This R package aims to automatically process Fluorescence Recovery After Photobleaching (FRAP) data and generate consistent, publishable figures. The automation would bring down the hour-long routine work to a few seconds.
Tired of tedious manual quantification of raw images? Check out my
fraptrack repository :)
FRAP image courtesy of Dr. Michael Rosen's Lab:
(The top-left puncta is the targeted area.)
Compare results of any two groups with a single command.
Have tested with a real-world dataset from a FRAP experiment.
# remove sample(column) 1 and 3 from "mut1" data modified <- exclude(example_dataset, "mut1", c(1,3))
frapplot()now validate the input before execution. Raise Error when input is not valid and provide specific instructions.
View update history.
# Install frapplot from CRAN: install.packages("frapplot") # Install frapplot from Github: install.packages("devtools") devtools::install_github("GuanqiaoDing/frapplot") # Load frapplot library(frapplot) # bring up the manual ?frapprocess ?frapplot
Example use of frapprocess and frapplot:
# after the preprocessing (refer to ./data-raw/preprocess.R) load("data/example_dataset.rda") info <- frapprocess(example_dataset, seq(0, 145, 5)) # view results info$summary info$details # plot any two groups as desired frapplot ("output_dir", "control", "mut1", info) frapplot ("output_dir", "control", "mut2", info)
Raw data can either be in a single file or separate files, refer to preprocess.R for more information.
Make sure the names (case-sensitive) you provide to
frapplot() are correct;
Make sure "info" (the third argument) remains in your global environment and refers to the same experiment before you run
frapplot(), otherwise re-run
frapprocess() and get its return value.
frapplot() returns a list (if assigned to variable "info"):
info$time_points: a vector of time points
info$summary: a dataframe showing the summary of the regression including ymax, ymin, k, halftime, tau, total_recovery, total_recovery_sd.
info$sample_means: a matrix of sample means, nrow = num of time points, ncol = sample size
info$sample_sd: a matrix of standard deviations, nrow = num of time points, ncol = sample size
info$model: a list of models for each group from the non-linear regression
info$details: details of the regression for each group
frapplot() generates a pdf file that compares two groups of choice in the provided directory.
The preprocessing generates ".rda" file that is ready to be loaded. The code has been tested with the example dataset and generates expected results. Note that only five samples are included in each group of this dataset for demonstration, but larger sample size is highly recommended for statistical robustness.
The code has also passed R CMD check.
Please report any bugs or issues here. The project also welcomes your contribution.
frapplot is licensed under the MIT License - see LICENSE for the details.
I truly appreciate the help and resources provided by Dr. Michael Rosen's Lab at UT Southwestern Medical Center for this project.
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