1. Normalization of RNA-Seq data using adaptive trimmed mean with multi-reference.
- Author
-
Singh V, Kirtipal N, Song B, and Lee S
- Subjects
- Humans, Algorithms, Sequence Analysis, RNA methods, Computational Biology methods, Gene Expression Profiling methods, ROC Curve, Software, RNA-Seq methods
- Abstract
The normalization of RNA sequencing data is a primary step for downstream analysis. The most popular method used for the normalization is the trimmed mean of M values (TMM) and DESeq. The TMM tries to trim away extreme log fold changes of the data to normalize the raw read counts based on the remaining non-deferentially expressed genes. However, the major problem with the TMM is that the values of trimming factor M are heuristic. This paper tries to estimate the adaptive value of M in TMM based on Jaeckel's Estimator, and each sample acts as a reference to find the scale factor of each sample. The presented approach is validated on SEQC, MAQC2, MAQC3, PICKRELL and two simulated datasets with two-group and three-group conditions by varying the percentage of differential expression and the number of replicates. The performance of the present approach is compared with various state-of-the-art methods, and it is better in terms of area under the receiver operating characteristic curve and differential expression., (© The Author(s) 2024. Published by Oxford University Press.)
- Published
- 2024
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