The goal of FastRPA is to speed up the interpretation time of root
length measurement data. Manually selecting, organizing and analysing
data of potentially thousands of root measurements are time consuming.
FastRPA enables a semi-automatic solution, enabling the user to select
root length measurements from the multiple output file of FastIDR imageJ
tool, thereby centralizing data from numerous data files. This package
was developed using R version 4.1.1 (2021-08-10,
Platform: x86_64-apple-darwin17.0 (64-bit) and
Running under: macOS Big Sur 11.4
You can install the development version of FastRPA like so:
require("devtools")
devtools::install_github("ErikEnsminger/FastRPA", build_vignettes = TRUE)
library("FastRPA")Shiny app:
FastRPA::runFastRPA() ls("package:FastRPA")
data(package = "FastRPA")Look into the vignettes for a specific example process flow:
browseVignettes("FastRPA")An overview of the pacakge is illustrated below.
FastRPA contains 3 functions: filterData, analyseRootData, and createRootBarG. filterData is the first function in the process. As input for filterData the user must specify a column index of the root measurements to be extracted, the .csv input file (Fig.1 A.i. and Fig.1 A.ii.) , an output file, and the Treatment type used on the roots.It will allow the user to select different Root length measurements and the corresponding Gene Lines from a user defined .csv files. analyseRootData takes in an input file (prefferably the output file from filterData Fig. 1B), the control Treatment, and an output file. analyseRootData will then based on the indciated controlTreatment normalize each of the treatment data and output the data to the desired output file (Fig. 1C).
createRootBarG takes in an input file (the output file from analyseRootData (Fig.1C)). createRootBarG_ creates a bar plot of the specified treatments and their corresponding gene lines (Fig. 1.D).
An overview of the package R functions illustrated below in Figure 1.:
The author of this package is Erik Ensminger. The filterData
function makes use of split function of the plyr R package for binding
data frames. The createRootBarG function makes use of the reshape2
R package for the melting dataframes. In addition, createRootBarG
function makes use of the ggplot R package for plotting the Bar graph
of the root length data.
Steipe B., ABC project (.utility 4.07) A Bioinformatics Course: Applied Bioinformatics http://steipe.biochemistry.utoronto.ca/abc/index.php/Bioinformatics_Main_Page
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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/
This package was developed as part of an assessment for 2021 BCB410H: Applied Bioinfor- matics, University of Toronto, Toronto, CANADA.