In ErikEnsminger/FastRPA: Analysis tool for root length measurements

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Introduction

FastRPA is a tool for the filtering, organizing, analysing and plotting of root length measurement data. This tool enables users to select root length measurements from multiple files containing root length measurements. This allows to organize data in acentrale location from numerous data files. In addition, root measurements can be normalized according to the control Treatment. A plot can also be generated to have a simple visualization of the data. This document gives a tour of the FastRPA package.

To download FastRPA, use the following commands:

require("devtools")
devtools::install_github("ErikEnsminger/FastRPA", build_vignettes = TRUE)
library("FastRPA")

To list all functions available in this package:

ls("package:FastRPA")

To view sample dataset in this package:

data(package = "FastRPA")

To run the shiny app of this package:

FastRPA::runFastRPA()

Components

There are 3 functions in this package: filterData, analyseRootData, and createRootBarG.

filterData allows for the user to input a file containing root length measurements, select certain root length measurements from this file and add them to a user defined output file. Repeating the filterData for numerous files containing different root length measurements allows for the fast collection of measurements to one file.

analyseRootData allows for the user to take the output file from filterData (which contains the all the different root measurements) and normalize the data based on a user defined control Treatment that must exist in the data set. The mean of each root length with respect to root treatment and the geneLines is then calculated. The resulting data is then exported to a new user defined output file.

createRootBarG allows for the bar plotting of root length measurements from the output file of analyseRootData, with respect to the experimental treatment and the gene lines.

Example

Here is an example workflow using all 3 functions. All the input and output data for each functions can be found under under the file path: FastRPA/inst/extdata/

Using filterData:

Precondition the user input: Column name for root length measurements must be: Longest Shortest Path

input <- system.file("extdata", "inputFileFunc1.csv", package = "FastRPA")
roi <- c(2,1,5)
outputFile <- system.file("extdata", "outputFileFunc1.csv", package = "FastRPA")
filterData <- filterData(inputFile = input, roi = roi, rootLines = "ASER.12.32",
                           rootTreatment = "TEX", outputFile = outputFile)

Output:

**Output Explanation:** Here column 1 contains the gene lines, in this case *ASER.12.32* The remaining columns will contain the respective root Treatments with the rows containing the root length measurements (cm).

**Using `analyseRootData`:**
Precondition for the user input file: Last column in the .csv file must contain the control treatment. wzxhzdk:6 **Output:**

**Output Explanation:** Again, the first column will contain all the gene lines (i.e. geneLines). The remaining columns names will contain the root treatments that were applied and the root lengths (i.e. MEX, TEX, WES). Column 'ControlMean' contains the averages root length for the control treatment with respect to root lines. The column names that contain a `/` character contain the normalized data. (i.e. have been divided by the user defined control treatment) (i.e. MEX/TEX and TEX/WES).

**Using `createRootBar`:**
Precondition for user input file: Columns with treatment values to be plotted must include `/` character wzxhzdk:7 **Output:**

**Output Explanation:** The output will be a bar plot, which will compare the average normalized root length measurements of root treatments with respect to the gene lines (see legend)

## Package References

[Ensminger, E. (2021) FastRPA (Root length measurement analysis tool): an R package for organizing, analysing and visualization of plant root length measurement data) ](https://github.com/ErikEnsminger/FastRPA)

## Other References

Steipe B., ABC project (.utility 4.07) A Bioinformatics Course: Applied Bioinformatics http://steipe.biochemistry.utoronto.ca/abc/index.php/Bioinformatics_Main_Page R Core Team (2021). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ Wickham H (2007). “Reshaping Data with the reshape Package.” *Journal of Statistical Software*, 21(12), 1–20. http://www.jstatsoft.org/v21/i12/. Wickham H (2011). “The Split-Apply-Combine Strategy for Data Analysis.” *Journal of Statistical Software*, 40(1), 1–29. http://www.jstatsoft.org/v40/i01/. Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. *Springer-Verlag* New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org. Wickham, H. and Bryan, J. (2019). *R Packages* (2nd edition). Newton, Massachusetts: O’Reilly Media. https://r-pkgs.org/
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ErikEnsminger/FastRPA documentation built on Sept. 29, 2023, 12:35 a.m.

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