Top variables in mass spectrometry analysis
ms_top.RdShow top genes/peptides defined by significance level and absolute fold change.
Usage
ms_top(x, level = c("Gene", "Peptide"), alpha = 0.05, fc = 1.25, path = NULL)Value
A data frame showing the top variables. If level = "Gene", the
return value is a 4 column data frame, showing the Gene, Accession, BH
adjusted omnibus p-value, and absolute fold change columns from x.
If level = "Peptide", the return value is the same except the Gene
and Accession columns are replaced with the AGDSM column from x.
Details
The input x is the result matrix returned by ms_summarize. We
want to filter x such that only interesting variables remain: i.e.
those that show statistical significance in an overall test and a
scientifically relevant effect size. Variables of interest are summarized
either on the gene level or peptide level.
By default, the level of statistical significance is set to 5\ Benjamini-Hochberg adjusted omnibus test p-value. The minimum absolute fold change for determining scientific relevance is 1.25 by default. These default values can be modified for different studies or projects, but offer a general measure of validity for users to start with.
Note
The fold change criterion only needs to be satisfied for one
comparison if the experiment has 3 or more sample groups. For example,
suppose we have 1 control group and treatments A and B. We filter variables
on the fold change criterion where the absolute fold change is greater than
fc for either A vs. control or B vs. control.
See also
Other Mass Spectrometry functions:
ms_condition(),
ms_plot,
ms_process(),
ms_summarize()