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DiPhiSeq (version 0.2.0)

Robust Tests for Differential Dispersion and Differential Expression in RNA-Sequencing Data

Description

Implements the algorithm described in Jun Li and Alicia T. Lamere, "DiPhiSeq: Robust comparison of expression levels on RNA-Seq data with large sample sizes" (Unpublished). Detects not only genes that show different average expressions ("differential expression", DE), but also genes that show different diversities of expressions in different groups ("differentially dispersed", DD). DD genes can be important clinical markers. 'DiPhiSeq' uses a redescending penalty on the quasi-likelihood function, and thus has superior robustness against outliers and other noise. Updates from version 0.1.0: (1) Added the option of using adaptive initial value for phi. (2) Added a function for estimating the proportion of outliers in the data. (3) Modified the input parameter names for clarity, and modified the output format for the main function.

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Version

Install

install.packages('DiPhiSeq')

Monthly Downloads

145

Version

0.2.0

License

GPL-3

Maintainer

Jun Li

Last Published

October 24th, 2018

Functions in DiPhiSeq (0.2.0)

diphiseq

Main function. For most users, this function is all what they need for the analysis.
example_data

Numeric count matrix of example data.
outprop

Give a rough estimate of the proportion of outliers in the data based on the results of DiPhiSeq.
robnb

Calculates the estimate and standard error of beta and phi. It takes as input counts from one group of samples for a single gene. This function is the core underlining function of the whole package. A significant part of the code is edited based on William H. Aeberhard's glmrob.nb R function; we appreciate them very much for sharing their code online. This function also implement Algorithm 1 of our submitted paper about DiPhiSeq. This function is called by robtest. Most users don't need to call this function directly.
robtest

Calls the robnb function to estimate the coefficients, and then construct the statistical tests for DD and DE. It works for a single gene. y1 and y2 are count vectors for a single gene. diphiseq calls this function to do the calculation for each gene. Normal users often don't need to use this function directly.