View source: R/TwoPart_MultiMS.R
convert_log2 replaces 0's with NA's than does a log2 transformation Replacing 0's with NA's is the correct approach to Proteomics data analysis as 0's are not values that should be left in the data where no observation was made, see citation below. Karpievitch et al. 2009 "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition". PMID: 19602524 Karpievitch et al. 2009 "A statistical framework for protein quantitation in bottom-up MS-based proteomics". PMID: 19535538
a dataframe of raw intensities in format: (# peptides)x(# samples+possibly peptide & protein information (metadata))
vector of column indecies that make up the intensities usually in sequential order but do not have to be user is responsible for making sure that specified columns are indeed numeric and correspond to intensities for each sample
matrix of log2 transforemd intensities where 0's were replaced with NA's prior to transformation
data(mm_peptides) head(mm_peptides) intsCols = 8:13 metaCols = 1:7 m_logInts = make_intencities(mm_peptides, intsCols) m_prot.info = make_meta(mm_peptides, metaCols) m_logInts = convert_log2(m_logInts) # 0's replaced with NAs and # log2 transnform applied
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