sd_selection {BioTIP} | R Documentation

## Selecting Highly Oscillating Transcripts

### Description

`sd_selection` pre-selects highly oscillating transcripts from the input dataset `df`. The dataset must contain multiple sample groups (or 'states'). For each state, the function filters the dataset using a cutoff value for standard deviation. The default cutoff value is 0.01 (i.e., higher than the top 1% standard deviation).

### Usage

    sd_selection(df, samplesL, cutoff = 0.01, method = c("other",
      "reference", "previous", "itself", "longitudinal reference"),
      control_df = NULL, control_samplesL = NULL)

### Arguments

`df` | A numeric matrix or data frame. The rows and columns represent unique transcript IDs (geneID) and sample names, respectively.
`samplesL` | A list of vectors, whose length is 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'.
`cutoff` | A positive numeric value. Default is 0.01. If < 1, automatically selects top x transcripts using the a selecting method (which is either the `reference`, `other` stages or `previous` stage), e.g. by default it will select top 1% of the transcripts.
`method` | Selection 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 trancript in df is also in control_df.
`control_df` | A count matrix with unique loci as row names and samples names of control samples as column names, only used for method `longitudinal reference`
`control_samplesL` | A list of characters with stages as names of control samples, required for method 'longitudinal reference'

### Value

`sd_selection()` A list of data frames, whose length is the number of states. The rows in each data frame are the filtered transcripts with highest standard deviation selected from `df` and based on an assigned cutoff value. Each resulting data frame represents a subset of the raw input `df`, with the sample ID of the same state in the column.

### Author(s)

Zhezhen Wang zhezhen@uchicago.edu

### See Also

`optimize.sd_selection`

### Examples

    counts = matrix(sample(1:100,18),2,9)
    colnames(counts) = 1:9
    row.names(counts) = c('loci1','loci2')
    cli = cbind(1:9,rep(c('state1','state2','state3'),each = 3))
    colnames(cli) = c('samples','group')
    samplesL <- split(cli[,1],f = cli[,'group'])
    test_sd_selection <- sd_selection(counts, samplesL, 0.01)

[Package BioTIP version 1.0.0 Index]