Description Usage Arguments Details Value Author(s) References Examples
Given a data matrix, this function will estimate the sequencing depths
on the basis of the Poisson goodness-of-fit statistic.
This estimate is applicable to data with any types of outcome, as it
estimates under the null hypothesis.
A more detailed instruction as well as sample data is available at
http://www.stanford.edu/~junli07/research.html
.
1 | PS.Est.Depth(n, iter=5, ct.sum=5, ct.mean=0.5)
|
n |
The data matrix. The rows are counts for a gene, and the columns are counts from an experiment. |
iter |
Number of iterations used. Default value: 5. The default value is usually a good choice. |
ct.sum |
if the total number of reads of a gene across all experiments <= ct.sum, this gene will not be considered for estimating sequencing depth. Default value: 5. |
ct.mean |
if the mean number of reads of a gene across all experiments <= ct.mean, this gene will not be considered for estimating sequencing depth. Default value: 0.5. |
The value in the data matrix does not need to be integers.
estimated sequencing depth. a vector. their product is 1.
Jun Li.
Li J, Witten DM, Johnstone I, Tibshirani R (2011). Normalization, testing, and false discovery rate estimation for RNA-sequencing data. To appear, Biostatistics.
1 2 | data(dat)
seq.depth <- PS.Est.Depth(dat$n)
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