LFQ {DEP} | R Documentation

## LFQ workflow

### Description

`LFQ` is a wrapper function running the entire differential enrichment/expression analysis workflow for label free quantification (LFQ)-based proteomics data. The protein table from MaxQuant is used as direct input.

### Usage

    LFQ(proteins, expdesign, fun = c("man", "bpca", "knn", "QRILC", "MLE",
      "MinDet", "MinProb", "min", "zero", "mixed", "nbavg"), type = c("all",
      "control", "manual"), control = NULL, test = NULL,
      filter = c("Reverse", "Potential.contaminant"), name = "Gene.names",
      ids = "Protein.IDs", alpha = 0.05, lfc = 1)

### Arguments

`proteins` | Data.frame, The data object.
`expdesign` | Data.frame, The experimental design object.
`fun` | "man", "bpca", "knn", "QRILC", "MLE", "MinDet", "MinProb", "min", "zero", "mixed" or "nbavg", Function used for data imputation based on `manual_impute` and `impute`.
`type` | 'all', 'control' or 'manual', The type of contrasts that will be generated.
`control` | Character(1), The sample name to which the contrasts are generated (the control sample would be most appropriate).
`test` | Character, The contrasts that will be tested if type = "manual". These should be formatted as "SampleA_vs_SampleB" or c("SampleA_vs_SampleC", "SampleB_vs_SampleC").
`filter` | Character, Name(s) of the column(s) to be filtered on.
`name` | Character(1), Name of the column representing gene names.
`ids` | 'Character(1), Name of the column representing protein IDs.
`alpha` | Numeric(1), sets the false discovery rate threshold.
`lfc` | Numeric(1), sets the log fold change threshold.

### Value

A list of 9 objects:

`data` | data.frame containing the original data
`se` | SummarizedExperiment object containing the original data
`filt` | SummarizedExperiment object containing the filtered data
`norm` | SummarizedExperiment object containing the normalized data
`imputed` | SummarizedExperiment object containing the imputed data
`diff` | SummarizedExperiment object containing FDR estimates of differential expression
`dep` | SummarizedExperiment object annotated with logical columns indicating significant proteins
`results` | data.frame containing containing all results variables from the performed analysis
`param` | data.frame containing the test parameters

### Examples

    data <- UbiLength
    expdesign <- UbiLength_ExpDesign
    results <- LFQ(data, expdesign, 'MinProb', 'control', 'Ctrl')

[Package DEP version 1.9.0 Index]