Focal Input To Gistic (focal_input.seg.txt)

An image showing the correlation between gene counts and frequency of copy

Gene Counts vs. Copy Number Alteration Frequency (freqarms_vs_ngenes.{fig|pdf})

A file indicating the position of the confidence intervals around GISTIC

peaks that can be loaded as a track in a compatible viewer browser such as

Confidence Intervals (regions_track.conf_##.bed, where ## is the confidence level)

lost across a set of tumors. It takes segmented copy number ratios

as input, separates arm-level events from focal events, and then

performs two tests: (i) identifies significantly amplified/deleted

chromosome arms; and (ii) identifies regions that are significantly

focally amplified or deleted. For the focal analysis, the

significance levels (Q values) are calculated by comparing the

observed gains/losses at each locus to those obtained by randomly

permuting the events along the genome to reflect the null

hypothesis that they are all 'passengers' and could have occurred

anywhere. The locus-specific significance levels are then

corrected for multiple hypothesis testing. The arm-level

significance is calculated by comparing the frequency of

gains/losses of each arm to the expected rate given its size. The

method outputs genomic views of significantly amplified and

deleted regions, as well as a table of genes with gain or loss

scores. A more in depth discussion of the GISTIC algorithm and its

Regions of the genome that are prone to germ line variations in copy

number are excluded from the GISTIC analysis using a list of germ

line copy number variations (CNVs). A CNV is a DNA sequence that may

be found at different copy numbers in the germ line of two different

individuals. Such germ line variations can confound a GISTIC

analysis, which finds significant  somatic copy number variations in

cancer. A more in depth discussion is provided in

. GISTIC currently uses two CNV exclusion lists. One is based on the

literature describing copy number variation, and a second one comes

from an analysis of significant variations among the blood normals in