Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform


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Documentation for package ‘ProteoMM’ version 1.4.0

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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