I'm starting to use TCGA data. Level 3 data SNP data determining CNV have the following format:
Sample Chromosome Start End Num_Probes Segment_Mean
BEDEW_p_TCGA_FFPE_7_13_N_GenomeWideSNP_6_A05_1347396 1 61735 836713 68 -0.0961
BEDEW_p_TCGA_FFPE_7_13_N_GenomeWideSNP_6_A05_1347396 1 836746 3208375 663 0.4065
BEDEW_p_TCGA_FFPE_7_13_N_GenomeWideSNP_6_A05_1347396 1 3212946 16007300 7302 0.0227
BEDEW_p_TCGA_FFPE_7_13_N_GenomeWideSNP_6_A05_1347396 1 16007754 16088317 33 0.4554
The TCGA consortium includes segments with very small segment means (e.g. 0.02, line 3 above), but a large number of involved probes (e.g. 7302, line 3 above). Does anyone have any opinion on how to interpret these? Such a large number of involved probes makes me think they're more than noise, but the small segment mean suggests there's barely an increase in this segment. Does it represent a subclone comprising a small fraction of the tumor?
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joshwarrick •
These small variations in segment levels are only there because there is uncertainty in calling the "true" level. There is of course underlying heterogeneity in many tumor samples, but 0.02 is not really even a deviation from 0. If you plot the distribution of discrete levels among all chromosomes for the sample you will see that there is a range around 0 for your normal copy number segments and (ideally) discrete deviations from this representing gains and losses. For subclonality you could of course look for deviations from expected levels, but that can just as well come from adjacent normal cells or infiltrating immune cells. In general I would say its good to look at the distribution of levels for each tumor to get a feel for the data.
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Mattias Aine
Thanks for the reply. That makes a lot of sense.
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joshwarrick •
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