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pmartr
pmartR:Panomics Marketplace - Quality Control and Statistical Analysis for Panomics Data
Provides functionality for quality control processing and statistical analysis of mass spectrometry (MS) omics data, in particular proteomic (either at the peptide or the protein level), lipidomic, and metabolomic data, as well as RNA-seq based count data and nuclear magnetic resonance (NMR) data. This includes data transformation, specification of groups that are to be compared against each other, filtering of features and/or samples, data normalization, data summarization (correlation, PCA), and statistical comparisons between defined groups. Implements methods described in: Webb-Robertson et al. (2014) <doi:10.1074/mcp.M113.030932>. Webb-Robertson et al. (2011) <doi:10.1002/pmic.201100078>. Matzke et al. (2011) <doi:10.1093/bioinformatics/btr479>. Matzke et al. (2013) <doi:10.1002/pmic.201200269>. Polpitiya et al. (2008) <doi:10.1093/bioinformatics/btn217>. Webb-Robertson et al. (2010) <doi:10.1021/pr1005247>.
Maintained by Lisa Bramer. Last updated 16 days ago.
data-summarizationlipidsmass-spectrometrymetabolitesmetabolomics-datapeptidesproteinsrna-seq-analysisopenblascpp
40 stars 7.69 score 144 scripts
rishvish
DImodelsVis:Visualising and Interpreting Statistical Models Fit to Compositional Data
Statistical models fit to compositional data are often difficult to interpret due to the sum to 1 constraint on data variables. 'DImodelsVis' provides novel visualisations tools to aid with the interpretation of models fit to compositional data. All visualisations in the package are created using the 'ggplot2' plotting framework and can be extended like every other 'ggplot' object.
Maintained by Rishabh Vishwakarma. Last updated 7 months ago.
3.70 score 7 scripts