| Type: | Package |
| Title: | Multiple SIGnal SEGmentation |
| Version: | 0.2.0 |
| Description: | Traditional methods typically detect breakpoints from individual signals, which means that when applied separately to multiple signals, the breakpoints are not aligned. However, this package implements a common breakpoint detection approach for multiple piecewise constant signals, resulting in increased detection sensitivity and specificity. By employing various techniques, optimal performance is ensured, and computation is accelerated. We hope that this package will be beneficial for researchers in signal processing, bioinformatics, economy, and other related fields. The segmentation(), lambda_estimator() functions are the main functions of this package. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| Collate: | 'MSigSeg_Class.R' 'allGenerics.R' 'brkps_method.R' 'change.R' 'data.R' 'data.input_method.R' 'data.output_method.R' 'funtion_colsum_colmean.R' 'lambda_estimator.R' 'lambda_method.R' 'multi_plot.R' 'noisegen.R' 'print_method.R' 'seg.len_method.R' 'segmentation.R' 'summary_method.R' |
| Depends: | R (≥ 4.0.0), methods |
| Imports: | MASS, ggpubr, ggplot2 |
| NeedsCompilation: | no |
| Packaged: | 2023-11-13 13:02:43 UTC; lxy123 |
| Author: | Xuanyu Liu [aut, cre], Junbo Duan [aut] |
| Maintainer: | Xuanyu Liu <lxy382198251@stu.xjtu.edu.cn> |
| Repository: | CRAN |
| Date/Publication: | 2023-11-13 15:03:21 UTC |
An S4 class to encapsulation the result of breakpoints analysis.
Description
An S4 class to encapsulation the result of breakpoints analysis.
Slots
data.inputAn data.frame/matrix containing the data to be segmented. Each column stores a signal.
data.outputA matrix containing the input data which has been smoothed.
lambdaA penalty term, small value leads to large number of breakpoints, and vice versa.
brkpsA vector containing the locations of common breakpoints.
fminA numeric containing the optimal numerical value calculated.
dateCharacter string containing date information.
influenza data set from CDC used as an example.
Description
influenza data set from CDC used as an example.
Usage
NCHSData
Format
A matrix with 52 rows and 10 columns.
Examples
data("NCHSData",package = "MSigSeg")
A chromosome sequencing data set used as an example.
Description
A chromosome sequencing data set used as an example.
Usage
T16M
Format
A data.frame with 2928 rows and 22 columns.
References
Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90-94. doi:10.1038/nature09807
Examples
data("T16M",package = "MSigSeg")
A chromosome sequencing data set used as an example.
Description
A chromosome sequencing data set used as an example.
Usage
T16P
Format
A data.frame with 2928 rows and 16 columns.
References
Navin N, Kendall J, Troge J, et al. Tumour evolution inferred by single-cell sequencing. Nature. 2011;472(7341):90-94. doi:10.1038/nature09807
Examples
data("T16P",package = "MSigSeg")
Generic Function-brkps.
Description
This function returns the brkps slot of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
brkps(object)
Arguments
object |
A 'MSigSeg' object. |
Details
This function is a S4 method for MSigSeg object. It retrieves brkps slot, which contains the locations of break points.
Value
The brkps slot of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
brkps(x)
Breakpoints matrix generation.
Description
Generate matrix based on specified breakpoints.
Usage
change(M, p = 0.01)
Arguments
M |
A matrix users aimed to add breakpoints. |
p |
Probability of occurrence of breakpoints. |
Details
Generate matrix with common breakpoints, based on specific probability of occurrence.
Value
A list containing the matrix with specified change point and the location of breakpoints.
Generic Function-data.input.
Description
This function returns the data.input slot of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
data.input(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It retrieves data.input slot, which contains the data users input.
Value
The data.input slot of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
data.input(x)
Generic Function-data.output.
Description
This function returns the data.output slot of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
data.output(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It retrieves data.output slot, which contains the input data which has been smoothed..
Value
The data.output slot of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
data.output(x)
A simulated data set used for testing.
Description
A simulated data set used for testing.
Usage
data_test
Format
A matrix with 1000 rows and 20 columns.
Examples
data("data_test",package = "MSigSeg")
Generic Function-lambda.
Description
This function returns the lambda slot of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
lambda(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It retrieves lambda slot, which contains penalty coefficient to prevent over fitting.
Value
The lambda slot of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
lambda(x)
Detecting common breakpoints with designated number.
Description
Automatic estimation of penalty parameter lambda for user defined breakpoints number.
Usage
lambda_estimator(Y, K)
Arguments
Y |
An data.frame/matrix containing the data to be segmented. Each column stores a signal. |
K |
Number of change points users want to detect. |
Details
This function is based on the segmentation() function. Number of breakpoints are defined by users and lambda is calculated by algorithm automatically.
Value
An object of S4 class "MSigSeg".
Examples
data(data_test)
lambda_estimator(data_test,5)
Plot function of MSigSeg package.
Description
Graph signals and breakpoints based on ggplot2 and ggarange packages.
Usage
multi_plot(m, ncol, nrow)
Arguments
m |
An object of S4 class "MSigSeg". |
ncol |
Column numbers of signals arrangement in the graph. |
nrow |
Row numbers of signals arrangement in the graph |
Value
A list, first item in the list is a graphic objects with all signals drawn and second is a list with individual signals.
Examples
data(data_test)
m <- segmentation(data_test,100)
p <- multi_plot(m,4,5)
Noisegen.
Description
Generate matrix based on signal-to-noise ratio.
Usage
noisegen(X, SNR)
Arguments
X |
A matrix users aimed to add signal-to-noise ratio. |
SNR |
Signal-to-noise ratio. |
Value
A matrix with specified signal-to-noise ratio.
Generic Function-print.
Description
This function print the basic information of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
print(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It prints class, slots, created date and summary of MSigSeg object.
Value
The the basic information of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
print(x)
Generic Function-seg.len.
Description
This function returns the length of segmentation.
Usage
## S4 method for signature 'MSigSeg'
seg.len(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It calculates the distance between each change points.
Value
A vector contains length of segmentation.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
seg.len(x)
Detecting common change points for multiple signals.
Description
Calculates the optimal positioning and number of common breakpoints for multiple signals.
Usage
segmentation(Y, lambda, flag = TRUE, return_smooth_signals = TRUE)
Arguments
Y |
An data.frame/matrix containing the data to be segmented. Each column stores a signal. |
lambda |
A penalty term, small value leads to large number of breakpoints, and vice versa. |
flag |
Logical. If True then use th PELT method. If False then use the OP method. |
return_smooth_signals |
Logical. If True then smoothed signals are returned. |
Details
This function uses modified PELT method to find optimal common change points for multiple signals.
Value
An object of S4 class "MSigSeg"
Examples
data(data_test)
segmentation(data_test,100)
A stock data set used as an example.
Description
A stock data set used as an example.
Usage
stock
Format
A data.frame with 757 rows and 488 columns.
Examples
data("stock",package = "MSigSeg")
Generic Function-summary.
Description
This function summarize the information of MSigSeg object.
Usage
## S4 method for signature 'MSigSeg'
summary(object)
Arguments
object |
A MSigSeg object. |
Details
This function is a S4 method for MSigSeg object. It summarizes the number of signals, length of signals, number of change points and fmin.
Value
A summary of MSigSeg object.
Examples
x=new("MSigSeg") # Creating a new MSigSeg object.
summary(x)