Using a Pearson correlation cut-off >0.6 combined with a gene expression log2 standard deviation cut-off >0.9, we identified 34 genes to be positively co-expressed with CCNB1 (Table S1).
However, when performing a CG analysis in the Neve et al. cell line panel we found PLK1, AURKB and CENPE to be correlated (Pearson correlation >0.5) with CCNB1 given a gene expression log2 standard deviation cut-off >0.4.
Only 24% (12 of 50) of persons from disease-endemic regions had values greater than the two standard deviation cut-off value compared with the Indian NEC group.
In addition, data were filtered based on a stringent standard deviation cut-off of 0.075 between replicate spots, with those exceeding this cut-off excluded from further analysis.
Sample adaptive thresholds for calling gain or loss can be generated by performing steps 1 3 in Figure 5 using the same form of data input and standard deviation cut-off criteria.
To select genes whose mRNA were either destabilized or stabilized in differentiated cells in both LIF− and RA conditions, we used one standard deviation cut-off of log decay rates (Supplementary Fig. S5A).
Thresholds were estimated using a chromosomal moving window of size 1% of the total probe number for each chromosome separately and the standard deviation cut-off value was selected as the median of the standard deviation distribution.
Hybridizations were generally performed as duplicates and outlier data points from four spots (duplicate spots from duplicate chips) were filtered out using a mean ± 1.2 times standard deviation cut-off.
The identified standard deviation cut-off value can be scaled by multiplicative factors to generate sample specific gain/loss thresholds of desired stringency for downstream applications, e.g., calling CNAs after segmentation.
Bound regions were then defined by stretches of array probes (passing the three standard deviation cut-off) at least 360 bp in length and a region was extended as long as there was a value within 360 bp of the previous value.
