patterns of gene expression found on microarray ...

In contrast, microarray data is specific to the format, so data sets cannot be easily compared between platforms. Microarrays are also easier to use and more suitable for high-throughput experiments.