sSeq: A Simple and Shrinkage Approach of Differential Expression Analysis for RNA-Seq experiments
نویسندگان
چکیده
In this section, we will use the Hammer et. al. data [1] to illustrate how to use the functions in the sSeq package. The two conditions are control Sprague Dawley after 2 months (Condition A) and L5 SNL Sprague Dawley after 2 months (Condition B). There are two samples within each condition. This data is included in the sSeq package as an example, and can be imported as follows. “countsTable” is a matrix or data frame in which a column represents a sample and a row represents a gene. “conds.Hammer” is a characteristic vector, and used to define the conditions corresponding to the samples in columns. After defining the input counts table and the groups for conditions, the function “nbTestSH” can be utilized to obtain the regularized dispersion estimates and perform the exact tests. The output is a data frame in which the “pval” column includes the p-values of the exact tests.
منابع مشابه
Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size
MOTIVATION RNA-seq experiments produce digital counts of reads that are affected by both biological and technical variation. To distinguish the systematic changes in expression between conditions from noise, the counts are frequently modeled by the Negative Binomial distribution. However, in experiments with small sample size, the per-gene estimates of the dispersion parameter are unreliable. ...
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Maintainer Danni Yu Depends R (>= 3.0), caTools, RColorBrewer Description The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The meth...
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