sd_selection {BioTIP} | R Documentation

## Selecting Highly Oscillating Transcripts

### 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\ \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 transcripts 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'

`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. `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\ 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)

`optimize.sd_selection` Zhezhen Wang zhezhen@uchicago.edu

[Package BioTIP version 1.1.1 Index]