setup_contrasts: Setup contrasts for downstream Differential Expression...

In ftwkoopmans/msdap: Mass Spectrometry Downstream Analysis Pipeline

Setup contrasts for downstream Differential Expression Analysis

Description

For more fine-grained control over specified contrasts, use the functions remove_contrasts() and add_contrast().

Usage

setup_contrasts(dataset, contrast_list, random_variables = NULL)

Arguments

dataset	your dataset. Make sure you've already imported sample metadata (so each sample is assigned to a sample group)
contrast_list	a list that captures all contrasts that you want to compare. Check the examples for details.
random_variables	a vector of column names in your sample metadata table that are added as additional(!) regression terms in each statistical contrast tested downstream. Note that not all DEA algorithms may support this, consult documentation on individual methods for more info (start at dea_algorithms() )

Details

Note that a MS-DAP contrast for "A vs B" will return foldchanges for B/A in downstream output tables and data visualizations. For example, for the contrast "control vs disease" a positive log2 foldchange implies protein abundances are higher in the "disease" sample group.

Examples

# a simple wild-type knockout study with only 2 groups, WT and KO
## Not run: contrast_list = list(c("WT", "KO"))
# multiple contrasts
## Not run: contrast_list = list(c("cntrl", "group1"), c("cntrl", "group2"), c("group1", "group2"))
# An example of multi-groups. The first contrast is just control-vs-group1
# as in the previous example. In the second contrast we compare control against
# multiple-groups combined as if they were one. Note the nested lists!
## Not run: 
contrast_list = list(c("control", "group1"), list("control", c("group1", "group2")))

## End(Not run)

ftwkoopmans/msdap documentation built on March 5, 2025, 12:15 a.m.