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All data were filtered for anti-aliasing and sampled with 1000 Hz.
All data were filtered for statistical significance (P<0.05) using t-tests in MultiExperiment Viewer (http://www.tm4.org/mev.html).
Prior to merging, the iControlDB data were filtered for individuals with <90% genotyping and SNPs with <90% genotyping, MAF <1%, or HWE p-value <0.00001.
The data were filtered for signal over background of greater than 1.5 in the channel measuring aaRNA from extract, and only features that met these criteria in >50% of the arrays were included for further analysis.
To focus on genes with reliable measurements, the normalized data were filtered for data that had present call in ≥33% of all samples (based on flag information of Agilent Feature Extraction Software v.9.5.3) and the genes chosen by parametric test (don't assume variances equal) with a Bejamini and Hochberg false discovery rate (FDR) <0.01 and twofold restriction filters were utilized.
Sequence data were filtered for non-recombinant clones.
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The background subtracted LOWESS (for time course data) or global linearly (steady state experiment) normalized data were filter for spots with intensity at least 20% above background.
2018 data was filtered for drivers in the Millennial age range of 22 to 37 to find the most common vehicles driven by Millennials.
The data was filtered for flags, then selected based on expression levels greater than 2-fold.
The data was filtered for signal vs. background using several parameters.
Expression data was filtered for a 3 fold change across samples, with a minimal "delta" value of 50.
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