Scaling with ranked subsampling (SRS) Shiny app {SRS}

Shiny app for scaling with ranked subsampling (SRS)

Description

Shiny app for the determination of Cmin for scaling with ranked subsampling (SRS).

Usage

SRS.shiny.app(data)

Arguments

data

Data frame (species count or OTU table) in which columns are samples and rows are the counts of species or OTUs. Only integers are accepted as data.

Details

Shiny app that generates a visualization of retained samples, summary statistics, SRS curves, and an interactive table in response to varying minimum sample size (Cmin).

Value

Launches Shiny app for SRS in the default web browser.

Author(s)

Vitor Heidrich, Devon O'rourke, Petr Karlovsky, Lukas Beule

References

Beule L, Karlovsky P. 2020. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8:e9593

<https://doi.org/10.7717/peerj.9593>

Examples

##Samples should be arranged columnwise.
##Input data should not contain any categorial
##data such as taxonomic assignment or barcode sequences.
##An example of the input data can be found below:

example_input_data <- matrix(c(sample(1:20, 100, replace = TRUE),
sample(1:30, 100, replace = TRUE),sample(1:40, 100, replace = TRUE)), nrow = 100)
colnames(example_input_data) <- c("sample_1","sample_2","sample_3")
example_input_data <- as.data.frame(example_input_data)
example_input_data

##Launching the SRS shiny app with example_input_data as input
if (interactive()) {SRS.shiny.app(example_input_data)}