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Activity Number:
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384
- Next-Generation Sequencing and High-Dimensional Data
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Type:
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Contributed
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Date/Time:
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Thursday, August 12, 2021 : 12:00 PM to 1:50 PM
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Sponsor:
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Biometrics Section
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Abstract #318732
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Title:
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Detecting Differential Expressed/Spliced Transcripts That Are Associated with Continuous Clinical Covariates, Including Survival Time
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Author(s):
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Huining Kang* and Xichen Li
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Companies:
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Univeristy of New Mexico and University of New Mexico
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Keywords:
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differential isoform expression;
time-to-event;
missing data;
mixture model;
EM-algorithm
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Abstract:
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RNA-sequencing technology has made it possible to reconstruct and quantify the alternative spliced isoform transcripts. We have recently developed a mixed-effects model approach for identifying genes with differentially expressed/spliced transcripts associated with categorical clinical characteristics (https://pubmed.ncbi.nlm.nih.gov/33035235/). We extend the method to identifying those that are associated with the continuous variables, including survival time. We propose a mixture model approach coped with the EM-Algorithm to make the best use of the observations with missing/censoring data to improve the statistic power. We demonstrate our method through application to an RNA-Sequencing dataset from a study of children’s acute myeloid leukemia (AML).
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Authors who are presenting talks have a * after their name.
