DESnowball-package: A R package implemented Snowball approach (see references)

Description Details References


Genome-wide differential gene expression analysis with respect to the presence of a recurrent genetic disturbance (a driver mutation)


The DESnowball package implements a differential gene expression analysis tool that compares the whole genome gene expression profiles on samples relative to the presence of a recurrent genetic disturbance (driver mutation).

The input data for the snowball analysis are the profiling of the whole genome gene expression and the mutation status of a recurrent genetic event on a group of samples. The analysis has been tested on human primary tumor samples and the minimum sample size required per group is three. Snowball does not require a balanced design between groups (see references).

The main function of the package is snowball, it requires two input data, named y and X, where y is a binary vector indicating the mutation status of the samples, and X is the gene expression profiles with rows corresponding to genes and columns the samples. y can be a numerical, character or logical vector. It can also be a factor. The typical format is a character vector with two values indicating the the mutation status of each subject. X is expected to be a data.frame with gene names as its row names, and typically it is after the initial filtering and in log scale. A reasonable choice for the initial filtering could be based on the variation of gene expression across all the samples in the study, e.g., using the coefficient of variation of each gene to select the ones with greater values than a given cutoff.

The other functions include plotJn for visualizing gene selection, select.features for gene ranking and statistical significance assessment, and toplist to report the top genes based on the user provided cutoff.


Xu, Y. and Sun, J. (2005) PfCluster: a new cluster analysis procedure for gene expression profiles. Presented at a conference on Nonparametric Inference and Probability With Applications to Science honoring Michael Woodroofe; September 24-25, 2005; Ann Arbor, Mich, 2005.

McArdlei, B.H. and Anderson, M.J. (2001) Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82(1): 290-297.

Xu, Y., Guo, X., Sun, J. and Zhao. Z. Snowball: resampling combined with distance-based regression to discover transcriptional consequences of driver mutation, manuscript.

Guo, X., Xu, Y. and Zhao, Z.. Driver mutation BRAF regulates cell proliferation and apoptosis via MITF in the pathogenesis of melanoma, manuscript.

DESnowball documentation built on May 29, 2017, 9:10 a.m.