sample.size.evaluation: The function executes and plots results from ssize and delta...
In oneChannelGUI: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops
Description
Usage
Details
Author(s)
Description
This function represents avisual tool for helping users to understand the trade off between sample size and statistical power.
To know more about see ssize help.
Usage
1
Details
Both ssize and delta outputs are calculated using the BH type I error correction instead of the Bonferroni
used as default in the ssize package.
Furthermore, instead using the control group variance, this implementation uses the common variance described
in Wei et al. BMC Genomics. 2004, 5:87
Main assumptions:
A microarray experiment is set up to compare gene expressions between one treatment group and one control group.
Microarray data has been normalized and transformed so that the data for each gene is sufficiently close to a
normal distribution that a standard 2-sample pooled-variance t-test will reliably detect differentially expressed genes.
Author(s)
Raffaele A Calogero
oneChannelGUI documentation built on Nov. 17, 2017, 11:02 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Details Author(s)
Description
This function represents avisual tool for helping users to understand the trade off between sample size and statistical power. To know more about see ssize help.
Usage
1 |
Details
Both ssize and delta outputs are calculated using the BH type I error correction instead of the Bonferroni used as default in the ssize package. Furthermore, instead using the control group variance, this implementation uses the common variance described in Wei et al. BMC Genomics. 2004, 5:87
Main assumptions: A microarray experiment is set up to compare gene expressions between one treatment group and one control group. Microarray data has been normalized and transformed so that the data for each gene is sufficiently close to a normal distribution that a standard 2-sample pooled-variance t-test will reliably detect differentially expressed genes.
Author(s)
Raffaele A Calogero
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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