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Activity Number:
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70
- Novel Approaches for Omics and Multi-Omics Analysis
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Type:
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Contributed
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Date/Time:
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Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #323649
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Title:
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Fold Change Estimation Variation in MicroRNA Data with Application to an Environmentally Exposed Residential Cohort
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Author(s):
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Christina Pinkston* and Shesh Rai
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Companies:
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University of Louisville and University of Louisville
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Keywords:
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Bias correction;
Covariate adjusted;
Differential Expression;
FirePlex (R);
Fold change;
miRNA
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Abstract:
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A common measure of differential expression in microRNA (miRNA) studies is log-fold change between two groups without adjusting for covariates. When the mean expression levels are distributed log-normally and expressed in values other than counts, some commonly available tools may not be used. Here, we explore the impact of heteroscedasticity and additional bias introduced from covariates on the estimated log-fold change values and their standard errors using simulated data in log-linear models fitted with or without covariates or with or without weights. We then apply our methods to a real dataset of normalized miRNA levels measured in a residential cohort exposed to environmental toxins and pollutants with the Fireplex(R) platform technology by Abcam. The results suggest the potential for differing log-fold change variability as expressed by the standard errors when comparing estimates calculated with and without correction for these biases.
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Authors who are presenting talks have a * after their name.
