| Title: | Visualization of Methylation Data |
|---|---|
| Description: | methylscaper is an R package for processing and visualizing data jointly profiling methylation and chromatin accessibility (MAPit, NOMe-seq, scNMT-seq, nanoNOMe, etc.). The package supports both single-cell and single-molecule data, and a common interface for jointly visualizing both data types through the generation of ordered representational methylation-state matrices. The Shiny app allows for an interactive seriation process of refinement and re-weighting that optimally orders the cells or DNA molecules to discover methylation patterns and nucleosome positioning. |
| Authors: | Bacher Rhonda [aut, cre], Parker Knight [aut] |
| Maintainer: | Bacher Rhonda <[email protected]> |
| License: | GPL-2 |
| Version: | 1.15.0 |
| Built: | 2024-10-30 08:48:20 UTC |
| Source: | https://github.com/bioc/methylscaper |
Check if a package is installed
check_package(package)check_package(package)
package |
Name of the package. |
Message is returned if package not installed.
This reverses a subset of the ordering, as determined by the user. By default, the entire ordering is reversed.
forceReverse( orderObject, reverseStart = 1, reverseEnd = length(orderObject$order1) )forceReverse( orderObject, reverseStart = 1, reverseEnd = length(orderObject$order1) )
orderObject |
An object of class |
reverseStart |
The first index to be included in the reversal. |
reverseEnd |
The last index to be included in the reversal. |
The new complete ordering, with the reversal applied.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") # reorder first 50 cells/molecules (rows) orderObj$order1 <- refineFunction(orderObj, 1, 50) orderObj$order1 <- forceReverse(orderObj, 1, 50)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") # reorder first 50 cells/molecules (rows) orderObj$order1 <- refineFunction(orderObj, 1, 50) orderObj$order1 <- forceReverse(orderObj, 1, 50)
library(biomaRt) #v2.44.4 ensembl <- useMart("ensembl") # GRCh38 ensembl <- useDataset("hsapiens_gene_ensembl",mart=ensembl) my_chr <- c(1:22, 'M', 'X', 'Y') human_bm <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'hgnc_symbol'), filters = 'chromosome_name', values = my_chr, mart=ensembl)
data(human_bm)data(human_bm)
An object of class data.frame with 60580 rows and 4 columns.
This function performs the weighted seriation procedure described in the methylscaper manuscript if the method is set to "PCA". The data may also be ordered using a given seriation method from the seriation R package. The weighting is done between a designated start and end base pair chosen by the user, and the weight can be done on the endogenous methylation or the accessibility.
initialOrder( dataIn, Method = "PCA", weightStart = NULL, weightEnd = NULL, weightFeature = "red", updateProgress = NULL )initialOrder( dataIn, Method = "PCA", weightStart = NULL, weightEnd = NULL, weightFeature = "red", updateProgress = NULL )
dataIn |
A list object containing two elements labelled gch and hcg (already pre-processed.) |
Method |
Indicates the seriation method to use. The default option
is "PCA", which orders the data using a weighted first principal component approach. Any
seriation method provided in the |
weightStart |
Index of the first column used in the weighted seriation. |
weightEnd |
Index of the last column used in the weighted seriation. |
weightFeature |
Indicates whether to weight the GCH or HCG data. Valid input to weight the GCH is 'gch', 'acc', or 'yellow'. To weight the HCG, valid input for this option is 'hcg', 'met', or 'red'. |
updateProgress |
A function to handle the progress bar for the Shiny app. Should not be used when using the function independently. |
An object of class orderObject, which contains the generated
ordering ($order1) and a clean data matrix ($toClust)
to be passed into the plotting function plotSequence().
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example)
Calculate the average methylation/accessibility status across all cells/molecules.
methyl_average_status(orderObject, window_length = 20, makePlot = TRUE, ...)methyl_average_status(orderObject, window_length = 20, makePlot = TRUE, ...)
orderObject |
An object of class |
window_length |
Length of the window to be used to compute a moving average. Default is 20. |
makePlot |
Logical, indicates whether to generate a line plot of average status. |
... |
Addition parameters used by the |
The proportion of methylated bases for each cell/molecule within a defined moving window. Output is a list with elements "meth_avg" and "acc_avg", indicating endogenous or accessible methylation respectively.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_average_status(orderObj, makePlot = TRUE)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_average_status(orderObj, makePlot = TRUE)
Calculates the percentage of methylated cells/molecules per site
methyl_percent_sites(orderObject, makePlot = TRUE, ...)methyl_percent_sites(orderObject, makePlot = TRUE, ...)
orderObject |
An object of class |
makePlot |
Logical, indicates whether to generate the percentage plot |
... |
Additional parameters used by the |
The percent of molecules or cells methylated (endogenous (yellow) or accessible) at each site. Output is a list with names "red" and "yellow". Red represents the endogenous methylation and yellow represents the accessibility. Within each list object is a vector of the percent of cells/molecules methylated. The location of the site is also represent in the form CXX, where XX is the position of the site within the defined region.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_percent_sites(orderObj, makePlot = TRUE)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_percent_sites(orderObj, makePlot = TRUE)
Calculate the proportion of methylated bases for each cell/molecule
methyl_proportion(orderObject, type = "yellow", makePlot = TRUE, ...)methyl_proportion(orderObject, type = "yellow", makePlot = TRUE, ...)
orderObject |
An object of class |
type |
Indicates which data set to compute proportions for. This should be 'met' or 'hcg' or 'red' for endogenous methylation; 'acc' or 'gch' or 'yellow' for accessibility. |
makePlot |
Indicates whether to plot a histogram of the proportions across all cells/molcules. |
... |
Additional parameters used by the |
The proportion of methylated (endogenous (yellow) or accessible) bases for each cell/molecule. Output is vector with length the numbner of cells/molecules and contains a proportion.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_proportion(orderObj, makePlot = TRUE)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") methyl_proportion(orderObj, makePlot = TRUE)
Runs the methylscaper Shiny app.
methylscaper()methylscaper()
This starts up the shiny app interface for methylscaper.
# methylscaper()# methylscaper()
library(biomaRt) #v2.44.4 ensembl <- useMart("ensembl") # GRCm39 ensembl <- useDataset("mmusculus_gene_ensembl",mart=ensembl) my_chr <- c(1:19, 'M', 'X', 'Y') mouse_bm <- getBM(attributes=c('chromosome_name', 'start_position', 'end_position', 'mgi_symbol'), filters = 'chromosome_name', values = my_chr, mart=ensembl)
data(mouse_bm)data(mouse_bm)
An object of class data.frame with 55365 rows and 4 columns.
Generates an ordered sequence plot of methylation data.
plotSequence( orderObject, plotFast = TRUE, blankWidth = NULL, Title = "", drawLine = TRUE, drawKey = TRUE, shinySizer = 0 )plotSequence( orderObject, plotFast = TRUE, blankWidth = NULL, Title = "", drawLine = TRUE, drawKey = TRUE, shinySizer = 0 )
orderObject |
An object of class |
plotFast |
Logical, setting to FALSE will generate a higher quality plot. TRUE generates a lower resolution file, useful to improve speed while testing. For publication quality use plotFast=TRUE. |
blankWidth |
Indicates the amount of space to leave between the two plots |
Title |
The title of the plot. |
drawLine |
Logical, indicates whether to draw a line above the CG/GC sites. |
drawKey |
Logical, indicates whether to draw a key representing a 147bp nucleosome at the bottom of the plot. |
shinySizer |
internal sizing parameter for plot layout in shiny app. |
Output is two side-by-side heatmaps with the endogenous methylation (HCG) on the left and the acciessibility methylation (GCH) on the right. The tick marks at the top indicate either HCG or GCH sites when drawLine=TRUE. If drawKey=TRUE then a black rectangle key is plot at the bottom of the heatmap that is 147 basepairs long. In the HCG plot, red patches represent methylation between two sites; black patches represent unmethylated bases between two unmethylated sites; and gray patches are base pairs which have one methylated site and one unmethylated site flanking. In the HCG plot, yellow patches represent accessibility between two sites; black patches represent occupied bases between two occupied sites; and gray patches are base pairs which have one methylated site and one unmethylated site flanking.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") plotSequence(orderObj)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") plotSequence(orderObj)
This function subsets the data and prepares it for visualizing by generating representation methylation-state matrices from single-cell methylation data (for example, sc-MNT data). We assume the data has already been preprocess using the subsetSC function in methylscaper. See the vignette for a more thorough explanation of each parameter. The output should be passed directly to the plotting function.
prepSC(dataIn, startPos = NULL, endPos = NULL, updateProgress = NULL)prepSC(dataIn, startPos = NULL, endPos = NULL, updateProgress = NULL)
dataIn |
A list object containing two elements labelled gch and hcg (already pre-processed.) |
startPos |
The index of the first position to include in the visualization. If using this within the R console it is recomended to specify the start and end directly. In the Shiny app, a slider will let the user refine these positions. |
endPos |
The index of the final position to include in the visualization. |
updateProgress |
A function for generating progress bars in the Shiny app. Should be left NULL otherwise. |
The output is a list containing the elements 'gch' and 'hcg. Each is a dataframe with reads/cells on the rows and each column is a base-pair. The matrix is coded as follows: -2: unmethylated GCH or HCG site -1: base pairs between two unmethylated GCH or HCG sites 0: base pairs between mismatching methylation states of two GCH or HCG sites 1: base pairs between two methylated GCH or HCG sites 2: methylated GCH or HCG site
data(singlecell_subset) prepsc.out <- prepSC(singlecell_subset, startPos = 105636488, endPos = 105636993 )data(singlecell_subset) prepsc.out <- prepSC(singlecell_subset, startPos = 105636488, endPos = 105636993 )
This dataset was loaded into R using seqinr::read.fasta
data(reads_sm)data(reads_sm)
An object of class list of length 293.
This dataset was loaded into R using seqinr::read.fasta
data(ref_seq)data(ref_seq)
An object of class list of length 1.
Reorders a subset of the methylation data.
refineFunction(orderObject, refineStart, refineEnd, Method = "PCA")refineFunction(orderObject, refineStart, refineEnd, Method = "PCA")
orderObject |
An object of class |
refineStart |
The index of the first sample (row) used in the refinement. |
refineEnd |
The index of the last sample (row) used in the refinement. |
Method |
The seriation method used to perform the refinement. |
The refinement reorders the cells/molecules (rows) between the indicated start and end positions. The function returns the new complete ordering with the refinement applied.
data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") # reordering the first 50 cells/molecules (rows) orderObj$order1 <- refineFunction(orderObj, 1, 50)data(singlemolecule_example) orderObj <- initialOrder(singlemolecule_example, Method = "PCA") # reordering the first 50 cells/molecules (rows) orderObj$order1 <- refineFunction(orderObj, 1, 50)
This is an internal function for now and a place-holder in case SingleCellExperiment may be used in the future. We may need to update this later. Assumes rownames are formatted like chr_pos and there are two assays. The assay names are 'methylation_met' for endogenous methylation and 'methylation_acc' for accessibility.
reformatSCE(dataIn)reformatSCE(dataIn)
dataIn |
Input SCE object passed to the prepSC() function. |
List object containing the met and acc tables.
Runs the preprocessing methods for single-molecule data.
runAlign( ref, fasta, fasta_subset = seq(1, length(fasta)), multicoreParam = NULL, updateProgress = NULL, log_file = NULL, score_cutoff = NULL )runAlign( ref, fasta, fasta_subset = seq(1, length(fasta)), multicoreParam = NULL, updateProgress = NULL, log_file = NULL, score_cutoff = NULL )
ref |
A reference sequence to align the reads to. |
fasta |
A list of reads/sequences from a single-molecule experiment (e.g. MAPit) |
fasta_subset |
(optional) A vector of indices indicating which sequences to process if a subset should be used. Leave this blank if all sequences should be processed. |
multicoreParam |
(optional) A MulticoreParam object, used to align sequences in parallel. |
updateProgress |
(optional) Used to add a progress bar to the Shiny app. Should not be used otherwise. |
log_file |
(optional) String indicating where to save a log of the alignment process. If left NULL, no log is saved. We highly recommend saving a log file. |
score_cutoff |
(optional) Used mostly for testing purposes. |
The output is a list containing the the matrices 'gch' and 'hcg. Each is a dataframe with reads/cells on the rows and each column is a base-pair. The matrix represents the methylation state for cell across all base pairs. The coding is as follows: -2: unmethylated GCH or HCG site -1: base pairs between two unmethylated GCH or HCG sites 0: base pairs between mismatching methylation states of two GCH or HCG sites 1: base pairs between two methylated GCH or HCG sites 2: methylated GCH or HCG site
data(reads_sm) data(ref_seq) example_alignedseq <- runAlign(fasta = reads_sm, ref = ref_seq[[1]], fasta_subset = 1:150)data(reads_sm) data(ref_seq) example_alignedseq <- runAlign(fasta = reads_sm, ref = ref_seq[[1]], fasta_subset = 1:150)
This data is from GSE109262, and has been pre-processed by methylscaper It contains a small subset of chromosome 19 region from 8947041bp - 8987041bp. The RDS in ext data was made specifically with the two commands: singlecell_subset <- subsetSC("~/Downloads/GSE109262_RAW/", chromosome="19", startPos = 8967041-20000, endPos = 8967041+20000, updateProgress = NULL) saveRDS(singlecell_subset, file="methylscaper/inst/ext/singlecell_subset.rds", compress = 'xz') A version is also saved as RData used running examples in the man pages. save(singlecell_subset, file="methylscaper/data/singlecell_subset.RData", compress = 'xz')
data(singlecell_subset)data(singlecell_subset)
An object of class list of length 2.
The RDS in ext data was made specifically with the command: singlemolecule_example <- methylscaper::runAlign(fasta=reads_sm, ref=ref_seq) saveRDS(singlemolecule_example, file="methylscaper/inst/ext/singlemolecule_example.rds", compress = 'xz') A version is also saved as RData used running examples in the man pages. save(singlemolecule_example, file="methylscaper/data/singlemolecule_example.RData", compress = 'xz')
data(singlemolecule_example)data(singlemolecule_example)
An object of class list of length 2.
This function loads the single-cell files. It takes a path to the data files and a chromosome number as arguments and returns the desired subset of the data. Processing by chromosome is important for speed and memory efficiency. The input files should be tab separated with three columns. The first column is the chromosome, the second is the position (basepair), and the third is the methylation indicator/rate. The folder should contain two subfolders titled met and acc, with the endogenous methylation and accessibility methylation files, respectively.
subsetSC( path, chromosome, startPos = NULL, endPos = NULL, updateProgress = NULL )subsetSC( path, chromosome, startPos = NULL, endPos = NULL, updateProgress = NULL )
path |
Path to the folder containing the single-cell files. |
chromosome |
The chromosome to subset the files to. |
startPos |
The index of the first position to include in the subsetting. This is optional as further narrowing of the position can be done in the visualization step/tab. In the Shiny app, a slider will let the user refine the positions. |
endPos |
The index of the final position to include in subset. |
updateProgress |
A function for generating progress bars in the Shiny app. Should be left NULL otherwise. |
The output is RDS files that can be loaded into the visualization tab on the Shiny app
# example not run since needs directory input from user # subsc.out <- subsetSC("filepath", chromosome=19)# example not run since needs directory input from user # subsc.out <- subsetSC("filepath", chromosome=19)