Eliminate the effects of systematic bias identified in eig_norm1() Ref: "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition" Karpievitch YV, Taverner T et al. 2009, Bioinformatics Ref: "Metabolomics data normalization with EigenMS" Karpievitch YK, Nikolic SB, Wilson R, Sharman JE, Edwards LM Submitted to PLoS ONE.
return value from the eig_norm1 if user wants to change the number of bias trends that will be eliminated h.c in rv should be updates to the desired number
A structure with multiple components
matrix of normalized abundances with 2 columns of protein and peptdie names
matrix of normalized abundances, no extra columns
trends found in raw data, bias trends up to h.c
trends in normalized data, if one wanted to plot at later time
peptides excluded due to not enough peptides or exception in fitting a linear model
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data(mm_peptides) head(mm_peptides) # different from parameter names as R uses outer name # spaces if variable is undefined intsCols = 8:13 metaCols = 1:7 # reusing this variable m_logInts = make_intencities(mm_peptides, intsCols) m_prot.info = make_meta(mm_peptides, metaCols) m_logInts = convert_log2(m_logInts) grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG')) set.seed(123) # set for repoducubility of eig_norm1 mm_m_ints_eig1 = eig_norm1(m=m_logInts,treatment=grps,prot.info=m_prot.info) mm_m_ints_eig1$h.c # check the number of bias trends detected mm_m_ints_norm = eig_norm2(rv=mm_m_ints_eig1)
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