scalpel (version 1.0.3)

scalpel-package: scalpel: A package for processing calcium imaging data.

Description

This package is called scalpel for "Segmentation, Clustering, and Lasso Penalties", which is a method for processing neuronal calcium imaging data that identifies the locations of neurons, and estimates their calcium concentrations over time. The main function is scalpel, which runs the entire SCALPEL pipeline. The pipeline involves several steps, each of which is described briefly in its corresponding function. See scalpelStep0, scalpelStep1, scalpelStep2, scalpelStep3 for more details. Results can be summarized using summary and the main plotting function is plotResults, which displays the estimated spatial and temporal components. Full details for the SCALPEL method are provided in Petersen, Ashley; Simon, Noah; Witten, Daniela. SCALPEL: Extracting neurons from calcium imaging data. Ann. Appl. Stat. 12 (2018), no. 4, 2430--2456. doi:10.1214/18-AOAS1159. https://projecteuclid.org/euclid.aoas/1542078051

Examples

# NOT RUN {
### many of the functions in this package are interconnected so the
### easiest way to learn to use the package is by working through the vignette,
### which is available at ajpete.com/software

 #general example illustrating some of the main functions
 #see the vignette for additional direction on using all of the functions
 #and the help pages for the specific functions for details on using each function

 #existing folder to save results (update this to an existing folder on your computer)
 outputFolder = "scalpelResults"
 #location on computer of raw data in R package to use
 rawDataFolder = gsub("Y_1.rds", "", system.file("extdata", "Y_1.rds", package = "scalpel"))
 #video height of raw data in R package
 videoHeight = 30
 #run SCALPEL pipeline
 scalpelOutput = scalpel(outputFolder = outputFolder, rawDataFolder = rawDataFolder,
                         videoHeight = videoHeight)
 #summarize each step
 summary(scalpelOutput, step = 0)
 summary(scalpelOutput, step = 1)
 summary(scalpelOutput, step = 2)
 summary(scalpelOutput, step = 3)

 #plot the spatial and temporal components
 plotResults(scalpelOutput = scalpelOutput)
 #plot a summary of the video with the found neurons outlined
 plotVideoVariance(scalpelOutput = scalpelOutput, neuronSet = "Afilter")
 #plot the frames with the most fluorescence for each found neuron
 plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 1)
 plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2)
 plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 3)

 #if you want to use results from a previous session,
 #use "getScalpel" to read in previous results
 scalpelOutputCopy = getScalpel(outputFolder = outputFolder)

# }
# NOT RUN {
# }