Selected article for: "ANOVA test and variance analysis"

Author: Chin-Yi Chu; Xing Qiu; Matthew N. McCall; Lu Wang; Anthony Corbett; Jeanne Holden-Wiltse; Christopher Slaunwhite; Qian Wang; Christopher Anderson; Alex Grier; Steven R. Gill; Gloria S. Pryhuber; Ann R. Falsey; David J. Topham; Mary T. Caserta; Edward E. Walsh; Thomas J Mariani
Title: Insufficiency in airway interferon activation defines clinical severity to infant RSV infection
  • Document date: 2019_5_20
  • ID: bx49tbui_28
    Snippet: Due to the fact that our study spanned three years, all samples were processed as six library batches. We noticed that there are significant batch effects in the total number of mapped reads of these 175 samples. In addition, using an analysis of variance (ANOVA) F-test with false discovery rate (FDR) controlled at 0.05 level, we found 3,984 genes (28.8% of the expressed transcriptome) had significantly different mean expression across batches. B.....
    Document: Due to the fact that our study spanned three years, all samples were processed as six library batches. We noticed that there are significant batch effects in the total number of mapped reads of these 175 samples. In addition, using an analysis of variance (ANOVA) F-test with false discovery rate (FDR) controlled at 0.05 level, we found 3,984 genes (28.8% of the expressed transcriptome) had significantly different mean expression across batches. Based on these observations, we used ComBat 36 to remove batch effects. As expected, after applying ComBat, no gene had a significant batch effect based on an ANOVA F-test. To avoid spurious findings due to outliers, we also winsorized the data at 1% and 99% levels. Specifically, if an observation was less (greater) than 99% of the data, we replaced its value by the 1% (99%) sample quantile.

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