eig_norm1 | R Documentation |

First portion of EigenMS: Identify eigentrends attributable to bias, allow the user to adjust the number (with causion! if desired) before normalizing with eig_norm2. 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 2014, PLoS ONE

eig_norm1(m, treatment, prot.info, write_to_file = "")

`m` |
number of peptides x number of samples matrix of log-transformed expression data, metadata not included in this matrix |

`treatment` |
either a single factor indicating the treatment group of each sample i.e. [1 1 1 1 2 2 2 2...] or a data frame of factors, eg: treatment= data.frame(cbind(data.frame(Group), data.frame(Time)) |

`prot.info` |
2+ colum data frame, pepID, prID columns IN THAT ORDER. IMPORTANT: pepIDs must be unique identifiers and will be used as Row Names If normalizing non-proteomics data, create a column such as: paste('ID_',seq_len(num_rows), sep=”) Same can be dome for ProtIDs, these are not used for normalization but are kept for future analyses |

`write_to_file` |
if a string is passed in, 'complete' peptides (peptides with NO missing observations) will be written to that file name |

A structure with multiple components

- m, treatment, prot.info, grp
initial parameters passed into the function, returned for future reference

- my.svd
matrices produced by SVD

- pres
matrix of peptides that can be normalized, i.e. have enough observations for ANOVA

- n.treatment
number of factors passed in

- n.u.treatment
number of unique treatment facotr combinations, eg: Factor A: a a a a c c c c Factor B: 1 1 2 2 1 1 2 2 then: n.treatment = 2; n.u.treatment = 4

- h.c
number of bias trends identified

- present
names/IDs of peptides in variable 'pres'

- complete
complete peptides with no missing values, these were used to compute SVD

- toplot1
trends automatically identified in raw data, can be plotted at a later time

- Tk
scores for each bias trend, eigenvalues

- ncompl
number of complete peptides with no missing observations

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) # will reuse the name m_prot.info = make_meta(mm_peptides, metaCols) m_logInts = convert_log2(m_logInts) # 3 samples for CG and 3 for mCG grps = as.factor(c('CG','CG','CG', 'mCG','mCG','mCG')) # ATTENTION: SET RANDOM NUMBER GENERATOR SEED FOR REPRODUCIBILITY !! set.seed(123) # Bias trends are determined via a permutaion, results may # vary slightly if a different seed is used, such as when set.seed() # function is not used 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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