| Title: | Processes Calcium Imaging Data |
|---|---|
| Description: | Identifies the locations of neurons, and estimates their calcium concentrations over time using the SCALPEL method proposed 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>. |
| Authors: | Ashley Petersen |
| Maintainer: | Ashley Petersen <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.3 |
| Built: | 2025-02-24 04:02:52 UTC |
| Source: | https://github.com/cran/scalpel |
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
## 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) ## End(Not run)## 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) ## End(Not run)
This function allows the user to read in the manual classifications of neurons, based on the classifying done using
reviewNeurons or reviewNeuronsInteractive.
getNeuronStatus(scalpelOutput, neuronSet)getNeuronStatus(scalpelOutput, neuronSet)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
A vector of length equal to the number of columns in scalpelOutput$A if neuronSet="A" or scalpelOutput$Afilter if neuronSet="Afilter".
The elements give the manual classifications of the neurons. The possible classifications are: "yes" if
a neuron is to be kept, "no" if a neuron is to be discarded, "unsure" if a neuron needs to be reviewed further, and
NA if a neuron has not yet been classified.
reviewNeurons, reviewNeuronsInteractive
## 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 #assumes you have run the example for the "updateNeurons" function getNeuronStatus(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)## 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 #assumes you have run the example for the "updateNeurons" function getNeuronStatus(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)
This step allows the user to retrieve the object of class scalpel for results from a previous session.
getScalpel( outputFolder, version = NULL, cutoff = 0.18, omega = 0.2, lambdaMethod = "trainval", lambda = NULL, minClusterSize = 1, alpha = 0.9, removeBorder = FALSE, excludeReps = NULL )getScalpel( outputFolder, version = NULL, cutoff = 0.18, omega = 0.2, lambdaMethod = "trainval", lambda = NULL, minClusterSize = 1, alpha = 0.9, removeBorder = FALSE, excludeReps = NULL )
outputFolder |
The existing directory where the results that the user wishes to use are saved. |
version |
The 5-digit folder ID for the results that the user wishes to load. If NULL, automatically chooses the only version in outputFolder and if more than one version exists, returns an error. |
cutoff |
A value in [0,1] indicating the dendrogram cutpoint used. The default value is 0.18. |
omega |
A value in [0,1] indicating the dissimilarity metric weight used for clustering. The default value is 0.2. |
lambdaMethod |
How lambda was chosen: either |
lambda |
The value of lambda used to fit the sparse group lasso. If NULL, automatically chooses the only lambda in directory and if more than one lambda exists, returns an error. |
minClusterSize |
The minimum number of preliminary dictionary elements that a cluster must have contained to have been included in the sparse group lasso. The default value is 1. |
alpha |
The value of alpha used to fit the sparse group lasso. The default value is 0.9. |
removeBorder |
A logical scalar indicating whether the dictionary elements that contained pixels in the 10-pixel
border of the video were removed prior to fitting the sparse group lasso. The default value is |
excludeReps |
A vector giving the indices of which dictionary elements were excluded.
The default value is |
An object of class scalpel, which can be used to rerun SCALPEL Steps 1-3 with new parameters using scalpelStep1, scalpelStep2, and scalpelStep3
or can be used with any of the plotting functions: plotFrame, plotThresholdedFrame, plotVideoVariance, plotCandidateFrame,
plotCluster, plotResults, plotResultsAllLambda, plotSpatial,
plotTemporal, and plotBrightest.
## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpel(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpel(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18, alpha = 0.9, minClusterSize = 1) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpel(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpel(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18, alpha = 0.9, minClusterSize = 1) ## End(Not run)
This step allows the user to retrieve the object of class scalpelStep0 for results from a previous session.
getScalpelStep0(outputFolder)getScalpelStep0(outputFolder)
outputFolder |
The existing directory where the results that the user wishes to use are saved. |
An object of class scalpelStep0, which can be used to run SCALPEL Step 1 using scalpelStep1
or can be used with the plotting functions plotFrame, plotThresholdedFrame, and plotVideoVariance.
## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in out = getScalpelStep0(outputFolder = outputFolder) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in out = getScalpelStep0(outputFolder = outputFolder) ## End(Not run)
This step allows the user to retrieve the object of class scalpelStep1 for results from a previous session.
getScalpelStep1(outputFolder, version = NULL)getScalpelStep1(outputFolder, version = NULL)
outputFolder |
The existing directory where the results that the user wishes to use are saved. |
version |
The 5-digit folder ID for the results that the user wishes to load. If NULL, automatically chooses the only version in outputFolder and if more than one version exists, returns an error. |
An object of class scalpelStep1, which can be used to run SCALPEL Step 2 using scalpelStep2
or can be used with the plotting function plotCandidateFrame.
## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in out = getScalpelStep1(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in out = getScalpelStep1(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' ## End(Not run)
This step allows the user to retrieve the object of class scalpelStep2 for results from a previous session.
getScalpelStep2(outputFolder, version = NULL, cutoff = 0.18, omega = 0.2)getScalpelStep2(outputFolder, version = NULL, cutoff = 0.18, omega = 0.2)
outputFolder |
The existing directory where the results that the user wishes to use are saved. |
version |
The 5-digit folder ID for the results that the user wishes to load. If NULL, automatically chooses the only version in outputFolder and if more than one version exists, returns an error. |
cutoff |
A value in [0,1] indicating the dendrogram cutpoint used. The default value is 0.18. |
omega |
A value in [0,1] indicating the dissimilarity metric weight used for clustering. The default value is 0.2. |
An object of class scalpelStep2, which can be used to run SCALPEL Step 3 using scalpelStep3
or can be used with the plotting functions plotCluster and plotSpatial.
## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpelStep2(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpelStep2(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpelStep2(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpelStep2(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18) ## End(Not run)
This step allows the user to retrieve the object of class scalpelStep3 for results from a previous session.
getScalpelStep3( outputFolder, version = NULL, cutoff = 0.18, omega = 0.2, lambdaMethod = "trainval", minClusterSize = 1, alpha = 0.9, lambda = NULL, removeBorder = FALSE, excludeReps = NULL )getScalpelStep3( outputFolder, version = NULL, cutoff = 0.18, omega = 0.2, lambdaMethod = "trainval", minClusterSize = 1, alpha = 0.9, lambda = NULL, removeBorder = FALSE, excludeReps = NULL )
outputFolder |
The existing directory where the results that the user wishes to use are saved. |
version |
The 5-digit folder ID for the results that the user wishes to load. If NULL, automatically chooses the only version in outputFolder and if more than one version exists, returns an error. |
cutoff |
A value in [0,1] indicating the dendrogram cutpoint used. The default value is 0.18. |
omega |
A value in [0,1] indicating the dissimilarity metric weight used for clustering. The default value is 0.2. |
lambdaMethod |
How lambda was chosen: either |
minClusterSize |
The minimum number of preliminary dictionary elements that a cluster must have contained to have been included in the sparse group lasso. The default value is 1. |
alpha |
The value of alpha used to fit the sparse group lasso. The default value is 0.9. |
lambda |
The value of lambda used to fit the sparse group lasso. If NULL, automatically chooses the only lambda in directory and if more than one lambda exists, returns an error. |
removeBorder |
A logical scalar indicating whether the dictionary elements that contained pixels in the 10-pixel
border of the video were removed prior to fitting the sparse group lasso. The default value is |
excludeReps |
A vector giving the indices of which dictionary elements were excluded.
The default value is |
An object of class scalpelStep3, which can be used with the plotting functions
plotResults, plotResultsAllLambda, plotSpatial, plotTemporal, and plotBrightest.
## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpelStep3(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpelStep3(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18, alpha = 0.9, minClusterSize = 1) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #folder where results were saved outputFolder = "scalpelResults" #read previous results in #simplest example with default parameters: out = getScalpelStep3(outputFolder = outputFolder) #note: if Step 1 has been run more than once, will need to specify 'version' #example with optional parameters: #need to enter if non-default options were used out = getScalpelStep3(outputFolder = outputFolder, omega = 0.2, cutoff = 0.18, alpha = 0.9, minClusterSize = 1) ## End(Not run)
This step allows the user to read in Y, the matrix of raw or processed video data, to use with several plotting functions.
getY(scalpelOutput, videoType = "processed", part = NULL)getY(scalpelOutput, videoType = "processed", part = NULL)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
videoType |
Specify whether to read in the processed data from Step 0 (default; |
part |
The part of the video to read in, if it is split across multiple files. The default is NULL, which means that all parts will be read in and combined. |
An object of class scalpelY that can be provided as the Y argument in plotFrame,
plotVideoVariance, plotBrightest, plotThresholdedFrame, and plotCandidateFrame.
If would like to call these functions many times, this avoids reading the video into memory repeatedly.
## 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 #assumes you have run the example for the "scalpel" function #read in the raw data rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") #read in the processed data from Step 0 processedY = getY(scalpelOutput = scalpelOutput, videoType = "processed") ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #read in the raw data rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") #read in the processed data from Step 0 processedY = getY(scalpelOutput = scalpelOutput, videoType = "processed") ## End(Not run)
For a given neuron, we plot the frames with the highest estimated fluorescence, which results from fitting the sparse group lasso in Step 3 of SCALPEL.
plotBrightest( scalpelOutput, AfilterIndex, videoType = "processed", neuronsToOutline = "all", brightIndex = 1, shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, Y = NULL )plotBrightest( scalpelOutput, AfilterIndex, videoType = "processed", neuronsToOutline = "all", brightIndex = 1, shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, Y = NULL )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
AfilterIndex |
Scalar giving the neuron for which to plot the brightest frames. The index refers to which column
of |
videoType |
Specify whether to plot the processed data from Step 0 (default; |
neuronsToOutline |
Specify whether to plot outlines of all neurons (default; |
brightIndex |
Scalar giving which of the ordered brightest frames to plot. The default is 1, i.e., the brightest frame. |
shrinkLargest |
Logical value indicating whether the values above |
shrinkCutoff |
The value above which pixel values will be shrunk. By default, this will be chosen as
|
title |
Label for the title. The default is frame number. |
Y |
An object of class |
None
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2) #example with optional parameters: #only outline neuron corresponding to frame, plot 5th brightest with raw data plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2, videoType = "raw", neuronsToOutline = "main", brightIndex = 5) #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2, Y = rawY, neuronsToOutline = "main", brightIndex = 5) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2) #example with optional parameters: #only outline neuron corresponding to frame, plot 5th brightest with raw data plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2, videoType = "raw", neuronsToOutline = "main", brightIndex = 5) #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotBrightest(scalpelOutput = scalpelOutput, AfilterIndex = 2, Y = rawY, neuronsToOutline = "main", brightIndex = 5) ## End(Not run)
We plot the specified preliminary dictionary element, along with the frame of Y from which the component was derived in Step 1 of SCALPEL.
plotCandidateFrame( scalpelOutput, AzeroIndex = NULL, AIndex = NULL, AfilterIndex = NULL, member = NULL, videoType = "processed", shrinkLargest = FALSE, shrinkCutoff = NULL, Y = NULL )plotCandidateFrame( scalpelOutput, AzeroIndex = NULL, AIndex = NULL, AfilterIndex = NULL, member = NULL, videoType = "processed", shrinkLargest = FALSE, shrinkCutoff = NULL, Y = NULL )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
AzeroIndex |
The preliminary dictionary element of interest. The index refers to the column of |
AIndex |
The dictionary element (i.e., cluster) of interest. The index refers to the column of |
AfilterIndex |
The sparse group lasso component of interest. The index refers to the column of |
member |
Which member of the cluster corresponding to |
videoType |
Specify whether to plot the processed data from Step 0 (default; |
shrinkLargest |
Logical value indicating whether the values above |
shrinkCutoff |
The value above which pixel values will be shrunk. By default, this will be chosen as
|
Y |
An object of class |
None
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10) #example with optional parameters: #plot raw data instead of processed plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10, videoType = "raw") #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10, Y = rawY) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10) #example with optional parameters: #plot raw data instead of processed plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10, videoType = "raw") #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotCandidateFrame(scalpelOutput = scalpelOutput, AzeroIndex = 10, Y = rawY) ## End(Not run)
We plot the preliminary dictionary elements that correspond to a given dictionary element, derived during Step 2 of SCALPEL, or a given component included in the sparse group lasso of Step 3.
plotCluster( scalpelOutput, AIndex = NULL, AfilterIndex = NULL, pctTransp = 0.01 )plotCluster( scalpelOutput, AIndex = NULL, AfilterIndex = NULL, pctTransp = 0.01 )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
AIndex |
The dictionary element (i.e., cluster) of interest. The index refers to the column of |
AfilterIndex |
The refined dictionary element of interest. The index refers to the column of |
pctTransp |
The percent transparency (in [0,1]) for the colors used to plot the preliminary dictionary elements. The default value is 0.01. |
The left plot shows the dictionary element of interest in orange, with the other dictionary elements shown in blue. The middle plot shows all of the preliminary dictionary elements corresponding to the dictionary element plotted transparently. The right plot shows the dictionary element in orange, along with the union of all of the preliminary dictionary elements in gray. Note that the plots in the middle and on the right are zoomed-in, compared to the plot on the left that shows the entire field of view for the video.
None
## 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 #assumes you have run the example for the "scalpel" function #plots the cluster for the 2nd dictionary element (i.e., scalpelOutput$A[,2]) plotCluster(scalpelOutput = scalpelOutput, AIndex = 2) #plots the cluster for the 2nd component included in SGL (i.e., scalpelOutput$Afilter[,2]) plotCluster(scalpelOutput = scalpelOutput, AfilterIndex = 2) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #plots the cluster for the 2nd dictionary element (i.e., scalpelOutput$A[,2]) plotCluster(scalpelOutput = scalpelOutput, AIndex = 2) #plots the cluster for the 2nd component included in SGL (i.e., scalpelOutput$Afilter[,2]) plotCluster(scalpelOutput = scalpelOutput, AfilterIndex = 2) ## End(Not run)
We plot a specified frame of the raw video that we began with in Step 0 of SCALPEL, or the processed video that results from Step 0 of SCALPEL.
plotFrame( scalpelOutput, frame, videoType = "processed", shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, col = grDevices::grey(seq(0, 1, length = 256)), addToPlot = FALSE, Y = NULL )plotFrame( scalpelOutput, frame, videoType = "processed", shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, col = grDevices::grey(seq(0, 1, length = 256)), addToPlot = FALSE, Y = NULL )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
frame |
The frame to plot. |
videoType |
Specify whether to plot the processed data from Step 0 (default; |
shrinkLargest |
Logical value indicating whether the values above |
shrinkCutoff |
The value above which pixel values will be shrunk. By default, this will be chosen as
|
title |
Label for the title. By default, it is the frame number. |
col |
Vector of colors to use, which by default is grayscale. |
addToPlot |
Logical value indicating whether to add to the current plot. |
Y |
An object of class |
None
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotFrame(scalpelOutput = scalpelOutput, frame = 100) #example with optional parameters: #plot raw data instead of processed plotFrame(scalpelOutput = scalpelOutput, frame = 100, videoType = "raw") #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotFrame(scalpelOutput = scalpelOutput, frame = 100, Y = rawY) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotFrame(scalpelOutput = scalpelOutput, frame = 100) #example with optional parameters: #plot raw data instead of processed plotFrame(scalpelOutput = scalpelOutput, frame = 100, videoType = "raw") #same plot but if you have video data read in already #using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotFrame(scalpelOutput = scalpelOutput, frame = 100, Y = rawY) ## End(Not run)
We plot the temporal components, displaying the estimated fluorescence over time for each spatial component, along with a map of the spatial components.
plotResults( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, titleA = "", titleZ = "", ylabZ = "", fileName = NULL, pctTransp = 0.7, number = TRUE, border = FALSE )plotResults( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, titleA = "", titleZ = "", ylabZ = "", fileName = NULL, pctTransp = 0.7, number = TRUE, border = FALSE )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronsToDisplay |
Vector giving which neurons' spatial and temporal components to plot. The indices refer to which columns
of |
colVec |
Vector of colors to use, which are chosen automatically if the default value of NULL is used. |
titleA |
Label for the title of the spatial components plot. |
titleZ |
Label for the title of the temporal components plot. |
ylabZ |
Label for the y-axis of the temporal components plot. |
fileName |
If provided, the plot will be saved to the specified location. |
pctTransp |
The percent transparency (in [0,1]) for the colors used to plot the neurons. The default value is 0.7. |
number |
Logical value indicating whether the neurons should be numbered. |
border |
Logical value indicating whether only the borders of the neurons should be plotted. |
If lambdaIndex is NULL, each temporal component is scaled by its largest value. If
lambdaIndex is specified, each temporal component is scaled by its largest value across all of the lambda values.
Temporal components that were zeroed out in the sparse group lasso are omitted from the plot, and their corresponding
spatial components are shown in gray.
None
scalpelStep3, scalpel, plotSpatial, plotTemporal
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotResults(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons, do not number neurons, draw the outlines of the neurons plotResults(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), number = FALSE, border = TRUE) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotResults(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons, do not number neurons, draw the outlines of the neurons plotResults(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), number = FALSE, border = TRUE) ## End(Not run)
We plot the temporal components, displaying the estimated fluorescence over time for each spatial component, along with a map of the spatial components for a whole sequence of lambda values.
plotResultsAllLambda( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, titleA = "", ylabZ = "", fileName = NULL, pctTransp = 0.7, number = TRUE, border = FALSE )plotResultsAllLambda( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, titleA = "", ylabZ = "", fileName = NULL, pctTransp = 0.7, number = TRUE, border = FALSE )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronsToDisplay |
Vector giving which neurons' spatial and temporal components to plot. The indices refer to which columns
of |
colVec |
Vector of colors to use, which are chosen automatically if the default value of NULL is used. |
titleA |
Label for the title of the spatial components plot. |
ylabZ |
Label for the y-axis of the temporal components plot. |
fileName |
If provided, the plot will be saved to the specified location. |
pctTransp |
The percent transparency (in [0,1]) for the colors used to plot the neurons. The default value is 0.7. |
number |
Logical value indicating whether the neurons should be numbered. |
border |
Logical value indicating whether only the borders of the neurons should be plotted. |
Temporal components that were zeroed out in the sparse group lasso and their corresponding spatial components are shown in gray for both plots.
None
scalpelStep3, scalpel, plotSpatial, plotTemporal
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotResultsAllLambda(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons, do not number neurons, draw the outlines of the neurons plotResultsAllLambda(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), number = FALSE, border = TRUE) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotResultsAllLambda(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons, do not number neurons, draw the outlines of the neurons plotResultsAllLambda(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), number = FALSE, border = TRUE) ## End(Not run)
We plot the dictionary elements obtained from Step 2 of SCALPEL, or the filtered dictionary elements from Step 3 of SCALPEL.
plotSpatial( scalpelOutput = NULL, neuronSet = "", neuronsToDisplay = NULL, colVec = NULL, title = "", fileName = NULL, pctTransp = 0.7, number = TRUE, addToPlot = FALSE, border = FALSE, zoom = FALSE, A = NULL, videoHeight = NULL )plotSpatial( scalpelOutput = NULL, neuronSet = "", neuronsToDisplay = NULL, colVec = NULL, title = "", fileName = NULL, pctTransp = 0.7, number = TRUE, addToPlot = FALSE, border = FALSE, zoom = FALSE, A = NULL, videoHeight = NULL )
scalpelOutput |
An object returned by one of these SCALPEL functions:
|
neuronSet |
Which set of neurons should be plotted:
use |
neuronsToDisplay |
Vector giving which neurons' spatial components to plot. The indices refer to which columns to plot
of |
colVec |
Vector of colors to use, which are chosen automatically if the default value of NULL is used. |
title |
Label for the title. |
fileName |
If provided, the plot will be saved to the specified location. |
pctTransp |
The percent transparency (in [0,1]) for the colors used to plot the neurons. The default value is 0.7. |
number |
Logical value indicating whether the neurons should be numbered. |
addToPlot |
Logical value indicating whether these neurons should be plotted to an existing plot. |
border |
Logical value indicating whether only the borders of the neurons should be plotted. |
zoom |
Logical value indicating whether the plot should be zoomed in to exclude any area not containing neurons. |
A |
Optional advanced user argument: A matrix containing the spatial components to plot, where the ith column of |
videoHeight |
The height of the video (in pixels). This only needs to be specified if the user is plotting |
When neuronSet="Afilter", spatial components corresponding to temporal components that were zeroed out in the sparse group lasso are plotted in gray, unless colVec is specified by the user.
None
scalpelStep2, scalpelStep3, scalpel, plotResults
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotSpatial(scalpelOutput = scalpelOutput, neuronSet = "Afilter") #example with optional parameters: #plot only two of the neurons, add a title, do not number neurons, #and draw the outlines of the neurons plotSpatial(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), neuronSet = "Afilter", title = "First two neurons", number = FALSE, border = TRUE) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotSpatial(scalpelOutput = scalpelOutput, neuronSet = "Afilter") #example with optional parameters: #plot only two of the neurons, add a title, do not number neurons, #and draw the outlines of the neurons plotSpatial(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), neuronSet = "Afilter", title = "First two neurons", number = FALSE, border = TRUE) ## End(Not run)
We plot the temporal components, displaying the estimated fluorescence over time for each spatial component, which result from running Step 3 of SCALPEL.
plotTemporal( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, ylab = "", title = "", fileName = NULL, lambdaIndex = NULL )plotTemporal( scalpelOutput, neuronsToDisplay = NULL, colVec = NULL, ylab = "", title = "", fileName = NULL, lambdaIndex = NULL )
scalpelOutput |
An object returned by one of these SCALPEL functions:
|
neuronsToDisplay |
Vector giving which neurons' temporal components to plot. The indices refer to which rows
of |
colVec |
Vector of colors to use, which are chosen automatically if the default value of NULL is used. |
ylab |
Label for the y-axis. |
title |
Label for the title. |
fileName |
If provided, the plot will be saved to the specified location. |
lambdaIndex |
Optional advanced user argument: Index of lambda value for which results will be plotted. Default is
to use lambda value of |
If lambdaIndex is NULL, each temporal component is scaled by its largest value. If
lambdaIndex is specified, each temporal component is scaled by its largest value across all of the lambda values.
Temporal components that were zeroed out in the sparse group lasso are omitted from the plot.
None
scalpelStep3, scalpel, plotResults
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotTemporal(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons and add a title plotTemporal(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), title = "First two neurons") ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotTemporal(scalpelOutput = scalpelOutput) #example with optional parameters: #plot only two of the neurons and add a title plotTemporal(scalpelOutput = scalpelOutput, neuronsToDisplay = c(1,2), title = "First two neurons") ## End(Not run)
We plot a specified frame of the processed video, which results from Step 0 of SCALPEL, with shading to indicate values above a specified threshold.
plotThresholdedFrame( scalpelOutput, frame, threshold, shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, col = "yellow", Y = NULL )plotThresholdedFrame( scalpelOutput, frame, threshold, shrinkLargest = FALSE, shrinkCutoff = NULL, title = NULL, col = "yellow", Y = NULL )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
frame |
The frame to plot. |
threshold |
Value above which pixels will be shaded. |
shrinkLargest |
Logical value indicating whether the values above |
shrinkCutoff |
The value above which pixel values will be shrunk. By default, this will be chosen as
|
title |
Label for the title. By default, it gives the threshold value. |
col |
Color of shading to use, which is yellow by default. |
Y |
An object of class |
None
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, threshold = scalpelOutput$thresholdVec[1]) #example with optional parameters: #change shading to purple and add a title plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, col = "purple", threshold = scalpelOutput$thresholdVec[2]) #if you have video data read in already using 'getY' function, you can provide it processedY = getY(scalpelOutput = scalpelOutput, videoType = "processed") plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, threshold = scalpelOutput$thresholdVec[1], Y = processedY) ## End(Not run)## Not run: ### many of the functions in this package are interconnected so the ### easiest way to learn to use the package is by working through the vignette, ### which is available at ajpete.com/software #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, threshold = scalpelOutput$thresholdVec[1]) #example with optional parameters: #change shading to purple and add a title plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, col = "purple", threshold = scalpelOutput$thresholdVec[2]) #if you have video data read in already using 'getY' function, you can provide it processedY = getY(scalpelOutput = scalpelOutput, videoType = "processed") plotThresholdedFrame(scalpelOutput = scalpelOutput, frame = 100, threshold = scalpelOutput$thresholdVec[1], Y = processedY) ## End(Not run)
We plot a heat map of the variance of each pixel across the frames.
plotVideoVariance( scalpelOutput, neuronSet = "", videoType = "processed", neuronsToOutline = "all", shrinkLargest = FALSE, shrinkQuantile = 0.95, title = "", Y = NULL )plotVideoVariance( scalpelOutput, neuronSet = "", videoType = "processed", neuronsToOutline = "all", shrinkLargest = FALSE, shrinkQuantile = 0.95, title = "", Y = NULL )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
This argument is ignored unless the class of |
videoType |
Specify whether to plot the processed data from Step 0 (default; |
neuronsToOutline |
Specify whether to plot outlines of all neurons (default; |
shrinkLargest |
Logical value indicating whether the values above |
shrinkQuantile |
The quantile value above which pixel values will be shrunk. By default, this is the 95th quantile. |
title |
Label for the title. |
Y |
An object of class |
None
## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotVideoVariance(scalpelOutput = scalpelOutput, neuronSet = "Afilter") #example with optional parameters: #previous showed summary of processed data, #can instead show raw data, not outline the neurons found, and add a title plotVideoVariance(scalpelOutput = scalpelOutput, videoType = "raw", neuronsToOutline = "none", title = "Raw Data") #if you have video data read in already using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotVideoVariance(scalpelOutput = scalpelOutput, neuronSet = "Afilter", Y = rawY) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #simplest example with default parameters: plotVideoVariance(scalpelOutput = scalpelOutput, neuronSet = "Afilter") #example with optional parameters: #previous showed summary of processed data, #can instead show raw data, not outline the neurons found, and add a title plotVideoVariance(scalpelOutput = scalpelOutput, videoType = "raw", neuronsToOutline = "none", title = "Raw Data") #if you have video data read in already using 'getY' function, you can provide it rawY = getY(scalpelOutput = scalpelOutput, videoType = "raw") plotVideoVariance(scalpelOutput = scalpelOutput, neuronSet = "Afilter", Y = rawY) ## End(Not run)
We save plots that will be used to review the set of identified neurons that result from either Step 2 or 3 of SCALPEL in order to manually classify them according
to whether they appear to be real neurons or not. To do this, the plot of the frame from which the dictionary element was derived is saved.
The user can then sort these saved plot into folders indicating whether the neuron is real or not, or indicate that additional frames are needed to make the classification, in which
case the reviewNeuronsMoreFrames function can subsequently be used. After finishing this sorting process, updateNeurons should be called.
A similar manual classification can be done interactively using reviewNeuronsInteractive.
reviewNeurons( scalpelOutput, neuronSet, keepClusterSize = NULL, discardZeroedOut = FALSE )reviewNeurons( scalpelOutput, neuronSet, keepClusterSize = NULL, discardZeroedOut = FALSE )
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
keepClusterSize |
Neurons corresponding to clusters with at least |
discardZeroedOut |
Logical value indicating whether neurons zeroed out in the sparse group lasso problem should automatically be discarded. This argument
is ignored when |
Plots are saved for each of the neurons under consideration in a certain folder. Also within that folder,
there will be folders called 'keep', 'discard', and 'unsure'. After running this function, the plots for each of the neurons
should be moved into the appropriate folder. After completing this sorting, call updateNeurons in order to
update the classification of the neurons. Any plots that are missing or that remain in the original folder will be classified as not having been sorted yet.
None
After sorting the plots saved by this function, the user should call updateNeurons.
For other functions useful in the classification process, see reviewNeuronsMoreFrames and
reviewOverlappingNeurons. Once classification is finished,
the argument neuronsToOutline="kept" can be used with plotBrightest and plotVideoVariance,
and the argument neuronsToDisplay="kept" can be used with plotResults, plotResultsAllLambda,
plotTemporal, and plotSpatial. Finally, the argument excludeReps="discarded" allows
the discarded dictionary elements to be excluded from the sparse group lasso model when running scalpelStep3.
## 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 #assumes you have run the example for the "scalpel" function #we review the set of spatial components from Step 3, #which are contained in scalpelOutput$Afilter reviewNeurons(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)## Not run: ### many of the functions in this package are interconnected so the ### easiest way to learn to use the package is by working through the vignette, ### which is available at ajpete.com/software #assumes you have run the example for the "scalpel" function #we review the set of spatial components from Step 3, #which are contained in scalpelOutput$Afilter reviewNeurons(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)
We interactively review the set of identified neurons that result from either Step 2 or 3 of SCALPEL in order to manually classify them according
to whether they appear to be real neurons or not. To do this, the frame from which the dictionary element was derived is plotted.
The user can manually classify the neuron as real or not, or indicate that additional frames are needed to make the classification, in which
case the reviewNeuronsMoreFrames function can subsequently be used. A similar manual classification can be done non-interactively using reviewNeurons.
reviewNeuronsInteractive(scalpelOutput, neuronSet)reviewNeuronsInteractive(scalpelOutput, neuronSet)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
None
For other functions useful in the classification process, see reviewNeuronsMoreFrames,
reviewOverlappingNeurons, and updateNeuronsInteractive. Once classification is finished,
the argument neuronsToOutline="kept" can be used with plotBrightest and plotVideoVariance,
and the argument neuronsToDisplay="kept" can be used with plotResults, plotResultsAllLambda,
plotTemporal, and plotSpatial. Finally, the argument excludeReps="discarded" allows
the discarded dictionary elements to be excluded from the sparse group lasso model when running scalpelStep3.
## 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 #assumes you have run the example for the "scalpel" function #we review the set of spatial components from Step 2, #which are contained in scalpelOutput$A reviewNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #enter "Y" for the first neuron and then "Q" #entering "Q" allows us to finish manually classifying later using the same command #this time there are fewer left to review reviewNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #enter "N" for the first and "?" for the second this time #note that once a neuron is classified as "N", it disappears from the plot ## End(Not run)## Not run: ### many of the functions in this package are interconnected so the ### easiest way to learn to use the package is by working through the vignette, ### which is available at ajpete.com/software #assumes you have run the example for the "scalpel" function #we review the set of spatial components from Step 2, #which are contained in scalpelOutput$A reviewNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #enter "Y" for the first neuron and then "Q" #entering "Q" allows us to finish manually classifying later using the same command #this time there are fewer left to review reviewNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #enter "N" for the first and "?" for the second this time #note that once a neuron is classified as "N", it disappears from the plot ## End(Not run)
We use this function after running reviewNeurons or reviewNeuronsInteractive to plot additional frames for neurons whose classification was
unclear from the single frame plotted. The additional frames are saved, and the classification
for the neurons can then be updated using updateNeurons or updateNeuronsInteractive.
reviewNeuronsMoreFrames(scalpelOutput, neuronSet, numFrames = 10)reviewNeuronsMoreFrames(scalpelOutput, neuronSet, numFrames = 10)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
numFrames |
The maximum number of frames that should be saved for each neuron being considered. Each neuron has a number of frames
equal to the number of members in that neuron's cluster that can be plotted. All frames will be saved when the total number of available frames for the neuron
is less than |
None
reviewNeurons, updateNeurons, reviewNeuronsInteractive, updateNeuronsInteractive
## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function #we save frames for the neurons previously classified #as "?" using the "reviewNeuronsInteractive" function reviewNeuronsMoreFrames(scalpelOutput = scalpelOutput, neuronSet = "A") ## End(Not run)## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function #we save frames for the neurons previously classified #as "?" using the "reviewNeuronsInteractive" function reviewNeuronsMoreFrames(scalpelOutput = scalpelOutput, neuronSet = "A") ## End(Not run)
We use this function after running reviewNeurons or reviewNeuronsInteractive to plot additional frames for neurons that overlap with others.
These frames are saved, and the classification for the neurons can then be updated using updateNeurons or updateNeuronsInteractive.
reviewOverlappingNeurons(scalpelOutput, neuronSet, numFrames = 10)reviewOverlappingNeurons(scalpelOutput, neuronSet, numFrames = 10)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
numFrames |
The maximum number of frames that should be saved for each neuron being considered. Each neuron has a number of frames
equal to the number of members in that neuron's cluster that can be plotted. All frames will be saved when the total number of available frames for the neuron
is less than |
None
reviewNeurons, updateNeurons, reviewNeuronsInteractive, updateNeuronsInteractive
## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function reviewOverlappingNeurons(scalpelOutput = scalpelOutput, neuronSet = "A") ## End(Not run)## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function reviewOverlappingNeurons(scalpelOutput = scalpelOutput, neuronSet = "A") ## End(Not run)
Segmentation, Clustering, and Lasso Penalties (SCALPEL) is a method for neuronal calcium imaging
data that identifies the locations of neurons, and estimates their calcium concentrations over time.
The pipeline involves several steps, each of which is described briefly in its corresponding
function. See scalpelStep0, scalpelStep1, scalpelStep2,
scalpelStep3 for more details.
Full details for the SCALPEL method are provided in Petersen, A., Simon, N., and Witten, D. (Forthcoming).
SCALPEL: Extracting Neurons from Calcium Imaging Data.
scalpel( outputFolder, rawDataFolder, videoHeight, minClusterSize = 1, lambdaMethod = "trainval", lambda = NULL, cutoff = 0.18, omega = 0.2, fileType = "R", processSeparately = TRUE, minSize = 25, maxSize = 500, maxWidth = 30, maxHeight = 30, removeBorder = FALSE, alpha = 0.9, thresholdVec = NULL, maxSizeToCluster = 3000 )scalpel( outputFolder, rawDataFolder, videoHeight, minClusterSize = 1, lambdaMethod = "trainval", lambda = NULL, cutoff = 0.18, omega = 0.2, fileType = "R", processSeparately = TRUE, minSize = 25, maxSize = 500, maxWidth = 30, maxHeight = 30, removeBorder = FALSE, alpha = 0.9, thresholdVec = NULL, maxSizeToCluster = 3000 )
outputFolder |
Step 0 parameter: The existing directory where the results should be saved. |
rawDataFolder |
Step 0 parameter: The directory where the raw data version of Y is saved. The data should be a
PxT matrix, where P is the total number of pixels per image frame and T
the number of frames of the video, for which the (i,j)th element contains the
fluorescence of the ith pixel in the jth frame. To create Y, you should
vectorize each 2-dimensional image frame by concatenating the columns of the image frame. If the data is
saved in a single file, it should be named "Y_1.mat", "Y_1.rds", "Y_1.txt", or "Y_1.txt.gz" (depending on |
videoHeight |
Step 0 parameter: The height of the video (in pixels). |
minClusterSize |
Step 3 parameter: The minimum number of preliminary dictionary elements that a cluster must contain in order to be included in the sparse group lasso. |
lambdaMethod |
Step 3 parameter: A description of how lambda should be chosen: either |
lambda |
Step 3 parameter: The value of lambda to use when fitting the sparse group lasso. By default, the value is automatically
chosen using the approach specified by |
cutoff |
Step 2 parameter: A value in [0,1] indicating where to cut the dendrogram that results from hierarchical clustering of the preliminary dictionary elements. The default value is 0.18. |
omega |
Step 2 parameter: A value in [0,1] indicating how to weight spatial vs. temporal information in the dissimilarity metric
used for clustering. If |
fileType |
Step 0 parameter: Indicates whether raw data is an .rds (default value; |
processSeparately |
Step 0 parameter: Logical scalar giving whether the multiple raw data files should be
processed individually, versus all at once. Processing the files separately may be preferable for larger videos.
Default value is |
minSize, maxSize
|
Step 1 parameter: The minimum and maximum size, respectively, for a preliminary dictionary element with default values of 25 and 500, respectively. |
maxWidth, maxHeight
|
Step 1 parameter: The maximum width and height, respectively, for a preliminary dictionary element with default values of 30. |
removeBorder |
Step 3 parameter: A logical scalar indicating whether the dictionary elements containing pixels in the 10-pixel
border of the video should be removed prior to fitting the sparse group lasso. The default value is |
alpha |
Step 3 parameter: The value of alpha to use when fitting the sparse group lasso. The default value is 0.9. |
thresholdVec |
Optional advanced user argument: Step 1 parameter: A vector with the desired thresholds to use for image segmentation. If not specified, the default is to use the negative of the minimum of the processed Y data, the negative of the 0.1% quantile of the processed Y data, and the mean of these. If there were multiple raw data files that were processed separately, these values are calculated on only the first part of data, and then these thresholds are used for the remaining parts. |
maxSizeToCluster |
Optional advanced user argument: Step 2 parameter: The maximum number of preliminary dictionary elements to cluster at once. We attempt to cluster each
overlapping set of preliminary dictionary elements, but if one of these sets is very large (e.g., >10,000), memory issues may
result. Thus we perform a two-stage clustering in which we first cluster together random sets of size
approximately equaling |
Several files containing data from the pipeline, as well as summaries of each step, are saved in various subdirectories of "outputFolder".
An object of class scalpel, which can be summarized using summary, used to rerun SCALPEL Steps 1-3 with new parameters using scalpelStep1, scalpelStep2, and scalpelStep3,
or can be used with any of the plotting functions: plotFrame, plotThresholdedFrame, plotVideoVariance, plotCandidateFrame,
plotCluster, plotResults, plotResultsAllLambda, plotSpatial,
plotTemporal, and plotBrightest.
The individual elements are described in detail in the documentation for the corresponding step: scalpelStep0, scalpelStep1, scalpelStep2, and scalpelStep3.
The individual steps in the pipeline can be run using the scalpelStep0,
scalpelStep1, scalpelStep2, and scalpelStep3 functions.
Results can be summarized using summary, loaded at a later time using getScalpel, and plotted using plotResults,
plotSpatial, plotTemporal, plotCluster, plotVideoVariance,
plotFrame, plotThresholdedFrame, plotCandidateFrame, and plotBrightest.
## 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 #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) ## End(Not run)## 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 #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) ## End(Not run)
This step involves data pre-processing. We read in the raw data version of Y and perform standard pre-processing techniques in order to smooth the data both temporally and spatially, remove the bleaching effect, and calculate a standardized fluorescence.
scalpelStep0( outputFolder, rawDataFolder, videoHeight, fileType = "R", processSeparately = TRUE )scalpelStep0( outputFolder, rawDataFolder, videoHeight, fileType = "R", processSeparately = TRUE )
outputFolder |
The existing directory where the results should be saved. |
rawDataFolder |
The directory where the raw data version of Y is saved. The data should be a
PxT matrix, where P is the total number of pixels per image frame and T
the number of frames of the video, for which the (i,j)th element contains the
fluorescence of the ith pixel in the jth frame. To create Y, you should
vectorize each 2-dimensional image frame by concatenating the columns of the image frame. If the data is
saved in a single file, it should be named "Y_1.mat", "Y_1.rds", "Y_1.txt", or "Y_1.txt.gz" (depending on |
videoHeight |
The height of the video (in pixels). |
fileType |
Indicates whether raw data is an .rds (default value; |
processSeparately |
Logical scalar giving whether the multiple raw data files should be
processed individually, versus all at once. Processing the files separately may be preferable for larger videos.
The default value is |
Several files containing data from this step and a summary of the step are saved in "outputFolder".
An object of class scalpelStep0, which can be summarized using summary, used to run SCALPEL Step 1 using scalpelStep1,
or can be used with the plotting functions plotFrame, plotThresholdedFrame, and plotVideoVariance.
minRaw, maxRaw, minDeltaf, maxDeltaf: Minimum and maximum values for the raw and processed videos.
partsRaw, partsDeltaf: Vectors indicating the indices of the raw and processed data files, respectively.
nFramesRaw, nFramesDeltaf: The number of frames in each part of the raw and processed data.
lowThreshold, highThreshold: The default lowest and highest threshold values for image segmentation that may be used in Step 1.
Other elements: As specified by the user.
The entire SCALPEL pipeline can be implemented using the scalpel function. The
other steps in the pipeline can be run using the scalpelStep1, scalpelStep2, scalpelStep3 functions.
Results from this step can be summarized using summary, loaded at a later time using getScalpelStep0, and plotted using
plotFrame, plotThresholdedFrame, and plotVideoVariance.
## 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 #existing folder to save results (update this to an existing folder on your computer) outputFolder = "scalpelResultsStepByStep" #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 Step 0 of SCALPEL Step0Out = scalpelStep0(outputFolder = outputFolder, rawDataFolder = rawDataFolder, videoHeight = videoHeight) summary(Step0Out) ## End(Not run)## 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 #existing folder to save results (update this to an existing folder on your computer) outputFolder = "scalpelResultsStepByStep" #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 Step 0 of SCALPEL Step0Out = scalpelStep0(outputFolder = outputFolder, rawDataFolder = rawDataFolder, videoHeight = videoHeight) summary(Step0Out) ## End(Not run)
This step involves constructing a spatial component dictionary. We apply a simple image segmentation procedure to each frame of the video in order to derive a dictionary of preliminary dictionary elements. Ideally, this dictionary is a superset of the true spatial components.
scalpelStep1( step0Output, minSize = 25, maxSize = 500, maxWidth = 30, maxHeight = 30, thresholdVec = NULL )scalpelStep1( step0Output, minSize = 25, maxSize = 500, maxWidth = 30, maxHeight = 30, thresholdVec = NULL )
step0Output |
An object of class |
minSize, maxSize
|
The minimum and maximum size, respectively, for a preliminary dictionary element with default values of 25 and 500, respectively. |
maxWidth, maxHeight
|
The maximum width and height, respectively, for a preliminary dictionary element with default values of 30. |
thresholdVec |
Optional advanced user argument: A vector with the desired thresholds to use for image segmentation. If not specified, the default is to
use the negative of the minimum of the processed Y data (i.e., |
Several files containing data from this step and a summary of the step are saved in
"outputFolder/Step1_version" where version is a 5-digit unique identifier that is automatically generated.
An object of class scalpelStep1, which can be summarized using summary, used to run SCALPEL Step 2 using scalpelStep2,
or can be used with the plotting function plotCandidateFrame.
Azero: A matrix containing the preliminary dictionary elements, where the ith column of Azero
is a vector of 1's and 0's, indicating whether each pixel is contained in the ith preliminary dictionary element.
AzeroFrames: A vector whose ith element gives the video frame from which the preliminary dictionary element
in the ith column of Azero was derived.
AzeroThreshold: A vector whose ith element gives the threshold used to obtain the preliminary dictionary element
in the ith column of Azero.
pixelsUse: A vector with the pixels (i.e., indices of the rows of Azero) that are contained in at
least one preliminary dictionary element.
version: A 5-digit unique identifier for the output folder name that is automatically generated in this step.
Other elements: As specified by the user or returned from a previous step.
The entire SCALPEL pipeline can be implemented using the scalpel function. The
other steps in the pipeline can be run using the scalpelStep0, scalpelStep2, scalpelStep3 functions.
Results from this step can be summarized using summary, loaded at a later time using getScalpelStep1, and plotted
using plotCandidateFrame.
## 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 #assumes you have run the example for the "scalpelStep0" function #run Step 1 of SCALPEL Step1Out = scalpelStep1(step0Output = Step0Out) summary(Step1Out) ## End(Not run)## 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 #assumes you have run the example for the "scalpelStep0" function #run Step 1 of SCALPEL Step1Out = scalpelStep1(step0Output = Step0Out) summary(Step1Out) ## End(Not run)
This step involves refinement of the spatial component dictionary from Step 1. We eliminate redundancy in the spatial component dictionary by clustering together preliminary dictionary elements that represent the same neuron, based on spatial and temporal information.
scalpelStep2(step1Output, cutoff = 0.18, omega = 0.2, maxSizeToCluster = 3000)scalpelStep2(step1Output, cutoff = 0.18, omega = 0.2, maxSizeToCluster = 3000)
step1Output |
An object of class |
cutoff |
A value in [0,1] indicating where to cut the dendrogram that results from hierarchical clustering of the preliminary dictionary elements. The default value is 0.18. |
omega |
A value in [0,1] indicating how to weight spatial vs. temporal information in the dissimilarity metric
used for clustering. If |
maxSizeToCluster |
Optional advanced user argument: The maximum number of preliminary dictionary elements to cluster at once. We attempt to cluster each
overlapping set of preliminary dictionary elements, but if one of these sets is very large (e.g., >10,000), memory issues may
result. Thus we perform a two-stage clustering in which we first cluster together random sets of size
approximately equaling |
Several files containing data from this step and a summary of the step are saved in the folder
"outputFolder/Step1_version/Step2_omega_omega_cutoff_cutoff" where version is a 5-digit unique
identifier that is automatically generated in Step 1 and omega and cutoff are the user-supplied parameters.
An object of class scalpelStep2, which can be summarized using summary, used to run SCALPEL Step 3 using scalpelStep3,
or can be used with the plotting functions plotCluster and plotSpatial.
A: A matrix containing the dictionary elements, where the ith column of A
is a vector of 1's and 0's, indicating whether each pixel is contained in the ith dictionary element.
repComps: A vector where the ith element indicates which preliminary dictionary element is the ith representive
component. That is, A[,i]=step1Output$Azero[,repComps[i]].
clusterID: A vector whose ith element indicates which of the dictionary elements in A is the
representative for the ith preliminary dictionary element.
overlapSetID: A vector indicating which preliminary dictionary elements overlap, with the ith element
giving the group index for the ith preliminary dictionary element.
treeList: A list of length max(overlapSetID) with the ith element containing an object of class protoclust corresponding
to prototype clustering for the preliminary dictionary elements with overlapSetID=i. If two-stage clustering was done for a
particular set, then the element will be NULL.
Other elements: As specified by the user or returned from a previous step.
The entire SCALPEL pipeline can be implemented using the scalpel function. The
other steps in the pipeline can be run using the scalpelStep0, scalpelStep1, scalpelStep3 functions.
Results from this step can be summarized using summary, loaded at a later time using getScalpelStep2, and plotted using
plotCluster and plotSpatial.
## 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 #assumes you have run the example for the "scalpelStep1" function #run Step 2 of SCALPEL Step2Out = scalpelStep2(step1Output = Step1Out) summary(Step2Out) ## End(Not run)## 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 #assumes you have run the example for the "scalpelStep1" function #run Step 2 of SCALPEL Step2Out = scalpelStep2(step1Output = Step1Out) summary(Step2Out) ## End(Not run)
This step involves spatial component selection and temporal component estimation. We estimate the temporal components corresponding to the dictionary elements from Step 2 by solving a sparse group lasso problem with a non-negativity constraint.
scalpelStep3( step2Output, lambdaMethod = "trainval", lambda = NULL, minClusterSize = 1, alpha = 0.9, removeBorder = FALSE, excludeReps = NULL )scalpelStep3( step2Output, lambdaMethod = "trainval", lambda = NULL, minClusterSize = 1, alpha = 0.9, removeBorder = FALSE, excludeReps = NULL )
step2Output |
An object of class |
lambdaMethod |
A description of how lambda should be chosen: either |
lambda |
The value of lambda to use when fitting the sparse group lasso. By default, the value is automatically
chosen using the approach specified by |
minClusterSize |
The minimum number of preliminary dictionary elements that a cluster must contain in order to be included in the sparse group lasso. The default value is 1 (i.e., all possible dictionary elements are included). |
alpha |
The value of alpha to use when fitting the sparse group lasso. The default value is 0.9. |
removeBorder |
A logical scalar indicating whether the dictionary elements containing pixels in the 10-pixel
border of the video should be removed prior to fitting the sparse group lasso. The default value is |
excludeReps |
A vector giving the indices of which dictionary elements to exclude, where the indices refer
to the columns of |
To solve the sparse group lasso problem in this step, we minimize the following over Z with all non-negative elements:
0.5*sum((Y - AfilterTilde %*% Z)^2) + lambda*alpha*sum(Z) + lambda*(1-alpha)*sum(sqrt(rowSums(Z^2)))
where AfilterTilde is a scaled version of Afilter.
Several files containing data from this step and a summary of the step are saved in the folder
"outputFolder/Step1_version/Step2_omega_omega_cutoff_cutoff/Step3_lambdaMethod_lambdaMethod_
minClusterSize_minClusterSize_alpha_alpha_removeBorder_removeBorder" where version is a 5-digit unique
identifier that is automatically generated in Step 1, omega and cutoff are the user-supplied parameters
from Step 2, and lambdaMethod, minClusterSize, alpha, and removeBorder are
the user-supplied parameters from this step. If dictionary elements were manually excluded using excludeReps,
this is appended to the folder name.
An object of class scalpelStep3, which can be summarized using summary and used with the plotting functions
plotResults, plotResultsAllLambda, plotSpatial, plotTemporal, and plotBrightest.
Afilter: A matrix containing the filtered dictionary elements, where the ith column of Afilter
is a vector of 1's and 0's, indicating whether each pixel is contained in the ith filtered dictionary element.
Note that Afilter is equivalent to A after removing the components excluded due to being on the border
(if removeBorder=TRUE) or having fewer preliminary dictionary elements in their cluster than minClusterSize.
Zhat: A matrix containing the estimated temporal components, where the ith row of Zhat
is the estimated calcium trace corresponding to the ith spatial component (i.e., the ith column of Afilter).
lambda: The value of lambda used in fitting the sparse group lasso.
ZhatList: A list of matrices containing the estimated temporal components for alternative values of lambda
specified in lambdaSeq. These can be plotted using plotResultsAllLambda.
lambdaSeq: A vector with length equaling the length of ZhatList, where the ith element indicates the value of lambda
corresponding to the temporal components in ZhatList[[i]].
clustersUse: A vector with length equaling the number of columns of Afilter, where the ith element indicates which column of
step2Output$A the ith column of Afilter equals.
Other elements: As specified by the user or returned from a previous step.
The entire SCALPEL pipeline can be implemented using the scalpel function. The
other steps in the pipeline can be run using the scalpelStep0, scalpelStep1, scalpelStep2 functions.
Results from this step can be summarized using summary, loaded at a later time using getScalpelStep3, and plotted using
plotSpatial, plotTemporal, plotResults, and plotBrightest.
## 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 #assumes you have run the example for the "scalpelStep2" function #run Step 3 of SCALPEL Step3Out = scalpelStep3(step2Output = Step2Out) summary(Step3Out) ## End(Not run)## 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 #assumes you have run the example for the "scalpelStep2" function #run Step 3 of SCALPEL Step3Out = scalpelStep3(step2Output = Step2Out) summary(Step3Out) ## End(Not run)
Prints the parameters used and a summary of results for a specified step of SCALPEL.
## S3 method for class 'scalpelStep0' summary(object, ...) ## S3 method for class 'scalpelStep1' summary(object, ...) ## S3 method for class 'scalpelStep2' summary(object, ...) ## S3 method for class 'scalpelStep3' summary(object, ...) ## S3 method for class 'scalpel' summary(object, step, ...)## S3 method for class 'scalpelStep0' summary(object, ...) ## S3 method for class 'scalpelStep1' summary(object, ...) ## S3 method for class 'scalpelStep2' summary(object, ...) ## S3 method for class 'scalpelStep3' summary(object, ...) ## S3 method for class 'scalpel' summary(object, step, ...)
object |
An object returned by one of the SCALPEL functions:
|
... |
Additional arguments to be passed, which are ignored in this function. |
step |
The SCALPEL step (0, 1, 2, or 3) that you wish to summarize. This is only needed if summarizing an object of class |
None
## 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 #assumes you have run the examples for the "scalpelStep0" and "scalpel" functions summary(Step0Out) #summarize each step summary(scalpelOutput, step = 0) summary(scalpelOutput, step = 1) summary(scalpelOutput, step = 2) summary(scalpelOutput, step = 3) ## End(Not run)## 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 #assumes you have run the examples for the "scalpelStep0" and "scalpel" functions summary(Step0Out) #summarize each step summary(scalpelOutput, step = 0) summary(scalpelOutput, step = 1) summary(scalpelOutput, step = 2) summary(scalpelOutput, step = 3) ## End(Not run)
This function allows the user to update the classifications of neurons, based on manual sorting of
plots saved as a result of running reviewNeurons.
updateNeurons(scalpelOutput, neuronSet)updateNeurons(scalpelOutput, neuronSet)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
None
reviewNeurons, reviewNeuronsMoreFrames, reviewOverlappingNeurons
## 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 #assumes you have run the example for the "reviewNeurons" function updateNeurons(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)## 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 #assumes you have run the example for the "reviewNeurons" function updateNeurons(scalpelOutput = scalpelOutput, neuronSet = "Afilter") ## End(Not run)
This function allows the user to update the classifications of neurons, which
were reviewed previously using reviewNeuronsInteractive. Typically, this function
is used after running reviewNeuronsMoreFrames.
updateNeuronsInteractive(scalpelOutput, neuronSet)updateNeuronsInteractive(scalpelOutput, neuronSet)
scalpelOutput |
An object returned by one of the SCALPEL functions:
|
neuronSet |
The set of neurons that should be reviewed:
use |
None
reviewNeuronsInteractive, reviewNeuronsMoreFrames, reviewOverlappingNeurons
## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function updateNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #you will be prompted for the changes you wish to make ## End(Not run)## 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 #assumes you have run the example for the "reviewNeuronsInteractive" function updateNeuronsInteractive(scalpelOutput = scalpelOutput, neuronSet = "A") #you will be prompted for the changes you wish to make ## End(Not run)
We plot random frames from the video processed in Step 0 of SCALPEL with shading to indicate the smallest of the automatically chosen thresholds that will be used to perform image segmentation in Step 1 of SCALPEL. The user is given the option to try out different thresholds and if desired, update the threshold to use.
updateThreshold(step0Output)updateThreshold(step0Output)
step0Output |
An object of class |
An object identical to step0Output, except it may (depending on the user's decision) have lowThreshold updated.
## 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 #assumes you have run the example for the "scalpel" function #update the smallest threshold used for image segmentation in Step 1 scalpelOutput = updateThreshold(step0Output = scalpelOutput) ## End(Not run)## 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 #assumes you have run the example for the "scalpel" function #update the smallest threshold used for image segmentation in Step 1 scalpelOutput = updateThreshold(step0Output = scalpelOutput) ## End(Not run)