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Learn what the ASCAT algorithm does and how it accounts for complications of aneuploidy, tumor heterogeneity, and contamination from normal cells in tumor sample analysis.
GISTIC helps to identify regions that are significantly gained or lost across a set of samples, giving a greater weight to high-amplitude events which are less likely to represent random aberrations.
Enrichment analysis identifies GO terms and the genes associated with these that are significantly over-represented in the aberrant regions. Learn more.
The method applies the frequency of aberration at a location across the entire sample set and a footprint as the interval lengths of overlapping aberrations.
Learn what aberrations are common, and two popular approaches (GISTIC and STAC) used to determine which are statistically significant.
Gain a better understanding of what mosaicism is, how to identify it, and how it affects the calling algorithms.
Learn about the different quality measures, how the quality score is calculated, and how to determine what is a good quality score.
Gain a better understanding of how to identify systematic biases in your data. Then, learn some different correction approaches, and when and how to apply them.
Learn some basic terms used when analyzing chromosomal copy number aberrations and allelic imbalances.
Get an integrated analysis of copy number and sequence variation using data from The Cancer Genome Atlas (TCGA), and learn how CNV, allelic events, and seq. variations can aid in the discovery process in cancer studies.
Get a general overview of the basic principles of array CGH including how arrays measure copy number, signal intensities, probe mapping, experiment/reference sample ratios, etc.
Learn a few different ways to re-center (median, mode, median probe intensity of known diploid regions) and in what scenarios (e.g. aCGH arrays, SNP arrays) to use a specific re-centering method.
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