calc_aov_padj: Perform ANOVA, calculate p-values and adjust for multiple...
In tkostas/komics: Tools to analyze omics data

Reads a dataframe, identifies the sample groups based on the input info, and performs ANOVA to identify rows that show difference in at least one of the groups. P-values are adjusted for multiple hypothesis testing error, and the output is returned as a dataframe. If the number of observations is not enought for the ANOVA test, the entry is labeled as excluded.

1	calc_aov_padj(x, sample_names, group_names, pAdj = "BH")

`x`	A dataframe containing the protein/gene identifier. Each row is one protein/gene and each column is one samlple. Additional columns can be included in this dataframe. The samples will be matched based on the `sample_names` vector. However because samples are identified by partial matching (`grep` function) try to reduce the number of columns if you encounter an error.
`sample_names`	A vector containing the sample names. Names will be matched partially. For this make sure the you don't have multiple columns that match.
`group_names`	A vector of equal length with the `sample_names` containing the group name for each sample. You can store in a separate dataframe the `sample_names` and the `group_names` and just reference to this table.
`pAdj`	Method for adjustment of p-values. See more at `p.adjust`. Default method is "BH".

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proc_data <- calc_aov_padj(my_data,
                           sample_names = sample_groups$samples,
                           group_names = sample_groups$groups)