consensus_SFT_fit {BioNERO} | R Documentation

## Pick power to fit networks to scale-free topology

### Description

Pick power to fit networks to scale-free topology

### Usage

    consensus_SFT_fit(
      exp_list,
      setLabels = NULL,
      metadata = NULL,
      cor_method = "spearman",
      net_type = "signed hybrid",
      rsquared = 0.8
    )

### Arguments

`exp_list` | A list of expression data frames or SummarizedExperiment objects. If input is a list of data frames, row names must correspond to gene IDs and column names to samples. The list can be created with `list(exp1, exp2, ..., expn)`.
`setLabels` | Character vector containing labels for each expression set.
`metadata` | A data frame containing sample names in row names and sample annotation in the first column. Ignored if 'exp_list' is a list of 'SummarizedExperiment' objects, since the function will extract colData.
`cor_method` | Correlation method used for network reconstruction. One of "spearman" (default), "biweight", or "pearson".
`net_type` | Network type. One of "signed hybrid" (default), "signed" or "unsigned".
`rsquared` | Minimum R squared to consider the network similar to a scale-free topology. Default is 0.8.

### Value

A list of 2 elements:

power

  Numeric vector of optimal beta powers to fit networks to SFT

plot

  A ggplot object displaying main statistics of the SFT fit test

### Examples

    set.seed(12)
    data(zma.se)
    filt.zma <- filter_by_variance(zma.se, n=500)
    zma.set1 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
    zma.set2 <- filt.zma[, sample(colnames(filt.zma), size=20, replace=FALSE)]
    list.sets <- list(zma.set1, zma.set2)
    cons_sft <- consensus_SFT_fit(list.sets, setLabels = c("Maize1", "Maize2"),
                                  cor_method = "pearson")

[Package BioNERO version 1.0.4 Index]