Multi-Dataset Model-based Differential Expression Proteomics
Analysis Platform
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Documentation for package ‘ProteoMM’ version 1.26.0
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 |