data_prepare {SingleCellSignalR} | R Documentation

## Data Prepare

### Description

Prepares the data for further analysis

### Usage

    data_prepare(
      file,
      most.variables = 0,
      lower = 0,
      upper = 0,
      normalize = TRUE,
      write = FALSE,
      verbose = TRUE,
      plot = FALSE
    )

### Arguments

`file` | a string for the scRNAseq data file
`most.variables` | a number
`lower` | a number in [0,1], low quantile threshold
`upper` | a number in [0,1], high quantile threshold
`normalize` | a logical, if TRUE, then computes 99th percentile normalization
`write` | a logical
`verbose` | a logical
`plot` | a logical

### Details

'file' is the path to the file containing the read or UMI count matrix the user wants to analyze.

'most.variables' can be set to N to select the Nth most variables genes. This option allows the user to use a reduced matrix (N x number of cells) to perform the clustering step faster.

'lower' and 'upper' are used to remove the genes whose average counts are outliers. The values of these arguments are fractions of the total number of genes and hence must be between 0 and 1. Namely, if 'lower = 0.05', then the function removes the 5 removes the 5

If 'normalize' is FALSE, then the function skips the 99th percentile normalization and the log transformation.

If 'write' is TRUE, then the function writes two text files. One for the normalized and gene thresholded read counts table and another one for the genes that passed the lower and upper threshold. Note that the length of the genes vector written in the *genes.txt* file is equal to the number of rows of the table of read counts written in the *data.txt* file.

### Value

The function returns a data frame of filtered and/or normalized data with genes as row names.

### Examples

    file <- system.file("scRNAseq_dataset.txt",package = "SingleCellSignalR")
    data <- data_prepare(file = file)

[Package SingleCellSignalR version 1.2.0 Index]