Subsampling results using the hammer dataset

The subsample object ss is the result from applying the subsample function to the hammer data set. The hypothesis test was a simple two-sample comparison (control vs. L5 SNL). Voom, DESeq2 and edgeR were used to test for differential expression at three different subsampling proportions: 0.01, 0.1 and 1. Genes with less than 5 counts across all replicates were filtered. For more details on how the object was generated, please see the subsample function.

The subsample object can then be used to determine whether an experiment has adequate read depth (see plot and summary functions).

Hammer, P., Banck, M. S., Amberg, R., Wang, C., Petznick, G., Luo, S., Khrebtukova, I., Schroth, G. P., Beyerlein, P., and Beutler, A. S. (2010). mRNA-seq with agnostic splice site discovery for nervous system transcriptomics tested in chronic pain. Genome research, 20(6), 847-860.

Frazee, A. C., Langmead, B., and Leek, J. T. (2011). ReCount: a multi-experiment resource of analysis-ready RNA-seq gene count datasets. BMC Bioinformatics, 12, 449.

  • ss
# import the subsampling object (see ?subsample to see how ss is created)

# summarise object
sum_ss <- summary(ss)
if (interactive()) {
Documentation reproduced from package subSeq, version 1.2.2, License: MIT + file LICENSE

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