optimize.sd_selection {BioTIP} | R Documentation

## Optimization of sd selection

### Usage

    optimize.sd_selection(
      df,
      samplesL,
      B = 100,
      percent = 0.8,
      times = 0.8,
      cutoff = 0.01,
      method = c("other", "reference", "previous", "itself", "longitudinal reference"),
      control_df = NULL,
      control_samplesL = NULL
    )

### Arguments

`df` | A dataframe of numerics. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively.
`samplesL` | A list of n vectors, where n equals to the number of states. Each vector gives the sample names in a state. Note that the vectors (sample names) has to be among the column names of the R object 'df'.
`B` | An integer indicating number of times to run this optimization, default 1000.
`percent` | A numeric value indicating the percentage of samples will be selected in each round of simulation.
`times` | A numeric value indicating the percentage of `B` times a transcript need to be selected in order to be considered a stable signature.
`cutoff` | A positive numeric value. Default is 0.01. If < 1, automatically goes to select top x# transcripts using the a selecting method (which is either the `reference`, `other` or `previous` stage), e.g. by default it will select top 1\ \item methodSelection of methods from `reference`, `other`, `previous`, default uses `other`. Partial match enabled. `itself`, or `longitudinal reference`. Some specific requirements for each option: `reference`, the reference has to be the first. `previous`, make sure `sampleL` is in the right order from benign to malign. `itself`, make sure the cutoff is smaller than 1. `longitudinal reference` make sure control_df and control_samplesL are not NULL. The row numbers of control_df is the same as df and all transcript in df are also in control_df. \item control_dfA count matrix with unique loci as row names and samples names of control samples as column names, only used for method `longitudinal reference`. \item control_samplesLA list of characters with stages as names of control samples, required for method 'longitudinal reference'.

A list of dataframe of filtered transcripts with the highest standard deviation are selected from `df` based on a cutoff value assigned. The resulting dataframe represents a subset of the raw input `df`. The `optimize.sd_selection` filters a multi-state dataset based on a cutoff value for standard deviation per state and optimizes. By default, a cutoff value of 0.01 is used. Suggested if each state contains more than 10 samples.

counts = matrix(sample(1:100, 30), 2, 30) colnames(counts) = 1:30 row.names(counts) = paste0('loci', 1:2) cli = cbind(1:30, rep(c('state1', 'state2', 'state3'), each = 10)) colnames(cli) = c('samples', 'group') samplesL <- split(cli[, 1], f = cli[, 'group']) test_sd_selection <- optimize.sd_selection(counts, samplesL, B = 3, cutoff =0.01) `sd_selection` Zhezhen Wang zhezhen@uchicago.edu

[Package BioTIP version 1.1.1 Index]