GISTIC_2.0 (v7) BETA

Summary

The GISTIC module identifies regions of the genome that are significantly amplified or deleted across a set of samples. Each aberration is assigned a G-score that considers the amplitude of the aberration as well as the frequency of its occurrence across samples. False Discovery Rate q-values are then calculated for the aberrant regions, and regions with q-values below a user-defined threshold are considered significant. 

For each significant region, a “peak region” is identified, which is the part of the aberrant region with greatest amplitude and frequency of alteration. In addition, a “wide peak” is determined using a leave-one-out algorithm to allow for errors in the boundaries in a single sample. The “wide peak” boundaries are more robust for identifying the most likely gene targets in the region. 

Each significantly aberrant region is also tested to determine whether it results primarily from broad events (longer than half a chromosome arm), focal events, or significant levels of both. The GISTIC module reports the genomic locations and calculated q-values for the aberrant regions. It identifies the samples that exhibit each significant amplification or deletion, and it lists genes found in each “wide peak” region. 

Note: The GISTIC module is memory-intensive. 

References

Mermel C, Schumacher S, et al. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biology. 2011;12:R41. 

Beroukhim R, Mermel C, et al. The landscape of somatic copy-number alteration across human cancers. Nature. 2010;463:899-905. 

Parameters

* - required

Input Files

Output Files

1. All Lesions File (all_lesions.conf_XX.txt, where XX is the confidence level)
   The all lesions file summarizes the results from the GISTIC run. It contains data about the significant regions of amplification and deletion as well as which samples are amplified or deleted in each of these regions. The identified regions are listed down the firstcolumn, and the samples are listed across the first row, starting in column 10.
   Region Data
   Columns 1-9 present the data about the significant regions as follows:

   1. Unique Name: A name assigned to identify the region.
   2. Descriptor: The genomic descriptor of that region
   3. Wide Peak Limits: The “wide peak” boundaries most likely to contain the targeted genes. These are listed in genomic coordinates and marker (or probe) indices.
   4. Peak Limits: The boundaries of the region of maximal amplification or deletion.
   5. Region Limits: The boundaries of the entire significant region of amplification or deletion.
   6. q values: The q-value of the peak region.
   7. Residual q values after removing segments shared with higher peaks : The q-value of the peak region after removing (“peeling off”) amplifications or deletions that overlap other more significant peak regions in the same chromosome.
   8. Broad or Focal: Identifies whether the region reaches significance due primarily to broad events (called “broad”), focal events (called “focal”), or independently significant broad and focal events (called “both”).
   9. Amplitude Threshold: Key giving the meaning of values in the subsequent columns associated with each sample.

   Sample Data

   Each of the analyzed samples is represented in one of the columns following the lesion data (columns 10 through end). The data contained in these columns varies slightly by section of the file.

   The first section can be identified by the key given in column 9 – it starts in row 2 and continues until the row that reads Actual Copy Change Given. This section contains summarized data for each sample. A ‘0’ indicates that the copy number of the sample was not amplified or deleted beyond the threshold amount in that peak region. A ‘1’ indicates that the sample had low-level copy number aberrations (exceeding the low threshold indicated in column 9), and a ‘2’ indicates that the sample had high-level copy number aberrations (exceeding the high threshold indicated in column 9).

    

   The second section can be identified as the rows in which column 9 reads Actual Copy Change Given. The second section exactly reproduces the first section, except that here the actual changes in copy number are provided rather than zeroes, ones, and twos.

    

   The final section is similar to the first section, except that here only broad events are included. A "1" in the samples columns (columns 10+) indicates that the median copy number of the sample across the entire significant region exceeded the threshold given in column 9. That is, it indicates whether the sample had a geographically extended event, rather than a focal amplification or deletion covering little more than the peak region.

    

   
2. Amplification Genes File (Amp_genes.conf_XX.txt, where XX is the confidence level)
3. Deletion Genes File (Del_genes.conf_XX.txt, where XX is the confidence level)
   Tables of deletion peaks, followed by the genes contained in them, organized in "ragged columns." The del genes file contains one column for each deletion identified in the GISTIC analysis. The file format for the del genes file is identical to the format for the amp genes file.
   
4. GISTIC Scores File (scores.gistic)
   A table of segmented q-values, scores, and amplification/deletion frequencies for the sample set. The scores file lists the q-values [presented as -log10(q)], G-scores, average amplitudes among aberrant samples, and frequency of aberration, across the genome for both amplifications and deletions. The scores file is viewable with the Integrative Genomics Viewer (IGV).
5. Segmented Copy Number (raw_copy_number.pdf and raw_copy_number.png)
   The segmented copy number file (both PDF and PNG) is a heat map image of the segmented copy number profiles in the input data.
6. Amplification GISTIC plot (amp_qplot.pdf and amp_qplot.png)
   The amplification plot (in both PDF and PNG format) shows the G-scores (top) and q-values (bottom) with respect to amplifications for all markers over the entire region analyzed.
   
7. Deletion GISTIC plot (del_qplot.pdf and del_quplot.png)
   The deletion plot (in both PDF and PNG format) shows the G-scores (top) and q-values (bottom) with respect to deletions for all markers over the entire region analyzed.
   
8. all_thresholded.by_genes.txt
   The table in this file is obtained by applying both low- and high-level thresholds to the gene copy levels of all the samples. The entries with value +/- 2 exceed the high-level thresholds for amps/dels, and those with +/- 1 exceed the low-level thresholds but not the high-level thresholds. The low-level thresholds are just the 'amplifications_threshold' and 'deletions_threshold' noise threshold input values (typically 0.1 or 0.3) and are the same for every threshold.
   By contrast, the high-level amplification (or deletion) thresholds are calculated on a sample-by-sample basis and are based on the maximum (or minimum) median arm-level amplification (or deletion) copy number found in the sample. The idea, for deletions anyway, is that this level is a good approximation for hemizygous given the purity and ploidy of the sample. The actual cutoffs used for each sample can be found in a table in the sample_cutoffs.txt file.
9. Other result files include:
   • regions_track.conf_XX.bed
   • broad_significance_results.txt (only output if run.broad.analysis is set to "yes")
   • broad_values_by_arm.txt (only output if run.broad.analysis is set to "yes")
   • freqarms_vs_ngenes.pdf (only output if run.broad.analysis is set to "yes")
   • arraylistfile.txt (only output if an array.list.file is provided as input)
   • all_data_by_genes.txt
   • broad_data_by_genes.txt
   • focal_data_by_genes.txt
   • sample_cutoffs.txt
   • amp_qplot.v2.pdf and amp_qplot.v2.ps (do not contain gene labels)
   • del_qplot.v2.pdf and del_qplot.v2.ps (do not contain gene labels)

Troubleshooting

Please see the GenePattern FAQ (http://www.broadinstitute.org/cancer/software/genepattern/doc/faq) for assistance with specific errors. 

Example Data

• Example segmentation file (the segmentation file contains segmented data for all the samples identified by some segmentation algorithm)
• Example markers file (the markers file identifies the marker names and positions of the markers in the original dataset before segmentation)
• Example array list file (the array list file is an optional file identifying the subset of samples to be used in the analysis)
• Example CNV file (the optional CNV file identifies CNVs by either marker name or genomic location)

Task Type:
SNP Analysis

CPU Type:
64-bit

Operating System:
Linux

Language:
MATLAB