Description Usage Arguments Details Value Author(s) Examples

Estimate the mean abundance and variance of each protein in each condition.

1 | ```
estimateVar(data, annotation)
``` |

`data` |
Data matrix with protein abundance. Rows are proteins and columns are Biological replicates or samples. |

`annotation` |
Group information for samples in data. ‘BioReplicate’ for sample ID and ‘Condition’ for group information are required. ‘BioReplicate’ information should be the same with the column of ‘data’. |

The function fits intensity-based linear model on the input data ‘data’. This function outputs variance components and mean abundance for each protein.

*model* is the list of linear models trained for each protein.

*mu* is the mean abundance matrix of each protein in each phenotype group.

*sigma* is the sd matrix of each protein in each phenotype group.

*promean* is the mean abundance vector of each protein across all the samples.

*protein* is proteins, correpsonding to the rows in *mu* and *sigma* or the element of *promean*.

Ting Huang, Meena Choi, Olga Vitek

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ```
data(OV_SRM_train)
data(OV_SRM_train_annotation)
# estimate the mean protein abunadnce and variance in each condition
variance_estimation <- estimateVar(data = OV_SRM_train,
annotation = OV_SRM_train_annotation)
# the mean protein abundance in each condition
head(variance_estimation$mu)
# the standard deviation in each condition
head(variance_estimation$sigma)
# the mean protein abundance across all the conditions
head(variance_estimation$promean)
``` |

MSstatsSampleSize documentation built on Oct. 31, 2019, 2:22 a.m.

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