Package 'ArchR'

An S4Vector object to search for in table.

The set of S4Vector objects to serve as the base for the match function.

An S4Vector object to search for in table.

The set of S4Vector objects to serve as the base for the match function.

The value to search for in table.

The set of values to serve as the base for the match function.

An ArchRProject object.

A string indicating the database or a path to a database to use for peak annotation. Options include ArchR, LOLA, and a valid path to a file of class ArchRAnno.

A string indicating which collection within the database to collect for annotation. For ArchR, options are "ATAC", "EncodeTFBS", "CistromeTFBS", or "Codex". For LOLA, options include "EncodeTFBS" "CistromeTFBS", "CistromeEpigenome", "Codex", or "SheffieldDnase". If supplying a custom ArchRAnno file please select a valid collection from within that database.

The name of the peakAnnotation object to be stored in the ArchRProject.

A boolean value indicating whether to force the peakAnnotation object indicated by name to be overwritten if it already exists in the given ArchRProject.

The path to a file to be used for logging ArchR output.

A boolean describing the requirement of chromosomes to have a "chr" prefix.

A boolean describing whether to use logging with ArchR.

A string indicating the default genome to be used for all ArchR functions. Currently supported values include "hg19","hg38","mm9", and "mm10". This value is stored as a global environment variable, not part of the ArchRProject. This can be overwritten on a per-function basis using the given function's geneAnnotationand genomeAnnotation parameter. For something other than one of the currently supported, see createGeneAnnnotation() and createGenomeAnnnotation().

A boolean value indicating whether the BSgenome object associated with the provided genome should be automatically installed if it is not currently installed. This is useful for helping reduce user download requirements.

A boolean describing whether to use logging with ArchR.

The default number of threads to be used for parallel execution across all ArchR functions. This value is stored as a global environment variable, not part of the ArchRProject. This can be overwritten on a per-function basis using the given function's threads parameter.

If you request more than the total number of CPUs minus 2, ArchR will set threads to (nCPU - 2). To bypass this, setting force = TRUE will use the number provided to threads.

A boolean describing whether to printMessages in addition to logging with ArchR.

An ArchRProject object.

The number of background peaks to sample. See chromVAR::getBackgroundPeaks().

The parameter controlling similarity of background peaks. See chromVAR::getBackgroundPeaks().

The precision with which the similarity is computed. See chromVAR::getBackgroundPeaks().

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A string indicating whether to use chromVAR or ArchR for background peak identification.

The path to save the backgroundPeaks object as a .RDS file for the given ArchRProject. The default action is to save this file in the outputDirectory of the ArchRProject.

A boolean value indicating whether to force the file indicated by outFile to be overwritten if it already exists.

An ArchRProject object.

The data to add to cellColData.

The column header name to be used for this new data in cellColData. If a column with this name already exists, you may set force equal to TRUE to overwrite the data in this column.

The names of the cells corresponding to data. Typically new data is added to all cells but you may use this argument to only add data to a subset of cells. Cells where data is not added are set to NA.

A boolean value indicating whether or not to overwrite data in a given column when the value passed to name already exists as a column name in cellColData.

Either (i) an ArchRProject object containing the dimensionality reduction matrix passed by reducedDims or (ii) a dimensionality reduction matrix. This object will be used for cluster identification.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject. Not required if input is a matrix.

The column name of the cluster label column to be added to cellColData if input is an ArchRProject object.

An integer specifying the number of cells to subsample and perform clustering on. The remaining cells that were not subsampled will be assigned to the cluster of the nearest subsampled cell. This enables a decrease in run time but can sacrifice granularity of clusters.

A number to be used as the seed for random number generation required in cluster determination. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A string indicating the clustering method to be used. Supported methods are "Seurat" and "Scran".

A vector containing the dimensions from the reducedDims object to use in clustering.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

The number of nearest neighbors to be used during clustering for assignment of outliers (clusters with less than nOutlier cells).

The minimum number of cells required for a group of cells to be called as a cluster. If a group of cells does not reach this threshold, then the cells will be considered outliers and assigned to nearby clusters.

The maximum number of clusters to be called. If the number exceeds this the clusters are merged unbiasedly using hclust and cutree. This is useful for contraining the cluster calls to be reasonable if they are converging on large numbers. Useful in iterativeLSI as well for initial iteration. Default is set to 25.

A boolean value that indicates whether or not to test clusters for bias.

A boolean value indicates whether or not to filter clusters that are identified as biased.

A numeric value between 0 and 1 indicating that clusters that are smaller than the specified proportion of total cells are to be checked for bias. This should be set close to 0. We recommend a default of 0.01 which specifies clusters below 1 percent of the total cells.

The name of a column in cellColData that contains the numeric values used for testing bias enrichment.

A set of numeric values used for testing bias enrichment if input is not an ArchRProject.

A vector of two numeric values, each between 0 and 1, that describes the lower and upper quantiles of the bias values to use for computing bias enrichment statistics.

A numeric value that specifies the minimum enrichment of biased cells over the median of the permuted background sets.

A numeric value between 0 and 1 that specifies the minimum proportion of biased cells in a cluster required to determine that the cluster is biased during testing for bias-enriched clusters.

A numeric value between 0 and 1 that specifies the p-value to use when testing for bias-enriched clusters.

An integer specifying the number of permutations to perform for testing bias-enriched clusters.

A character string to be added before each cluster identity. For example, if "Cluster" then cluster results will be "Cluster1", "Cluster2" etc.

An ArchRProject object containing the dimensionality reduction matrix passed by reducedDims. This argument can also be supplied as input.

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

A timestamp that is typically passed internally from another function (for ex. "IterativeLSI") to measure how long the clustering analysis has been running relative to the start time when this process was initiated in another function. This argument is rarely manually specified.

A boolean value that indicates whether or not to overwrite data in a given column when the value passed to name already exists as a column name in cellColData.

The path to a file to be used for logging ArchR output.

Additional arguments to be provided to Seurat::FindClusters or scran::buildSNNGraph (for example, knn = 50, jaccard = TRUE)

An ArchRProject object.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject.

A vector containing the dimensions from the reducedDims object to use in clustering.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

A character vector of cellNames to compute coAccessibility on if desired to run on a subset of the total cells.

The number of k-nearest neighbors to use for creating single-cell groups for correlation analyses.

The number of k-nearest neighbor groupings to test for passing the supplied overlapCutoff.

The maximum allowable overlap between the current group and all previous groups to permit the current group be added to the group list during k-nearest neighbor calculations.

The maximum allowable distance in basepairs between two peaks to consider for co-accessibility.

The total insertion counts from the designated group of single cells is summed across all relevant peak regions from the peakSet of the ArchRProject and normalized to the total depth provided by scaleTo.

A boolean value indicating whether to log2 transform the single-cell groups prior to computing co-accessibility correlations.

A number to be used as the seed for random number generation required in knn determination. It is recommended to keep track of the seed used so that you can reproduce results downstream.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output should be printed.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name for the combinedDims to be stored as.

The name of the reducedDims objects (i.e. "IterativeLSI") to use from the designated ArchRProject.

A vector of weights to be used to weight each dimensionality reduction when combining.

A vector containing the dimensions from the reducedDims objects to use.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

An ArchRProject object.

The file path to the .best files created by Demuxlet. There should be one .best file for each sample in the ArchRProject.

The sample names corresponding to the .best files. These must match the sample names present in the ArchRProject.

An ArchRProject object.

The name of the peakAnnotation stored in the ArchRProject.

A custom peakAnnotation matches object used as input for the hypergeometric test. See motifmatchr::matchmotifs() for additional information.

A SummarizedExperiment that contains for each peak a set of background peaks matched by biases such as total accessibility and GC nucleotide content. This can be computed using addBgdPeaks and accessed by getBgdPeaks.

The name to be used for storage of the deviations matrix in the provided ArchRProject.

A string or character vector that indicates whether to save the ouptut matrices as deviations ("deviations") z-scores ("z"), or both (c("deviations","z")).

A boolean value indicating whether the input matrix should be binarized before calculating deviations. This is often desired when working with insertion counts.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output includes verbose sections.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether to force the matrix indicated by matrixName to be overwritten if it already exists in the ArrowFiles associated with the given ArchRProject.

The path to a file to be used for logging ArchR output.

An ArchRProject object or a character vector containing the paths to the ArrowFiles to be used.

The name of the matrix to be used for performing doublet identification analyses. Options include "TileMatrix" and "PeakMatrix".

The number of cells neighboring a simulated doublet to be considered as putative doublets.

The number of times to simulate nCell (number of cells in the sample) doublets to use for doublet simulation when calculating doublet scores.

A vector containing the dimensions from the reducedDims object to use in clustering.

A number or string indicating the order of operations in the TF-IDF normalization. Possible values are: 1 or "tf-logidf", 2 or "log(tf-idf)", and 3 or "logtf-logidf".

A boolean that indicates whether to z-score the reduced dimensions for each cell during the LSI method performed for doublet determination. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

The name of the dimensionality reduction method to be used for k-nearest neighbors calculation. Possible values are "UMAP" or "LSI".

The list of parameters to pass to the UMAP function if "UMAP" is designated to knnMethod. See the function umap in the uwot package.

The list of parameters to pass to the IterativeLSI() function. See IterativeLSI().

The relative path to the output directory for relevant plots/results from doublet identification.

The number of threads to be used for parallel computing.

If the UMAP projection is not accurate (when R < 0.8 for the reprojection of the training data - this occurs when you have a very homogenous population of cells), setting force=FALSE will return -1 for all doubletScores and doubletEnrichments. If you would like to override this (not recommended!), you can bypass this warning by setting force=TRUE.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value that determines whether standard output is printed.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

A GRanges object of features to count scATAC-seq data in.

A character defining the name of the features. "nameCounts" and "nameRatio" will be added to the ArchRProject.

A boolean indicating whether to add the "nameRatio" to the ArchRProject.

The number of threads to use for parallel execution.

The path to a file to be used for logging ArchR output.

An ArchRProject object or character vector of paths to ArrowFiles.

A GRanges object containing the regions (aka features) to use for counting insertions for each cell.

The name to be used for storage of the feature matrix in the provided ArchRProject or ArrowFiles.

The maximum counts per feature allowed. This is used to prevent large biases in feature counts.

A boolean value indicating whether the feature matrix should be binarized prior to storage. This can be useful for downstream analyses when working with insertion counts.

A boolean value that determines whether standard output includes verbose sections.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether to force the matrix indicated by matrixName to be overwritten if it already exists in the input.

The path to a file to be used for logging ArchR output.

An ArchRProject object or character vector of ArrowFiles.

A a scRNA-seq SummarizedExperiment (cell x gene) to be integrated with the scATAC-seq data. Cell names from this object much match those of the cell names in the ArrowFiles/ArchRProject. We will add support shortly for Seurat Objects (see Seurat::as.SingleCellExperiment). The provided values MUST be in counts (integer), not log transformed.

A GRanges object of the chromosome lengths. See getChromSizes for more info.

A character vector containing the seqnames of the chromosomes that should be excluded from this analysis.

Each column in the calculated gene score matrix will be normalized to a column sum designated by scaleTo.

A boolean describing whether to print to console messages of progress.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether every cell in input must be represented in seRNA. If set to FALSE, this and this GeneExpressionMatrix is used for certain downstream analyses such as addIterativeLSI(), then errors may occur because not all cells will have relevant information.

A boolean value indicating whether to force the matrix indicated by matrixName to be overwritten if it already exist in the given input.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of a matrix in the ArchRProject containing gene scores to be used for RNA integration.

The name to use for the output matrix containing scRNA-seq integration to be stored in the ArchRProject.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject. This reducedDims will be used in weighting the transfer of data to scRNA to scATAC. See Seurat::TransferData for more info.

A SeuratObject or a scRNA-seq SummarizedExperiment (cell x gene) to be integrated with the scATAC-seq data.

A column name in cellColData of the ArchRProj that will be used to determine the subgroupings specified in groupList. This is used to constrain the integration to occur across biologically relevant groups.

A column name in either colData (if SummarizedExperiment) or metadata (if SeuratObject) of seRNA that will be used to determine the subgroupings specified in groupList. This is used to constrain the integration to occur across biologically relevant groups. Additionally this groupRNA is used for the nameGroup output of this function.

A list of cell groupings for both ATAC-seq and RNA-seq cells to be used for RNA-ATAC integration. This is used to constrain the integration to occur across biologically relevant groups. The format of this should be a list of groups with subgroups of ATAC and RNA specifying cells to integrate from both platforms. For example groupList <- list(groupA = list(ATAC = cellsATAC_A, RNA = cellsRNA_A), groupB = list(ATAC = cellsATAC_B, RNA = cellsRNA_B))

An integer describing the number of scATAC-seq cells to be used for integration. This number will be evenly sampled across the total number of cells in the ArchRProject.

An integer describing the number of scRNA-seq cells to be used for integration.

A data.frame of cell embeddings such as a UMAP for scATAC-seq cells to be used for density sampling. The data.frame object should have a row for each single cell described in row.names and 2 columns, one for each dimension of the embedding.

A data.frame of cell embeddings such as a UMAP for scRNA-seq cells to be used for density sampling. The data.frame object should have a row for each single cell described in row.names and 2 columns, one for each dimension of the embedding.

A vector containing the dimensions from the reducedDims object to use in clustering.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

A boolean determining whether to plot a UMAP for each integration block.

The list of parameters to pass to the UMAP function if "plotUMAP = TRUE". See the function umap in the uwot package.

The number of variable genes determined by Seurat::FindVariableGenes() to use for integration.

A boolean value indicating whether to use imputation for creating the Gene Score Matrix prior to integration.

The Seurat reduction method to use for integrating modalities. See Seurat::FindTransferAnchors() for possible reduction methods.

A boolean value indicating whether to add the log2-normalized transcript counts from the integrated matched RNA to the Arrow files.

Each column in the integrated RNA matrix will be normalized to a column sum designated by scaleTo prior to adding to Arrow files.

If desired a character vector of gene names to use for integration instead of determined ones from Seurat::variableGenes.

A column name to add to cellColData for the predicted scRNA-seq cell in the specified ArchRProject. This is useful for identifying which cell was closest to the scATAC-seq cell.

A column name to add to cellColData for the predicted scRNA-seq group in the specified ArchRProject. See groupRNA for more details.

A column name to add to cellColData for the predicted scRNA-seq score in the specified ArchRProject. These scores represent the assignment accuracy of the group in the RNA cells. Lower scores represent ambiguous predictions and higher scores represent precise predictions.

Additional params to be passed to Seurat::TransferData.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output includes verbose sections.

A boolean value indicating whether to force the matrix indicated by matrixName to be overwritten if it already exists in the given input.

The path to a file to be used for logging ArchR output.

Additional params to be added to Seurat::FindTransferAnchors

An ArchRProject object or character vector of ArrowFiles.

A stranded GRanges object containing the ranges associated with all gene start and end coordinates.

A string giving a "gene model function" used for weighting peaks for gene score calculation. This string should be a function of x, where x is the stranded distance from the transcription start site of the gene.

The name to be used for storage of the gene activity score matrix in the provided ArchRProject or ArrowFiles.

The minimum and maximum number of basepairs upstream of the transcription start site to consider for gene activity score calculation.

The minimum and maximum number of basepairs downstream of the transcription start site or transcription termination site (based on 'useTSS') to consider for gene activity score calculation.

An integer describing the number of bp upstream the gene to extend the gene body. This effectively makes the gene body larger as there are proximal peaks that should be weighted equally to the gene body. This parameter is used if 'useTSS=FALSE'.

An integer describing the number of bp downstream the gene to extend the gene body.This effectively makes the gene body larger as there are proximal peaks that should be weighted equally to the gene body. This parameter is used if 'useTSS=FALSE'.

A boolean value indicating whether gene boundaries should be employed during gene activity score calculation. Gene boundaries refers to the process of preventing tiles from contributing to the gene score of a given gene if there is a second gene's transcription start site between the tile and the gene of interest.

A boolean describing whether to build gene model based on gene TSS or the gene body.

A boolean describing whether to extend the gene TSS. By default useTSS uses the 1bp TSS while this parameter enables the extension of this region with 'geneUpstream' and 'geneDownstream' respectively.

The size of the tiles used for binning counts prior to gene activity score calculation.

The maximum counts per tile allowed. This is used to prevent large biases in tile counts.

A numeric scaling factor to weight genes based on the inverse of there length i.e. (Scale Factor)/(Gene Length). This is scaled from 1 to the scale factor. Small genes will be the scale factor while extremely large genes will be closer to 1. This scaling helps with the relative gene score value.

Each column in the calculated gene score matrix will be normalized to a column sum designated by scaleTo.

A character vector containing the seqnames of the chromosomes that should be excluded from this analysis.

A GRanges object containing genomic regions to blacklist that may be extremeley over-represented and thus biasing the geneScores for genes nearby that locus.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean determining whether possible use threads within each multi-threaded subprocess if greater than the number of input samples.

A boolean value indicating whether to force the matrix indicated by matrixName to be overwritten if it already exist in the given input.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the column in cellColData to use for grouping multiple cells together prior to generation of the insertion coverage file.

A boolean value indicating whether to use sample labels to create sample-aware subgroupings during as pseudo-bulk replicate generation.

The name of a column in cellColData to use to identify samples. In most cases, this parameter should be left as NULL and you should only use this parameter if you do not want to use the default sample labels stored in cellColData$Sample. However, if your individual Arrow files do not map to individual samples, then you should set this parameter to accurately identify your samples. This is the case in (for example) multiplexing applications where cells from different biological samples are mixed into the same reaction and demultiplexed based on a lipid barcode or genotype.

The minimum number of cells required in a given cell group to permit insertion coverage file generation.

The maximum number of cells to use during insertion coverage file generation.

The maximum number of fragments per cell group to use in insertion coverage file generation. This prevents the generation of excessively large files which would negatively impact memory requirements.

The minimum number of pseudo-bulk replicates to be generated.

The maximum number of pseudo-bulk replicates to be generated.

The fraction of the total cells that can be sampled to generate any given pseudo-bulk replicate.

The length of the k-mer used for estimating Tn5 bias.

The number of threads to be used for parallel computing.

A boolean value that indicates whether to return sample-guided cell-groupings without creating coverages. This is used mainly in addReproduciblePeakSet() when MACS2 is not being used to call peaks but rather peaks are called from a TileMatrix (peakMethod = "Tiles").

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value that indicates whether or not to overwrite the relevant data in the ArchRProject object if insertion coverage / pseudo-bulk replicate information already exists.

A boolean value that determines whether standard output includes verbose sections.

The path to a file to be used for logging ArchR output.

An ArchRProject object containing the dimensionality reduction matrix passed by reducedDims.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject.

A vector containing the dimensions from the reducedDims object to use in clustering.

A boolean that indicates whether to z-score the reduced dimensions for each cell. This is useful forminimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

The name to store harmony output as a reducedDims in the ArchRProject object.

The name of the column in cellColData to use for grouping cells together for vars in harmony batch correction. The value of groupBy is passed to the vars_use parameter in harmony::HarmonyMatrix(). When run through ArchR, this parameter defines which variables to correct for during batch correction. See harmony::HarmonyMatrix() for more information.

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

A boolean value that indicates whether or not to overwrite data in a given column when the value passed to name already exists as a column name in cellColData.

Additional arguments to be provided to harmony::HarmonyMatrix

An ArchRProject object.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject.

A vector containing the dimensions from the reducedDims object to use.

A boolean that indicates whether to z-score the reduced dimensions for each cell. This is useful forminimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded.

The diffusion time parameter determines the number of smoothing iterations to be performed (see MAGIC from van Dijk et al Cell 2018).

The k-nearest neighbors autotune parameter to equalize the effective number of neighbors for each cell, thereby diminishing the effect of differences in density. (see MAGIC from van Dijk et al Cell 2018).

The number of cells to sub-sample to compute an imputation block. An imputation block is a cell x cell matrix that describes the linear combination for imputation for numerical values within these cells. ArchR creates many blocks to keep this cell x cell matrix sparse for memory concerns.

An integer representing the number of imputation replicates to create when downsampling extremely low.

The number of nearest neighbors for smoothing to use for MAGIC (see MAGIC from van Dijk et al Cell 2018).

The value for the standard deviation of the kernel for MAGIC (see MAGIC from van Dijk et al Cell 2018).

A boolean value that indicates whether HDF5 format should be used to store the impute weights.

A boolean value that indicates whether a random suffix should be appended to the saved imputation weights hdf5 files.

The number of threads to be used for parallel computing.

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the data matrix to retrieve from the ArrowFiles associated with the ArchRProject. Valid options are "TileMatrix" or "PeakMatrix".

The name to use for storage of the IterativeLSI dimensionality reduction in the ArchRProject as a reducedDims object.

The number of LSI iterations to perform.

A list of additional parameters to be passed to addClusters() for clustering within each iteration. These params can be constant across each iteration, or specified for each iteration individually. Thus each param must be of length == 1 or the total number of iterations - 1. If you want to use scran for clustering, you would pass this as method="scran".

First iteration selection method for features to use for LSI. Either "Top" for the top accessible/average or "Var" for the top variable features. "Top" should be used for all scATAC-seq data (binary) while "Var" should be used for all scRNA/other-seq data types (non-binary).

A column in the ArchRProject that represents the coverage (scATAC = unique fragments, scRNA = unique molecular identifiers) per cell. These values are used to minimize the related biases in the reduction related. For scATAC we recommend "nFrags" and for scRNA we recommend "Gex_nUMI".

The number of N variable features to use for LSI. The top N features will be used based on the selectionMethod.

A vector containing the dimensions from the reducedDims object to use in clustering.

A number or string indicating the order of operations in the TF-IDF normalization. Possible values are: 1 or "tf-logidf", 2 or "log(tf-idf)", and 3 or "logtf-logidf".

A boolean that indicates whether to z-score the reduced dimensions for each cell. This is useful forminimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

A boolean value indicating whether the matrix should be binarized before running LSI. This is often desired when working with insertion counts.

Two numerical values (between 0 and 1) that describe the lower and upper quantiles of bias (number of acessible regions per cell, determined by nFrags or colSums) to filter cells prior to LSI. For example a value of c(0.02, 0.98) results in the cells in the bottom 2 percent and upper 98 percent to be filtered prior to LSI. These cells are then projected back in the LSI subspace. This prevents spurious 'islands' that are identified based on being extremely biased. These quantiles are also used for sub-sampled LSI when determining which cells are used.

A boolean indicating whether to drop bias clusters when computing clusters during iterativeLSI.

An integer specifying the number of cells to sample in iterations prior to the last in order to perform a sub-sampled LSI and sub-sampled clustering. This greatly reduced memory usage and increases speed for early iterations.

A boolean indicating whether to reproject all cells into the sub-sampled LSI (see sampleCellsPre). Setting this to FALSE allows for using the sub-sampled LSI for clustering and variance identification. If TRUE the cells are all projected into the sub-sampled LSI and used for cluster and variance identification.

An integer specifying the number of cells to sample in order to perform a sub-sampled LSI in final iteration.

The selection method to be used for identifying the top variable features. Valid options are "var" for log-variability or "vmr" for variance-to-mean ratio.

Each column in the matrix designated by useMatrix will be normalized to a column sum designated by scaleTo prior to variance calculation and TF-IDF normalization.

The number of features to consider for use in LSI after ranking the features by the total number of insertions. These features are the only ones used throught the variance identification and LSI. These are an equivalent when using a TileMatrix to a defined peakSet.

A number 0,1 that indicates the quantile above which features should be removed based on insertion counts prior to the first iteration of the iterative LSI paradigm. For example, if filterQuantile = 0.99, any features above the 99th percentile in insertion counts will be ignored for the first LSI iteration.

A string of chromosomes to exclude for iterativeLSI procedure.

A boolean value indicating whether the results of each LSI iterations should be saved as compressed .rds files in the designated outDir.

The list of parameters to pass to the UMAP function if "UMAP" if saveIterations=TRUE. See the function uwot::umap().

If saveIterations=TRUE, how many cells to sample make a UMAP and plot for each iteration.

The output directory for saving LSI iterations if desired. Default is in the outputDirectory of the ArchRProject.

The number of threads to be used for parallel computing.

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A boolean value that determines whether standard output includes verbose sections.

A boolean value that indicates whether or not to overwrite relevant data in the ArchRProject object.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the matrix to be used for calculation of the module score. See getAvailableMatrices() to view available options.

The name to be given to the designated module. If features is a list, this name will be prepended to the feature set names given in the list as shown below.

A list of feature names to be grouped into modules. For example, list(BScore = c("MS4A1", "CD79A", "CD74"), TScore = c("CD3D", "CD8A", "GZMB", "CCR7", "LEF1")). Each named element in this list will be stored as a separate module. The examples given in these parameters would yield two modules called Module.Bscore and Module.Tscore. If the elements of this list are not named, they will be numbered in order, i.e. Module1, Module2.

The number of bins to use to divide all features for identification of signal-matched features for background calculation

The number of background features to use for signal normalization.

A number to be used as the seed for random number generation required when sampling cells for the background set. It is recommended to keep track of the seed used so that you can reproduce results downstream.

The number of threads to be used for parallel computing.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

A string indicating the name of the fitted trajectory to be added in cellColData.

The cell groups to be used for creating trajectory analysis.

A string indicating the column name from cellColData that contains the cell group definitions used in useGroups to constrain trajectory analysis.

A monocle CDS object created from getMonocleTrajectories.

A boolean value indicating whether to force the trajactory indicated by name to be overwritten if it already exists in the given ArchRProject.

An ArchRProject object.

The motif set to be used for annotation. Options include: (i) "JASPAR2016", "JASPAR2018", "JASPAR2020" which gives the 2016, 2018 or 2020 version of JASPAR motifs, (ii) one of "cisbp", "encode", or "homer" which gives the corresponding motif sets from the chromVAR package, or (iii) "vierstra" which gives the clustered archetype motifs created by Jeff Vierstra (https://github.com/jvierstra/motif-clustering).

The name of the peakAnnotation object to be stored in the provided ArchRProject

The name of the species relevant to the supplied ArchRProject. This is used for identifying which motif to be used from CisBP/JASPAR. By default, this function will attempt to guess the species based on the value from getGenome().

If one of the JASPAR motif sets is used via motifSet, this parameter allows you to indicate the JASPAR collection to be used. See getMatrixSet() from TFBSTools for all options to supply for collection. If motifSet is "vierstra", then this must either be "archetype" (for the v2.1 clustered models) or "individual" (for the original v1 individual motif models). NOTE: vierstra archetype motifs are currently in beta and have not been finalized by Jeff Vierstra.

A custom set of motif PWMs as a PWMList for adding motif annotations.

The p-value cutoff to be used for motif search. The p-value is determined vs a background set of sequences (see MOODS for more details on this determination).

The width in basepairs to consider for motif matches. See the motimatchr package for more information.

An integer specifying version 1 or version 2 of chromVARmotifs see github for more info GreenleafLab/chromVARmotifs.

A boolean value indicating whether to force the peakAnnotation object indicated by annoName to be overwritten if it already exists in the given ArchRProject.

The path to a file to be used for logging ArchR output.

Additional parameters to be passed to TFBSTools::getMatrixSet for getting a PWM object.

An ArchRProject object.

The name of the reducedDims object (i.e. "IterativeLSI") to retrieve from the designated ArchRProject.

The name of the matrix containing gene expression information to be used for determining peak-to-gene links. See getAvailableMatrices(ArchRProj)

A vector containing the dimensions from the reducedDims object to use in clustering.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

A character vector of cellNames to compute coAccessibility on if desired to run on a subset of the total cells.

The number of k-nearest neighbors to use for creating single-cell groups for correlation analyses.

The number of k-nearest neighbor groupings to test for passing the supplied overlapCutoff.

The maximum allowable overlap between the current group and all previous groups to permit the current group be added to the group list during k-nearest neighbor calculations.

The maximum allowable distance in basepairs between two peaks to consider for co-accessibility.

The total insertion counts from the designated group of single cells is summed across all relevant peak regions from the peakSet of the ArchRProject and normalized to the total depth provided by scaleTo.

A boolean value indicating whether to log2 transform the single-cell groups prior to computing co-accessibility correlations.

A numeric describing the cutoff for RNA integration to use when picking cells for groupings.

Add empirical p-values based on randomly correlating peaks and genes not on the same seqname.

A number to be used as the seed for random number generation required in knn determination. It is recommended to keep track of the seed used so that you can reproduce results downstream.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output should be printed.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

A named list of GRanges that are to be overlapped with the peakSet in the ArchRProject.

The name of peakAnnotation object to be stored as in ArchRProject.

A boolean value indicating whether to force the peakAnnotation object indicated by name to be overwritten if it already exists in the given ArchRProject.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The maximum counts per feature allowed. This is used to prevent large biases in peak counts.

A boolean value indicating whether the peak matrix should be binarized prior to storage. This can be useful for downstream analyses when working with insertion counts.

A boolean value that determines whether standard output includes verbose sections.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether to force the "PeakMatrix" to be overwritten if it already exist in the given ArchRProject.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

A GRanges object containing the set of regions that define all peaks in the desired peak set.

The genomeAnnotation (see createGenomeAnnotation()) to be used for generating peak metadata such as nucleotide information (GC content) or chromosome sizes.

If a peakSet object has already been added to the given ArchRProject, the value of force determines whether or not to overwrite this peakSet.

An ArchRProject object.

The name of the summary information to add to the ArchRProject object.

A vector to add as summary information to the ArchRProject object.

An ArchRProject object.

The name of the column in cellColData to use for grouping cells together for peak calling.

The name of peak calling method to be used. Options include "Macs2" for using macs2 callpeak or "Tiles" for using a TileMatrix.

A string that indicates how peak reproducibility should be handled. This string is dynamic and can be a function of n where n is the number of samples being assessed. For example, reproducibility = "2" means at least 2 samples must have a peak call at this locus and reproducibility = "(n+1)/2" means that the majority of samples must have a peak call at this locus.

The upper limit of the number of peaks that can be identified per cell-grouping in groupBy. This is useful for controlling how many peaks can be called from cell groups with low cell numbers.

A numeric threshold for the maximum peaks to retain per group from groupBy in the union reproducible peak set.

The minimum allowable number of unique cells that was used to create the coverage files on which peaks are called. This is important to allow for exclusion of pseudo-bulk replicates derived from very low cell numbers.

A character vector containing the seqnames of the chromosomes that should be excluded from peak calling.

The full path to the MACS2 executable.

The genome size to be used for MACS2 peak calling (see MACS2 documentation). This is required if genome is not hg19, hg38, mm9, or mm10.

The number of basepairs to shift each Tn5 insertion. When combined with extsize this allows you to create proper fragments, centered at the Tn5 insertion site, for use with MACS2 (see MACS2 documentation).

The number of basepairs to extend the MACS2 fragment after shift has been applied. When combined with extsize this allows you to create proper fragments, centered at the Tn5 insertion site, for use with MACS2 (see MACS2 documentation).

The method to use for significance testing in MACS2. Options are "p" for p-value and "q" for q-value. When combined with cutOff this gives the method and significance threshold for peak calling (see MACS2 documentation).

The numeric significance cutOff for the testing method indicated by method (see MACS2 documentation).

A string of additional parameters to pass to MACS2 (see MACS2 documentation).

The number of basepairs to extend peak summits (in both directions) to obtain final fixed-width peaks. For example, extendSummits = 250 will create 501-bp fixed-width peaks from the 1-bp summits.

A vector of two integers specifying the distance in basepairs upstream and downstream of a TSS to be included as a promoter region. Peaks called within one of these regions will be annotated as a "promoter" peak. For example, promoterRegion = c(2000, 100) will annotate any peak within the region 2000 bp upstream and 100 bp downstream of a TSS as a "promoter" peak.

The genomeAnnotation (see createGenomeAnnotation()) to be used for generating peak metadata such as nucleotide information (GC content) or chromosome sizes.

The geneAnnotation (see createGeneAnnotation()) to be used for labeling peaks as "promoter", "exonic", etc.

A boolean describing whether to plot peak annotation results.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether to force the reproducible peak set to be overwritten if it already exist in the given ArchRProject peakSet.

A boolean value that determines whether standard output includes verbose sections.

The path to a file to be used for logging ArchR output.

Additional parameters to be pass to addGroupCoverages() to get sample-guided pseudobulk cell-groupings. Only used for TileMatrix-based peak calling (not for MACS2). See addGroupCoverages() for more info.

An ArchRProject object.

A vector containing the data to be added to sampleColData.

The column header name to be used for this new data in sampleColData. If a column with this name already exists, you may set force equal to TRUE to overwrite the data in this column.

The names of the samples corresponding to data. Typically new data is added to all samples but you may use this argument to only add data to a subset of samples. Samples where data is not added are set to NA.

A boolean value that indicates whether or not to overwrite data in a given column when the value passed to name already exists as a column name in sampleColData.

An ArchRProject object.

A string indicating the name of the fitted trajectory to be added in cellColData.

A character vector that is used to select a subset of groups by name from the designated groupBy column in cellColData. This limits the groups used to identify trajectories.

The principal group which represents the group that will be the starting point for all trajectories.

A string indicating the column name from cellColData that contains the cell group definitions used in useGroups to constrain trajectory analysis.

A string indicating the name of the embedding object from the ArchRProject that should be used for trajectory analysis.

A string indicating the name of the reducedDims object from the ArchRProject that should be used for trajectory analysis. embedding must equal NULL to use.

A boolean value indicating whether to force the trajactory indicated by name to be overwritten if it already exists in the given ArchRProject.

A number to be used as the seed for random number generation for trajectory creation.

An ArchRProject object or character vector of ArrowFiles.

A named numeric vector containing the chromsome names and lengths. The default behavior is to retrieve this from the ArchRProject using getChromSizes().

A GRanges object containing genomic regions to blacklist counting in these tiles. The default behavior is to retrieve this from the ArchRProject using getBlacklist().

The size of the tiles used for binning counts in the "TileMatrix".

A boolean value indicating whether the "TileMatrix" should be binarized prior to storage.

A character vector containing the seqnames of the chromosomes that should be excluded from the "TileMatrix".

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean value indicating whether to force the "TileMatrix' to be overwritten if it already exist in the given input.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

A string indicating the name of the fitted trajectory to be added in cellColData.

The order of cell groups to be used for constraining the initial supervised fitting procedure. For example, to get a trajectory from Cluster1 to Cluster2 to Cluster3, input should be c("Cluster1", "Cluster2", "Cluster3"). Cells will then be used from these 3 groups to constrain an initial fit in the group order.

A string indicating the column name from cellColData that contains the cell group definitions used in trajectory to constrain the initial supervised fitting procedure.

A string indicating the name of the reducedDims object from the ArchRProject that should be used for distance computation.

A string indicating the name of the embedding object from the ArchRProject that should be used for distance computation.

Prior to the initial supervised trajectory fitting, cells whose euclidean distance from the cell-grouping center is above the provided quantile will be excluded.

After initial supervised trajectory fitting, cells whose euclidean distance from the cell-grouping center is above the provided quantile will be excluded.

A boolean describing whether to use cells outside of trajectory groups for post-fitting procedure.

The number of degrees of freedom to be used in the spline fit. See stats::smooth.spline() for more information.

The sparsity to be used in the spline fit. See stats::smooth.spline() for more information.

A boolean value indicating whether to force the trajactory indicated by name to be overwritten if it already exists in the given ArchRProject.

A number to be used as the seed for random number generation for trajectory creation.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the reducedDims object (i.e. "IterativeLSI") to use from the designated ArchRProject.

The method for computing a TSNE embedding to add to the ArchRProject object. Possible options are "RTSNE", which uses Rtsne::Rtsne(), and "FFRTSNE", which uses Seurat::RunTSNE().

The name for the TSNE embedding to store in the given ArchRProject object.

An integer describing the number of nearest neighbors to compute an Rtsne. This argument is passed to perplexity in Rtsne::Rtsne().

An integer describing the maximum number of iterations when computing a TSNE. This argument is passed to max_iter in Rtsne::Rtsne().

An integer controlling how much the weights are adjusted at each iteration. This argument is passed to eta in Rtsne::Rtsne().

A vector containing the dimensions from the reducedDims object to use in computing the embedding.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

A boolean value that indicates whether printing TSNE output.

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A boolean value that indicates whether to overwrite the relevant data in the ArchRProject object if the embedding indicated by name already exists.

The number of threads to be used for parallel computing.

Additional parameters for computing the TSNE embedding to pass to Rtsne::Rtsne() (when method = "RTSNE") or to Seurat::RunTSNE() (when method = "FFRTSNE").

An ArchRProject object.

The name of the reducedDims object (i.e. "IterativeLSI") to use from the designated ArchRProject.

The name for the UMAP embedding to store in the given ArchRProject object.

An integer describing the number of nearest neighbors to compute a UMAP. This argument is passed to n_neighbors in uwot::umap().

A number that determines how tightly the UMAP is allowed to pack points together. This argument is passed to min_dist in uwot::umap(). For more info on this see https://jlmelville.github.io/uwot/abparams.html.

A number that determines how distance is computed in the reducedDims to compute a UMAP. This argument is passed to metric in uwot::umap().

A vector containing the dimensions from the reducedDims object to use in computing the embedding.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

An integer specifying the number of cells to subsample and perform UMAP Embedding on. The remaining cells that were not subsampled will be re-projected using uwot::umap_transform to the UMAP Embedding. This enables a decrease in run time and memory but can lower the overal quality of the UMAP Embedding. Only recommended for extremely large number of cells.

A numeric (0 to 1) describing the distance quantile in the subsampled cels (see sampleCells) to use to filter poor quality re-projections. This is necessary because there are lots of outliers if undersampled significantly.

A boolean value indicating whether or not to save the UMAP model in an RDS file for downstream usage such as projection of data into the UMAP embedding.

A boolean value that indicates whether printing UMAP output.

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A boolean value that indicates whether to overwrite the relevant data in the ArchRProject object if the embedding indicated by name already exists.

The number of threads to be used for parallel computing. Default set to 1 because if set to high can cause C stack usage errors.

Additional parameters to pass to uwot::umap()

An ArchRProject object.

A GRanges object containing the "features" to be plotted via the "featureTrack". This should be thought of as a bed track. i.e. the set of peaks obtained using ⁠getPeakSet(ArchRProj))⁠.

A GRanges object containing the "loops" to be plotted via the "loopTrack". This GRanges object start represents the center position of one loop anchor and the end represents the center position of another loop anchor. A "loopTrack" draws an arc between two genomic regions that show some type of interaction. This type of track can be used to display chromosome conformation capture data or co-accessibility links obtained using getCoAccessibility().

The minimum number of cells contained within a cell group to allow for this cell group to be plotted. This argument can be used to exclude pseudo-bulk replicates generated from low numbers of cells.

The numeric font size to be used in the plot. This applies to all plot labels.

The numeric line width to be used for plot borders.

The numeric line width to be used for axis tick marks.

The numeric font size to be used in the facets (gray boxes used to provide track labels) of the plot.

The geneAnnotation object to be used for plotting the "geneTrack" object. See createGeneAnnotation() for more info.

A shinytheme from shinythemes for viewing the ArchR Browser. If not installed this will be NULL. To install try devtools::install_github("rstudio/shinythemes").

The number of threads to use for parallel execution.

A boolean value that determines whether standard output should be printed.

The path to a file to be used for logging ArchR output.

A character vector containing the relative paths to the ArrowFiles to be used.

A name for the relative path of the outputDirectory for ArchR results. Relative to the current working directory.

A boolean value indicating whether ArrowFiles should be copied into outputDirectory.

The geneAnnotation object (see createGeneAnnotation()) to be used for downstream analyses such as calculating TSS Enrichment Scores, Gene Scores, etc.

The genomeAnnotation object (see createGenomeAnnotation()) to be used for downstream analyses requiring genome information such as nucleotide information or chromosome sizes.

A boolean value indicating whether to show the ascii ArchR logo after successful creation of an ArchRProject.

The number of threads to use for parallel execution.

A character/numeric value vector to see concordance with j.

A character/numeric value vector to see concordance with i.

An ArchRProject object.

A character describing the first matrix to use. See getAvailableMatrices for valid options.

A character describing the second matrix to use. See getAvailableMatrices for valid options.

A character vector describing which seqnames to use in matrix 1.

A character vector describing which seqnames to use in matrix 2.

A character vector describing how to filter names in matrix 1. Options include "underscore", "dash", "numeric" and "dot". The string portion prior to these will be kept.

A character vector describing how to filter names in matrix 2. Options include "underscore", "dash", "numeric" and "dot". The string portion prior to these will be kept.

A boolean describing whether to log2 normalize matrix 1.

A boolean describing whether to log2 normalize matrix 2.

The name of the reducedDims object (i.e. "IterativeLSI") to use from the designated ArchRProject.

A vector containing the dimensions from the reducedDims object to use in computing the embedding.

A boolean value that indicates whether to z-score the reduced dimensions for each cell. This is useful for minimizing the contribution of strong biases (dominating early PCs) and lowly abundant populations. However, this may lead to stronger sample-specific biases since it is over-weighting latent PCs. If set to NULL this will scale the dimensions based on the value of scaleDims when the reducedDims were originally created during dimensionality reduction. This idea was introduced by Timothy Stuart.

A numeric cutoff for the correlation of each dimension to the sequencing depth. If the dimension has a correlation to sequencing depth that is greater than the corCutOff, it will be excluded from analysis.

The number of k-nearest neighbors to use for creating single-cell groups for correlation analyses.

The number of k-nearest neighbor groupings to test for passing the supplied overlapCutoff.

The maximum allowable overlap between the current group and all previous groups to permit the current group be added to the group list during k-nearest neighbor calculations.

A number to be used as the seed for random number generation required in knn determination. It is recommended to keep track of the seed used so that you can reproduce results downstream.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output should be printed.

The path to a file to be used for logging ArchR output.

A SummarizedExperiment object that results from calling getTrajectory().

A SummarizedExperiment object that results from calling getTrajectory().

A numeric describing the cutoff for determining correlated features.

The "Variance Quantile Cutoff" to be used for identifying the top variable features across seTrajectory1. Only features with a variance above the provided quantile will be retained.

The "Variance Quantile Cutoff" to be used for identifying the top variable features across seTrajectory2. Only features with a variance above the provided quantile will be retained.

A character vector describing how to filter names in matrix 1. Options include "underscore", "dash", "numeric" and "dot". The string portion prior to these will be kept.

A character vector describing how to filter names in matrix 2. Options include "underscore", "dash", "numeric" and "dot". The string portion prior to these will be kept.

A boolean describing whether to use range overlap matching for correlation analysis.

A character describing where to resize the coordinates of seTrajectory1. Options include "start", "center", "end".

A character describing where to resize the coordinates of seTrajectory2. Options include "start", "center", "end".

A integer specifying the maximum distance between the coordinates of seTrajectory1 and seTrajectory2 for computing correlations.

A boolean describing whether to log2 normalize seTrajectory1.

A boolean describing whether to log2 normalize seTrajectory2.

A boolean value that determines whether analysis should continue if resizing coordinates in seTrajectory1 or seTrajectory2 does not align with the strandedness. Only when useRanges = TRUE.

The path to a file to be used for logging ArchR output.

A character vector containing the paths to the input files to use to generate the ArrowFiles. These files can be in one of the following formats: (i) scATAC tabix files, (ii) fragment files, or (iii) bam files.

A character vector containing the names to assign to the samples that correspond to the inputFiles. Each input file should receive a unique sample name. This list should be in the same order as inputFiles.

The prefix to use for output files. Each input file should receive a unique output file name. This list should be in the same order as "inputFiles". For example, if the predix is "PBMC" the output file will be named "PBMC.arrow"

A list of valid barcode strings to be used for filtering cells read from each input file (see getValidBarcodes() for 10x fragment files).

The geneAnnotation (see createGeneAnnotation()) to associate with the ArrowFiles. This is used downstream to calculate TSS Enrichment Scores etc.

The genomeAnnotation (see createGenomeAnnotation()) to associate with the ArrowFiles. This is used downstream to collect chromosome sizes and nucleotide information etc.

The minimum numeric transcription start site (TSS) enrichment score required for a cell to pass filtering for use in downstream analyses. Cells with a TSS enrichment score greater than or equal to minTSS will be retained. TSS enrichment score is a measurement of signal-to-background in ATAC-seq.

The minimum number of mapped ATAC-seq fragments required per cell to pass filtering for use in downstream analyses. Cells containing greater than or equal to minFrags total fragments wll be retained.

The maximum number of mapped ATAC-seq fragments required per cell to pass filtering for use in downstream analyses. Cells containing greater than or equal to maxFrags total fragments wll be retained.

The minimum fragment size to be included into Arrow File. Fragments lower than this number are discarded. Must be less than maxFragSize.

The maximum fragment size to be included into Arrow File. Fragments above than this number are discarded. Must be greater than maxFragSize.

The relative path to the output directory for QC-level information and plots for each sample/ArrowFile.

The length in basepairs that wraps around a nucleosome. This number is used for identifying fragments as sub-nucleosome-spanning, mono-nucleosome-spanning, or multi-nucleosome-spanning.

A integer vector describing the number of basepairs upstream and downstream c(upstream, downstream) of the TSS to include as the promoter region for downstream calculation of things like the fraction of reads in promoters (FIP).

A list of parameters for computing TSS Enrichment scores. This includes the window which is the size in basepairs of the window centered at each TSS (default 101), the flank which is the size in basepairs of the flanking window (default 2000), and the norm which describes the size in basepairs of the flank window to be used for normalization of the TSS enrichment score (default 100). For example, given ⁠window = 101, flank = 2000, norm = 100⁠, the accessibility within the 101-bp surrounding the TSS will be normalized to the accessibility in the 100-bp bins from -2000 bp to -1901 bp and 1901:2000.

A character vector containing the names of chromosomes to be excluded from downstream analyses. In most human/mouse analyses, this includes the mitochondrial DNA (chrM) and the male sex chromosome (chrY). This does, however, not exclude the corresponding fragments from being stored in the ArrowFile.

The number of chunks to divide each chromosome into to allow for low-memory parallelized reading of the inputFiles. Higher numbers reduce memory usage but increase compute time.

The name of the field in the input bam file containing the barcode tag information. See ScanBam in Rsamtools.

A regular expression used to clean up the barcode tag string read in from a bam file. For example, if the barcode is appended to the readname or qname field like for the mouse atlas data from Cusanovic* and Hill* et al. (2018), the gsubExpression would be ":.*". This would retrieve the string after the colon as the barcode.

A vector of bam flags to be used for reading in fragments from input bam files. Should be in the format of a scanBamFlag passed to ScanBam in Rsamtools.

The numeric offset to apply to a "+" stranded Tn5 insertion to account for the precise Tn5 binding site. This parameter only applies to bam file input and it is assumed that fragment files have already been offset which is the standard from 10x output. See Buenrostro et al. Nature Methods 2013.

The numeric offset to apply to a "-" stranded Tn5 insertion to account for the precise Tn5 binding site. This parameter only applies to bam file input and it is assumed that fragment files have already been offset which is the standard from 10x output. See Buenrostro et al. Nature Methods 2013.

A boolean value indicating whether to add a "Tile Matrix" to each ArrowFile. A Tile Matrix is a counts matrix that, instead of using peaks, uses a fixed-width sliding window of bins across the whole genome. This matrix can be used in many downstream ArchR operations.

A list of parameters to pass to the addTileMatrix() function. See addTileMatrix() for options.

A boolean value indicating whether to add a Gene-Score Matrix to each ArrowFile. A Gene-Score Matrix uses ATAC-seq signal proximal to the TSS to estimate gene activity.

A list of parameters to pass to the addGeneScoreMatrix() function. See addGeneScoreMatrix() for options.

A boolean value indicating whether to force ArrowFiles to be overwritten if they already exist.

The number of threads to be used for parallel computing.

A list of parameters to be passed for biocparallel/batchtools parallel computing.

A boolean determining whether possible use threads within each multi-threaded subprocess if greater than the number of input samples.

A boolean value that determines whether standard output should be printed.

The path to a file to be used for logging ArchR output.

A boolean value that determines whether to clean temp folder of all intermediate ".arrow" files.

A string that specifies the genome (ie "hg38", "hg19", "mm10", "mm9"). If genome is not supplied, TxDb and OrgDb are required. If genome is supplied, TxDb and OrgDb will be ignored.

A TxDb object (transcript database) from Bioconductor which contains information for gene/transcript coordinates. For example, from txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene.

An OrgDb object (organism database) from Bioconductor which contains information for gene/transcript symbols from ids. For example, from orgdb <- org.Hs.eg.db.

A GRanges object containing gene coordinates (start to end). Must have a symbols column matching the symbols column of exons.

A GRanges object containing gene exon coordinates. Must have a symbols column matching the symbols column of genes.

A GRanges object containing standed transcription start site coordinates for computing TSS enrichment scores downstream.

annotation style to map between gene names and various gene identifiers e.g. "ENTREZID", "ENSEMBL".

Either (i) a string that is a valid BSgenome or (ii) a BSgenome object (ie "hg38" or "BSgenome.Hsapiens.UCSC.hg38").

A GRanges object containing chromosome start and end coordinates.

A GRanges object containing regions that should be excluded from analyses due to unwanted biases.

A boolean value indicating whether non-standard chromosome scaffolds should be excluded. These "non-standard" chromosomes are defined by filterChrGR() and by manual annotation using the filterChr parameter.

A character vector indicating the seqlevels that should be removed if manual removal is desired for certain seqlevels. If no manual removal is desired, filterChr should be set to NULL. If filter is set to TRUE but filterChr is set to NULL, non-standard chromosomes will still be removed as defined in filterChrGR().

A character string to add a more descriptive name in log file.

The path to a directory where log files should be written.

An ArchRProject object to get data matrix from.

A character vector of chromosome names to be used to subset the data matrix being obtained.

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

A boolean value indicating whether the matrix should be binarized before return. This is often desired when working with insertion counts.

The path to a file to be used for logging ArchR output.

A GRanges object.

The number of basepairs upstream (5') to extend each region in gr in a strand-aware fashion.

The number of basepairs downstream (3') to extend each region in gr in a strand-aware fashion.

A GRanges object or another object containing seqlevels.

A character vector indicating the seqlevels that should be removed if manual removal is desired for certain seqlevels. If no manual removal is desired, remove should be set to NULL.

A boolean value indicating whether to remove all seqlevels whose names contain an underscore (for example "chr11_KI270721v1_random").

A boolean value indicating whether only standard chromosomes should be kept. Standard chromosomes are defined by GenomeInfoDb::keepStandardChromosomes().

The name of the pruning method to use (fromGenomeInfoDb::seqinfo()) when seqlevels must be removed from a GRanges object. When some of the seqlevels to drop from the given GRanges object are in use (i.e. have ranges on them), the ranges on these sequences need to be removed before the seqlevels can be dropped. Four pruning modes are currently defined: "error", "coarse", "fine", and "tidy".

An ArchRProject object.

The minimum numeric cutoff for DoubletEnrichment. This number is equivalent to the number of simulated doublets identified as a nearest neighbor to the cell divided by the expected number given a random uniform distribution.

The minimum numeric cutoff for DoubletScore which represents the ⁠-log10(binomial adjusted p-value)⁠ for the DoubletEnrichment.

The maximum ratio of predicted doublets to filter based on the number of pass-filter cells. For example, if there are 5000 cells, the maximum would be filterRatio * 5000^2 / (100000) (which simplifies to filterRatio * 5000 * 0.05). This filterRatio allows you to apply a consistent filter across multiple different samples that may have different percentages of doublets because they were run with different cell loading concentrations. The higher the filterRatio, the greater the number of cells potentially removed as doublets.

A boolean value indicating whether the geneAnnotation associated with the ArchRGenome should be returned instead of the globally defined genome. The geneAnnotation is used downstream to calculate things like TSS Enrichment Scores. This function is not meant to be run with both geneAnnotation and genomeAnnotation set to TRUE (it is an either/or return value).

A boolean value indicating whether the genomeAnnotation associated with the ArchRGenome should be returned instead of the globally defined genome. The genomeAnnotation is used downstream to determine things like chromosome sizes and nucleotide content. This function is not meant to be run with both geneAnnotation and genomeAnnotation set to TRUE (it is an either/or return value).

An ArchRProject object.

An ArchRProject object.

An ArchRProject object.

The number of background peaks to sample. See chromVAR::getBackgroundPeaks().

The parameter controlling similarity measure of background peaks. See chromVAR::getBackgroundPeaks().

The precision with which the similarity is computed. See chromVAR::getBackgroundPeaks().

A number to be used as the seed for random number generation. It is recommended to keep track of the seed used so that you can reproduce results downstream.

A string indicating whether to use chromVAR or ArchR for background peak identification.

A boolean value indicating whether to force the file indicated by outFile to be overwritten if it already exists.

An ArchRProject object.

An ArchRProject object.

A character vector of column names to select from cellColData if you would like to subset the returned data.

A boolean value that indicates whether to drop the dataframe structure and convert to a vector if selecting only one column.

An ArchRProject object.

An ArchRProject object.

An ArchRProject object.

An ArchRProject object.

A numeric describing the minimum numeric peak-to-peak correlation to return.

A numeric describing the bp resolution to use when returning loops. This helps with overplotting of correlated regions. This only takes affect if returnLoops = TRUE.

A boolean indicating to return the co-accessibility signal as a GRanges "loops" object designed for use with the ArchRBrowser() or as an ArchRBrowserTrack().

An ArchRProject object.

The name of the embeddings object (i.e. UMAP, TSNE see embeddingOut of the addEmbeddings() function) to retrieve from the designated ArchRProject.

A boolean value indicating whether to return the embedding object as a data.frame. Otherwise, it will return the full embedding object.

An ArchRProject object.

A character vector containing the gene symbols for the genes where exons should be extracted.

An ArchRProject object.

The name of the data matrix as stored in the ArrowFiles of the ArchRProject. Options include "TileMatrix", "GeneScoreMatrix", etc.

A string specifying a specific feature name (or rowname) to be found with grep.

A boolean value indicating whether to ignore the case (upper-case / lower-case) when searching via grep for the string passed to select.

An ArchRProject object.

A list or GRangesList of GRanges containing the positions to incorporate into the footprint. Each position should be stranded.

The prefix to add to the file name for the output PDF file containing the footprint plots.

The name of the column in cellColData used in the addGroupCoverages() function for grouping multiple cells together.

A character vector that is used to select a subset of groups by name from the designated groupBy column in cellColData. This limits the groups used to perform footprinting.

The number of basepairs from the position center (+/-) to consider as the flank.

The minimum number of cells required in a given cell group to permit footprint generation.

The number of genomic regions to consider. Only the top nTop genomic regions based on the "score" column in the GRanges object will be considered for the footprint.

The number of threads to be used for parallel computing.

A boolean value that determines whether standard output includes verbose sections.

The path to a file to be used for logging ArchR output.

The path to the ArrowFile from which fragments should be obtained.

A name of a chromosome to be used to subset the fragments GRanges object to a specific chromsome if desired.

A character vector indicating the cell names of a subset of cells from which fragments whould be extracted. This allows for extraction of fragments from only a subset of selected cells. By default, this function will extract all cells from the provided ArrowFile using getCellNames().

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

The path to a file to be used for logging ArchR output.

A Genomic Ranges object to subset fragments by.

A character vector indicating the cell names of a subset of cells from which fragments whould be extracted. This allows for extraction of fragments from only a subset of selected cells. By default, this function will extract all cells from the provided ArrowFile using getCellNames().

A boolean value indicating whether to use verbose output during execution of this function. Can be set to FALSE for a cleaner output.

The path to a file to be used for logging ArchR output.

An ArchRProject object to get fragments from.

An ArchRProject object.

An ArchRProject object.

A character vector containing the gene symbols to subset from the geneAnnotation.

An ArchRProject object.

An ArchRProject object.

An ArchRProject object.

A string that indicates how cells should be grouped. This string corresponds to one of the standard or user-supplied cellColData metadata columns (for example, "Clusters"). Cells with the same value annotated in this metadata column will be grouped together and the average signal will be plotted.

The name of the column in cellColData by which normalization should be performed. The recommended and default value is "ReadsInTSS" which simultaneously normalizes tracks based on sequencing depth and sample data quality. Accepted values are "None", "ReadsInTSS", "nCells", "ReadsInPromoter", or "nFrags".

The numeric width of the tile/bin in basepairs for plotting ATAC-seq signal tracks. All insertions in a single bin will be summed.

Maximum number of cells used for each bigwig.

Maximum contribution of accessibility per cell in each tile.

A boolean specifying to print messages during computation.

An integer specifying the number of threads for parallel.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the matrix in the ArrowFiles. See getAvailableMatrices to see options

The name of the column in cellColData to use for grouping cells together for summarizing.

A boolean describing whether to divide by the number of cells.

Depth normalize to this value if not NULL.

An integer specifying the number of threads for parallel.

A boolean specifying to print messages during computation.

The path to a file to be used for logging ArchR output.

An ArchRProject object.

The name of the column in cellColData to use for grouping multiple cells together for summarizing information.

A character vector containing the column names to select from cellColData.

A character vector describing which method for summarizing across group. Options include "median", "mean", or "sum".

Remove NA's from summary method.

An ArchRProject object.

A character vector of paths to search for usable input files.

An ArchRProject object.

The name of the column in cellColData to use for grouping cells together for marker feature identification.

A character vector that is used to select a subset of groups by name from the designated groupBy column in cellColData. This limits the groups used to perform marker feature identification.

A character vector that is used to select a subset of groups by name from the designated groupBy column in cellColData to be used for background calculations in marker feature identification.

The name of the matrix to be used for performing differential analyses. Options include "GeneScoreMatrix", "PeakMatrix", etc.

A character vector indicating the potential bias variables (i.e. c("TSSEnrichment", "log10(nFrags)")) to account for in selecting a matched null group for marker feature identification. These should be column names from cellColData.

The name of a numeric column in cellColData that should be normalized across cells (i.e. "ReadsInTSS") prior to performing marker feature identification.

The name of the pairwise test method to use in comparing cell groupings to the null cell grouping during marker feature identification. Valid options include "wilcoxon", "ttest", and "binomial".

The maximum number of cells to consider from a single-cell group when performing marker feature identification.

Each column in the matrix designated by useMatrix will be normalized to a column sum designated by scaleTo.

The number of threads to be used for parallel computing.

The number of nearby cells to use for selecting a biased-matched background while accounting for bgdGroups proportions.

When generating optimal biased-matched background groups of cells to determine significance, it can be difficult to find sufficient numbers of well-matched cells to create a background group made up of an equal number of cells. The bufferRatio indicates the fraction of the total cells that must be obtained when creating the biased-matched group. For example to create a biased-matched background for a group of 100 cells, when bufferRatio is set to 0.8 the biased-matched background group will be composed of the 80 best-matched cells. This option provides flexibility in the generation of biased-matched background groups given the stringency of also maintaining the group proportions from bgdGroups.

A boolean value indicating whether to binarize the matrix prior to differential testing. This is useful when useMatrix is an insertion counts-based matrix.

A character vector that indicates which seqnames should be plotted in the heatmap. Features from seqnames that are not listed will be ignored. In the context of a Sparse.Assays.Matrix, such as a matrix containing chromVAR deviations, the seqnames do not correspond to chromosomes, rather they correspond to the sub-portions of the matrix, for example raw deviations ("deviations") or deviation z-scores ("z") for a chromVAR deviations matrix.

A boolean value that determines whether standard output is printed.

The path to a file to be used for logging ArchR output.

A SummarizedExperiment object returned by getMarkerFeatures().

A valid-syntax logical statement that defines which marker features from seMarker. cutoff can contain any of the assayNames from seMarker.

An integer that indicates the maximum number of features to return per group.

A boolean indicating whether to return as a GRanges object. Only valid when seMarker is computed for a PeakMatrix.

An ArchRProject object.

The name of the peakAnnotation object (i.e. Motifs) to retrieve from the designated ArchRProject.