# Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform

## Documentation for package ‘ProteoMM’ version 1.30.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS

## Help Pages

convert_log2 | Convert values in a matrix to log2 transfored values
eigen_pi | Compute PI - proportion of observations missing completely at random
eig_norm1 | Identify bias trends
eig_norm2 | EigenMS normalization
g.test | G Test for presence - absence analysis
get_presAbs_prots | Get Presence/Absence Proteins
hs_peptides | hs_peptides - peptide-level intensities for human
makeLMFormula | String linear model formula suitable
make_intencities | Subdivide data into intensities columns only
make_meta | Subdivide data into metadata columns only
MBimpute | Model-Based Imputation of missing values
mm_peptides | mm_peptides - peptide-level intensities for mouse
peptideLevel_DE | Model-Based differential expression analysis
peptideLevel_PresAbsDE | Presence/Absence peptide-level analysis
plot_1prot | Plot trends for a single protien
plot_3_pep_trends_NOfile | Plot peptide trends
plot_volcano | Volcano plot
plot_volcano_wLab | Volcano plot with labels for the differentially expressed proteins
prot_level_multiMat_PresAbs | Multi-Matrix Presence Absence analysis
prot_level_multi_part | Multi-Matrix Differentia Expression Analysis
subset_proteins | Subset proteins
sva.id | Surrogate Variable Analysis