Package 'BPCells'

Title: Single Cell Counts Matrices to PCA
Description: > Efficient operations for single cell ATAC-seq fragments and RNA counts matrices. Interoperable with standard file formats, and introduces efficient bit-packed formats that allow large storage savings and increased read speeds.
Authors: Benjamin Parks [aut, cre, cph] , Stanford University [cph, fnd], Genentech, Inc. [cph, fnd]
Maintainer: Benjamin Parks <[email protected]>
License: Apache-2.0 or MIT
Version: 0.2.0
Built: 2024-07-10 22:28:03 UTC
Source: https://github.com/bnprks/BPCells

Help Index


Broadcasting vector arithmetic

Description

Convenience functions for adding or multiplying each row / column of a matrix by a number.

Usage

add_rows(mat, vec)

add_cols(mat, vec)

multiply_rows(mat, vec)

multiply_cols(mat, vec)

Arguments

mat

Matrix-like object

vec

Numeric vector

Value

Matrix-like object


Get/set inputs to a matrix transform

Description

A matrix object can either be an input (i.e. a file on disk or a raw matrix in memory), or it can represent a delayed operation on one or more matrices. The all_matrix_inputs() getter and setter functions allow accessing the base-level input matrices as a list, and changing them. This is useful if you want to re-locate data on disk without losing your transformed BPCells matrix. (Note: experimental API; potentially subject to revisions).

Usage

all_matrix_inputs(x)

all_matrix_inputs(x) <- value

Arguments

x

IterableMatrix

value

List of IterableMatrix objects

Value

List of IterableMatrix objects. If a matrix m is itself an input object, then all_matrix_inputs(m) will return list(m).


Apply a function to summarize rows/cols

Description

Apply a custom R function to each row/col of a BPCells matrix. This will run slower than the builtin C++-backed functions, but will keep most of the memory benefits from disk-backed operations.

Usage

apply_by_row(mat, fun, ...)

apply_by_col(mat, fun, ...)

Arguments

mat

IterableMatrix object

fun

⁠function(val, row, col)⁠ that takes in a row/col of values and returns a summary output. Argument details:

  1. val - Vector length (# non-zero values) with the value for each non-zero matrix entry

  2. row - one-based row index (apply_by_col: vector length (# non-zero values), apply_by_row: single integer)

  3. col - one-based col index (apply_by_col: single integer, apply_by_row: vector length (# non-zero values))

  4. ... - Optional additional arguments (should not be named row, col, or val)

...

Optional additional arguments passed to fun

Details

These functions require row-major matrix storage for apply_by_row and col-major storage for apply_by_col, so matrices stored in the wrong order may neeed a re-ordered copy created using transpose_storage_order() first. This is required to be able to keep memory-usage low and allow calculating the result with a single streaming pass of the input matrix.

If vector/matrix outputs are desired instead of lists, calling unlist(x) or do.call(cbind, x) or do.call(rbind, x) can convert the list output.

Value

apply_by_row - A list of length nrow(matrix) with the results returned by fun() on each row

apply_by_col - A list of length ncol(matrix) with the results returned by fun() on each row

See Also

For an interface more similar to base::apply, see the BPCellsArray project. For calculating colMeans on a sparse single cell RNA matrix it is about 8x slower than apply_by_col, due to the base::apply interface not being sparsity-aware. (See pull request #104 for benchmarking.)


Convert matrix elements to zeros and ones

Description

Binarize compares the matrix element values to the threshold value and sets the output elements to either zero or one. By default, element values greater than the threshold are set to one; otherwise, set to zero. When strict_inequality is set to FALSE, element values greater than or equal to the threshold are set to one. As an alternative, the <, <=, >, and >= operators are also supported.

Usage

binarize(mat, threshold = 0, strict_inequality = TRUE)

Arguments

mat

IterableMatrix

threshold

A numeric value that determines whether the elements of x are set to zero or one.

strict_inequality

A logical value determining whether the comparison to the threshold is >= (strict_inequality=FALSE) or > (strict_inequality=TRUE).

Value

binarized IterableMatrix object


Call peaks from tiles

Description

Calling peaks from a pre-set list of tiles can be much faster than using dedicated peak-calling software like macs3. The resulting peaks are less precise in terms of exact coordinates, but should be sufficient for most analyses.

Usage

call_peaks_tile(
  fragments,
  chromosome_sizes,
  cell_groups = rep.int("all", length(cellNames(fragments))),
  effective_genome_size = NULL,
  peak_width = 200,
  peak_tiling = 3,
  fdr_cutoff = 0.01,
  merge_peaks = c("all", "group", "none")
)

Arguments

fragments

IterableFragments object

chromosome_sizes

Chromosome start and end coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

See read_ucsc_chrom_sizes().

cell_groups

Grouping vector with one entry per cell in fragments, e.g. cluster IDs

effective_genome_size

(Optional) effective genome size for poisson background rate estimation. See deeptools for values for common genomes. Defaults to sum of chromosome sizes, which overestimates peak significance

peak_width

Width of candidate peaks

peak_tiling

Number of candidate peaks overlapping each base of genome. E.g. peak_width = 300 and peak_tiling = 3 results in candidate peaks of 300bp spaced 100bp apart

fdr_cutoff

Adjusted p-value significance cutoff

merge_peaks

How to merge significant peaks with merge_peaks_iterative()

  • "all" Merge the full set of peaks

  • "group" Merge peaks within each group

  • "none" Don't perform any merging

Details

Peak calling steps:

  1. Estimate the genome-wide expected insertions per tile based on peak_width, effective_genome_size, and per-group read counts

  2. Tile the genome with nonoverlapping tiles of size peak_width

  3. For each tile and group, calculate p_value based on a Poisson model

  4. Compute adjusted p-values using BH method and using the total number of tiles as the number of hypotheses tested.

  5. Repeat steps 2-4 peak_tiling times, with evenly spaced offsets

  6. If merge_peaks is "all" or "group": use merge_peaks_iterative() within each group to keep only the most significant of the overlapping candidate peaks

  7. If merge_peaks is "all", perform a final round of merge_peaks_iterative(), prioritizing each peak by its within-group significance rank

Value

tibble with peak calls and the following columns:

  • chr, start, end: genome coordinates

  • group: group ID that this peak was identified in

  • p_val, q_val: Poission p-value and BH-corrected p-value

  • enrichment: Enrichment of counts in this peak compared to a genome-wide background


Calculate the MD5 checksum of an IterableMatrix

Description

Calculate the MD5 checksum of an IterableMatrix and return the checksum in hexidecimal format.

Usage

checksum(matrix)

Arguments

matrix

IterableMatrix object

Details

checksum() converts the non-zero elements of the sparse input matrix to double precision, concatenates each element value with the element row and column index words, and uses these 16-byte blocks along with the matrix dimensions and row and column names to calculate the checksum. The checksum value depends on the storage order so column- and row-order matrices with the same element values give different checksum values. checksum() uses element and index values in little-endian CPU storage order. It converts to little-endian order on big-endian architecture although this has not been tested.

Value

MD5 checksum string in hexidecimal format.

Examples

library(Matrix)
library(BPCells)
m1 <- matrix(seq(1,12), nrow=3)
m2 <- as(m1, 'dgCMatrix')
m3 <- as(m2, 'IterableMatrix')
checksum(m3)

Cluster an adjacency matrix

Description

Cluster an adjacency matrix

Usage

cluster_graph_leiden(snn, resolution = 0.001, seed = 12531, ...)

cluster_graph_louvain(snn, resolution = 1, seed = 12531)

cluster_graph_seurat(snn, resolution = 0.8, ...)

Arguments

snn

Symmetric adjacency matrix (dgCMatrix) output from e.g. knn_to_snn_graph. Only the lower triangle is used

resolution

Resolution parameter. Higher values result in more clusters

seed

Random seed for clustering initialization

...

Additional arguments to underlying clustering function

Details

cluster_graph_leiden: Leiden graph clustering algorithm igraph::cluster_leiden()

cluster_graph_louvain: Louvain graph clustering algorithm igraph::cluster_louvain()

cluster_graph_seurat: Seurat's clustering algorithm Seurat::FindClusters()

Value

Factor vector containing the cluster assignment for each cell.


Convert grouping vector to sparse matrix

Description

Converts a vector of membership IDs into a sparse matrix

Usage

cluster_membership_matrix(groups, group_order = NULL)

Arguments

groups

Vector with one entry per cell, specifying the cell's group

group_order

Optional vector listing ordering of groups

Value

cell x group matrix where an entry is 1 when a cell is in a given group


Collect features for plotting

Description

Helper function for data on features to plot from a diverse set of data sources.

Usage

collect_features(
  source,
  features = NULL,
  gene_mapping = human_gene_mapping,
  n = 1
)

Arguments

source

Matrix or data frame to pull features from, or a vector of feature values for a single feature. For a matrix, the features must be rows.

features

Character vector of features names to plot if source is not a vector.

gene_mapping

An optional vector for gene name matching with match_gene_symbol(). Ignored if source is a data frame.

n

Internal-use parameter marking the number of nested calls. This is used for finding the name of the "source" input variable from the caller's perspective

Details

If source is a data.frame, features will be drawn from the columns. If source is a matrix object (IterableMatrix, dgCMatrix, or matrix), features will be drawn from rows.

Value

Data frame with one column for each feature requested


Convert the type of a matrix

Description

Convert the type of a matrix

Usage

convert_matrix_type(matrix, type = c("uint32_t", "double", "float"))

Arguments

matrix

IterableMatrix object input

type

One of uint32_t (unsigned 32-bit integer), float (32-bit real number), or double (64-bit real number)

Value

IterableMatrix object


Convert between BPCells fragments and R objects.

Description

BPCells fragments can be interconverted with GRanges and data.frame R objects. The main conversion method is R's builtin as() function, though the convert_to_fragments() helper is also available. For all R objects except GRanges, BPCells assumes a 0-based, end-exclusive coordinate system. (See genomic-ranges-like reference for details)

Usage

# Convert from R to BPCells
convert_to_fragments(x, zero_based_coords = !is(x, "GRanges"))
as(x, "IterableFragments")

# Convert from BPCells to R
as.data.frame(bpcells_fragments)
as(bpcells_fragments, "data.frame")
as(bpcells_fragments, "GRanges")

Arguments

x

Fragment coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • cell_id: cell barcodes or unique identifiers as string or factor

zero_based_coords

Whether to convert the ranges from a 1-based end-inclusive coordinate system to a 0-based end-exclusive coordinate system. Defaults to true for GRanges and false for other formats (see this archived UCSC blogpost)

Value

convert_to_fragments(): IterableFragments object


Color palettes

Description

These color palettes are derived from the ArchR color palettes, and provide large sets of distinguishable colors

Usage

discrete_palette(name, n = 1)

continuous_palette(name)

Arguments

name

Name of the color palette. Valid discrete palettes are: stallion, calm, kelly, bear, ironMan, circus, paired, grove, summerNight, and captain. Valid continuous palettes are bluePurpleDark

n

Minimum number of colors needed

Details

If the requested number of colors is too large, a new palette will be constructed via interpolation from the requested palette

Value

Character vector of hex color codes


Extend genome ranges in a strand-aware fashion.

Description

Extend genome ranges in a strand-aware fashion.

Usage

extend_ranges(
  ranges,
  upstream = 0,
  downstream = 0,
  metadata_cols = c("strand"),
  chromosome_sizes = NULL,
  zero_based_coords = !is(ranges, "GRanges")
)

Arguments

ranges

Genomic regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

upstream

Number of bases to extend each range upstream (negative to shrink width)

downstream

Number of bases to extend each range downstream (negative to shrink width)

metadata_cols

Optional list of metadata columns to require & extract

chromosome_sizes

(optional) Size of chromosomes as a genomic-ranges object

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

Details

Note that ranges will be blocked from extending past the beginning of the chromosome (base 0), and if chromosome_sizes is given then they will also be blocked from extending past the end of the chromosome


Get footprints around a set of genomic coordinates

Description

Get footprints around a set of genomic coordinates

Usage

footprint(
  fragments,
  ranges,
  zero_based_coords = !is(ranges, "GRanges"),
  cell_groups = rlang::rep_along(cellNames(fragments), "all"),
  cell_weights = rlang::rep_along(cell_groups, 1),
  flank = 125L,
  normalization_width = flank%/%10L
)

Arguments

fragments

IterableFragments object

ranges

Footprint centers given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

"+" strand ranges will footprint around the start coordinate, and "-" strand ranges around the end coordinate.

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

cell_groups

Character or factor assigning a group to each cell, in order of cellNames(fragments)

cell_weights

Numeric vector assigning weight factors (e.g. inverse of total reads) to each cell, in order of cellNames(fragments)

flank

Number of flanking basepairs to include on either side of the motif

normalization_width

Number of basepairs at the upstream + downstream extremes to use for calculating enrichment

Value

tibble::tibble() with columns group, position, and count, enrichment


Check if two fragments objects are identical

Description

Check if two fragments objects are identical

Usage

fragments_identical(fragments1, fragments2)

Arguments

fragments1

First IterableFragments to compare

fragments2

Second IterableFragments to compare

Value

boolean for whether the fragments objects are identical


Find gene region

Description

Conveniently look up the region of a gene by gene symbol. The value returned by this function can be used as the region argument for trackplot functions such as trackplot_coverage() or trackplot_gene()

Usage

gene_region(
  genes,
  gene_symbol,
  extend_bp = c(10000, 10000),
  gene_mapping = human_gene_mapping
)

Arguments

genes

Transcipt features given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

  • gene_name: Symbol or gene ID

gene_symbol

Name of gene symbol or ID

extend_bp

Bases to extend region upstream and downstream of gene. If length 1, extension is symmetric. If length 2, provide upstream extension then downstream extension as positive distances.

gene_mapping

Named vector where names are gene symbols or IDs and values are canonical gene symbols

Value

List of chr, start, end positions for use with trackplot functions.


Calculate gene-tile distances for ArchR gene activities

Description

ArchR-style gene activity scores are based on a weighted sum of each tile according to the signed distance from the tile to a gene body. This function calculates the signed distances according to ArchR's default parameters.

Usage

gene_score_tiles_archr(
  genes,
  chromosome_sizes = NULL,
  tile_width = 500,
  addArchRBug = FALSE
)

Arguments

genes

Gene coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

chromosome_sizes

(optional) Size of chromosomes as a genomic-ranges object

tile_width

Size of tiles to consider

addArchRBug

Replicate ArchR bug in handling nested genes

Details

ArchR's tile distance algorithm works as follows

  1. Genes are extended 5kb upstream

  2. Genes are linked to any tiles 1kb-100kb upstream + downstream, but tiles beyond a neighboring gene are not considered

Value

Tibble with one range per tile, with additional metadata columns gene_idx (row index of the gene this tile corresponds to) and distance.

Distance is a signed distance calculated such that if the tile has a smaller start coordinate than the gene and the gene is on the + strand, distance will be negative. The distance of adjacent but non-overlapping regions is 1bp, counting up from there.


Calculate GeneActivityScores

Description

Gene activity scores can be calculated as a distance-weighted sum of per-tile accessibility. The tile weights for each gene can be represented as a sparse matrix of dimension genes x tiles. If we multiply this weight matrix by a corresponding tile matrix (tiles x cells), then we can get a gene activity score matrix of genes x cells. gene_score_weights_archr() calculates the weight matrix (best if you have a pre-computed tile matrix), while gene_score_archr() provides a easy-to-use wrapper.

Usage

gene_score_weights_archr(
  genes,
  chromosome_sizes,
  blacklist = NULL,
  tile_width = 500,
  gene_name_column = "gene_id",
  addArchRBug = FALSE
)

gene_score_archr(
  fragments,
  genes,
  chromosome_sizes,
  blacklist = NULL,
  tile_width = 500,
  gene_name_column = "gene_id",
  addArchRBug = FALSE,
  tile_max_count = 4,
  scale_factor = 10000,
  tile_matrix_path = tempfile(pattern = "gene_score_tile_mat")
)

Arguments

genes

Gene coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

chromosome_sizes

Chromosome start and end coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

See read_ucsc_chrom_sizes().

blacklist

Regions to exclude from calculations, given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

tile_width

Size of tiles to consider

gene_name_column

If not NULL, a column name of genes to use as row names

addArchRBug

Replicate ArchR bug in handling nested genes

fragments

Input fragments object

tile_max_count

Maximum value in the tile counts matrix. If not null, tile counts higher than this will be clipped to tile_max_count. Equivalent to ceiling argument of ArchR::addGeneScoreMatrix()

scale_factor

If not null, counts for each cell will be scaled to sum to scale_factor. Equivalent to scaleTo argument of ArchR::addGeneScoreMatrix()

tile_matrix_path

Path of a directory where the intermediate tile matrix will be saved

Details

gene_score_weights_archr:

Given a set of tile coordinates and distances returned by gene_score_tiles_archr(), calculate a weight matrix of dimensions genes x tiles. This matrix can be multiplied with a tile matrix to obtain ArchR-compatible gene activity scores.

Value

gene_score_weights_archr

Weight matrix of dimension genes x tiles

gene_score_archr

Gene score matrix of dimension genes x cells.


Genomic range formats

Description

BPCells accepts a flexible set of genomic ranges-like objects as input, either GRanges, data.frame, lists, or character vectors. These objects must specify chromosome, start, and end coordinates along with optional metadata about each range. With the exception of GenomicRanges::GRanges objects, BPCells assumes all objects use a zero-based, end-exclusive coordinate system (see below for details).

Valid Range-like objects

BPCells can interpret the following types as ranges:

  • list(), data.frame(), with columns:

    • chr: Character or factor of chromosome names

    • start: Start coordinates (0-based)

    • end: End coordinates (exclusive)

    • (optional) strand: "+"/"-" or TRUE/FALSE for pos/neg strand

    • (optional) Additional metadata as named list entries or data.frame columns

  • GenomicRanges::GRanges

    • start(x) is interpreted as a 1-based start coordinate

    • end(x) is interpreted as an inclusive end coordinate

    • strand(x): "*" entries are interpeted as postive strand

    • (optional) mcols(x) holds additional metadata

  • character

    • Given in format "chr1:1000-2000" or "chr1:1,000-2,000"

    • Uses 0-based, end-exclusive coordinate system

    • Cannot be used for ranges where additional metadata is required

Range coordinate systems

There are two main conventions for the coordinate systems:

One-based, end-inclusive ranges

  • The first base of a chromosome is numbered 1

  • The last base in a range is equal to the end coordinate

  • e.g. 1-5 describes the first 5 bases of the chromosome

  • Used in formats such as SAM, GTF

  • In BPCells, used when reading or writing GenomicRanges::GRanges objects

Zero-based, end-exclusive ranges

  • The first base of a chromosome is numbered 0

  • The last base in a range is one less than the end coordinate

  • e.g. 0-5 describes the first 5 bases of the chromosome

  • Used in formats such as BAM, BED

  • In BPCells, used for all other range objects


Gene Symbol Mapping data

Description

Mapping of the canonical gene symbols corresponding to each unambiguous alias, previous symbol, ensembl ID, or entrez ID.

Usage

human_gene_mapping

mouse_gene_mapping

Format

human_gene_mapping

A named character vector. Names are aliases or IDs and values are the corresponding canonical gene symbol

mouse_gene_mapping

A named character vector. Names are aliases or IDs and values are the corresponding canonical gene symbol

Details

See the source code in data-raw/human_gene_mapping.R and data-raw/mouse_gene_mapping.R for exactly how these mappings were made.

Source

human_gene_mapping

http://ftp.ebi.ac.uk/pub/databases/genenames/hgnc/tsv/non_alt_loci_set.txt

mouse_gene_mapping

http://www.informatics.jax.org/downloads/reports/MGI_EntrezGene.rpt http://www.informatics.jax.org/downloads/reports/MRK_ENSEMBL.rpt


Import MatrixMarket files

Description

Read a sparse matrix from a MatrixMarket file. This is a text-based format used by 10x, Parse, and others to store sparse matrices. Format details on the NIST website.

Usage

import_matrix_market(
  mtx_path,
  outdir = tempfile("matrix_market"),
  row_names = NULL,
  col_names = NULL,
  row_major = FALSE,
  tmpdir = tempdir(),
  load_bytes = 4194304L,
  sort_bytes = 1073741824L
)

import_matrix_market_10x(
  mtx_dir,
  outdir = tempfile("matrix_market"),
  feature_type = NULL,
  row_major = FALSE,
  tmpdir = tempdir(),
  load_bytes = 4194304L,
  sort_bytes = 1073741824L
)

Arguments

mtx_path

Path of mtx or mtx.gz file

outdir

Directory to store the output

row_names

Character vector of row names

col_names

Character vector of col names

row_major

If true, store the matrix in row-major orientation

tmpdir

Temporary directory to use for intermediate storage

load_bytes

The minimum contiguous load size during the merge sort passes

sort_bytes

The amount of memory to allocate for re-sorting chunks of entries

mtx_dir

Directory holding matrix.mtx.gz, barcodes.tsv.gz, and features.tsv.gz

feature_type

String or vector of feature types to include. (cellranger 3.0 and newer)

Details

Import MatrixMarket mtx files to the BPCells format. This implementation ensures fixed memory usage even for very large inputs by doing on-disk sorts. It will be much slower than hdf5 inputs, so only use MatrixMarket format when absolutely necessary.

As a rough speed estimate, importing the 17GB Parse 1M PBMC DGE_1M_PBMC.mtx file takes about 4 minutes and 1.3GB of RAM, producing a compressed output matrix of 1.5GB. mtx.gz files will be slower to import due to gzip decompression.

When importing from 10x mtx files, the row and column names can be read automatically using the import_matrix_market_10x() convenience function.

Value

MatrixDir object with the imported matrix


IterableFragments methods

Description

Methods for IterableFragments objects

Usage

## S4 method for signature 'IterableFragments'
show(object)

cellNames(x)

cellNames(x, ...) <- value

chrNames(x)

chrNames(x, ...) <- value

Arguments

object

IterableFragments object

x

an IterableFragments object

value

Character vector of new names

Details

  • ⁠cellNames<-⁠ It is only possible to replace names, not add new names.

  • ⁠chrNames<-⁠ It is only possible to replace names, not add new names.

Value

  • cellNames() Character vector of cell names, or NULL if none are known

  • chrNames(): Character vector of chromosome names, or NULL if none are known

Functions

  • show(IterableFragments): Print IterableFragments

  • cellNames(): Get cell names

  • cellNames(x, ...) <- value: Set cell names

  • chrNames(): Set chromosome names

  • chrNames(x, ...) <- value: Set chromosome names


IterableMatrix methods

Description

Generic methods and built-in functions for IterableMatrix objects

Usage

matrix_type(x)

storage_order(x)

## S4 method for signature 'IterableMatrix'
show(object)

## S4 method for signature 'IterableMatrix'
t(x)

## S4 method for signature 'IterableMatrix,matrix'
x %*% y

## S4 method for signature 'IterableMatrix'
rowSums(x)

## S4 method for signature 'IterableMatrix'
colSums(x)

## S4 method for signature 'IterableMatrix'
rowMeans(x)

## S4 method for signature 'IterableMatrix'
colMeans(x)

colVars(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  center = NULL,
  ...,
  useNames = TRUE
)

rowVars(
  x,
  rows = NULL,
  cols = NULL,
  na.rm = FALSE,
  center = NULL,
  ...,
  useNames = TRUE
)

rowMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)

colMaxs(x, rows = NULL, cols = NULL, na.rm = FALSE, ..., useNames = TRUE)

## S4 method for signature 'IterableMatrix'
log1p(x)

log1p_slow(x)

## S4 method for signature 'IterableMatrix'
expm1(x)

expm1_slow(x)

## S4 method for signature 'IterableMatrix,numeric'
e1 ^ e2

## S4 method for signature 'numeric,IterableMatrix'
e1 < e2

## S4 method for signature 'IterableMatrix,numeric'
e1 > e2

## S4 method for signature 'numeric,IterableMatrix'
e1 <= e2

## S4 method for signature 'IterableMatrix,numeric'
e1 >= e2

## S4 method for signature 'IterableMatrix'
round(x, digits = 0)

## S4 method for signature 'IterableMatrix,numeric'
e1 * e2

## S4 method for signature 'IterableMatrix,numeric'
e1 + e2

## S4 method for signature 'IterableMatrix,numeric'
e1 / e2

## S4 method for signature 'IterableMatrix,numeric'
e1 - e2

Arguments

x

IterableMatrix/dgCMatrix object

object

IterableMatrix object

y

matrix

Value

  • t() Transposed object

  • x %*% y: dense matrix result

  • rowSums(): vector of row sums

  • colSums(): vector of col sums

  • rowMeans(): vector of row means

  • colMeans(): vector of col means

  • colVars(): vector of col variance

  • rowVars(): vector of row variance

  • rowMaxs(): vector of maxes for every row

  • colMaxs(): vector of column maxes

Functions

  • matrix_type(): Get the matrix data type (mat_uint32_t, mat_float, or mat_double for now)

  • storage_order(): Get the matrix storage order ("row" or "col")

  • show(IterableMatrix): Display an IterableMatrix

  • t(IterableMatrix): Transpose an IterableMatrix

  • x %*% y: Multiply by a dense matrix

  • rowSums(IterableMatrix): Calculate rowSums

  • colSums(IterableMatrix): Calculate colSums

  • rowMeans(IterableMatrix): Calculate rowMeans

  • colMeans(IterableMatrix): Calculate colMeans

  • colVars(): Calculate colVars (replacement for matrixStats::colVars())

  • rowVars(): Calculate rowVars (replacement for matrixStats::rowVars())

  • rowMaxs(): Calculate rowMaxs (replacement for matrixStats::rowMaxs())

  • colMaxs(): Calculate colMax (replacement for matrixStats::colMax())

  • log1p(IterableMatrix): Calculate log(x + 1)

  • log1p_slow(): Calculate log(x + 1) (non-SIMD version)

  • expm1(IterableMatrix): Calculate exp(x) - 1

  • expm1_slow(): Calculate exp(x) - 1 (non-SIMD version)

  • e1^e2: Calculate x^y (elementwise)

  • e1 < e2: Binarize matrix according to numeric < matrix comparison

  • e1 > e2: Binarize matrix according to matrix > numeric comparison

  • e1 <= e2: Binarize matrix according to numeric <= matrix comparison

  • e1 >= e2: Binarize matrix according to matrix >= numeric comparison

  • round(IterableMatrix): round to nearest integer (digits must be 0)

  • e1 * e2: Multiply by a constant, or multiply rows by a vector length nrow(mat)

  • e1 + e2: Add a constant, or row-wise addition with a vector length nrow(mat)

  • e1 / e2: Divide by a constant, or divide rows by a vector length nrow(mat)

  • e1 - e2: Subtract a constant, or row-wise subtraction with a vector length nrow(mat)


Get a knn matrix from reduced dimensions

Description

Search for approximate nearest neighbors between cells in the reduced dimensions (e.g. PCA), and return the k nearest neighbors (knn) for each cell. Optionally, we can find neighbors between two separate sets of cells by utilizing both data and query.

Usage

knn_hnsw(
  data,
  query = NULL,
  k = 10,
  metric = c("euclidean", "cosine"),
  verbose = TRUE,
  threads = 1,
  ef = 100
)

knn_annoy(
  data,
  query = data,
  k = 10,
  metric = c("euclidean", "cosine", "manhattan", "hamming"),
  n_trees = 50,
  search_k = -1
)

Arguments

data

cell x dims matrix for reference dataset

query

cell x dims matrix for query dataset (optional)

k

number of neighbors to calculate

metric

distance metric to use

verbose

whether to print progress information during search

threads

Number of threads to use. Note that result is non-deterministic if threads > 1

ef

ef parameter for RccppHNSW::hnsw_search. Increase for slower search but improved accuracy

n_trees

Number of trees during index build time. More trees gives higher accuracy

search_k

Number of nodes to inspect during the query, or -1 for default value. Higher number gives higher accuracy

Details

knn_hnsw: Use RcppHNSW as knn engine

knn_annoy: Use RcppAnnoy as knn engine

Value

List of 2 matrices – idx for cell x K neighbor indices, dist for cell x K neighbor distances. If no query is given, nearest neighbors are found mapping the data matrix to itself, prohibiting self-neighbors


K Nearest Neighbor (KNN) Graph

Description

Convert a KNN object (e.g. returned by knn_hnsw() or knn_annoy()) into a graph. The graph is represented as a sparse adjacency matrix.

Usage

knn_to_graph(knn, use_weights = FALSE, self_loops = TRUE)

knn_to_snn_graph(
  knn,
  min_val = 1/15,
  self_loops = FALSE,
  return_type = c("matrix", "list")
)

knn_to_geodesic_graph(knn, return_type = c("matrix", "list"), threads = 0L)

Arguments

knn

List of 2 matrices – idx for cell x K neighbor indices, dist for cell x K neighbor distances

use_weights

boolean for whether to replace all distance weights with 1

self_loops

Whether to allow self-loops in the output graph

min_val

minimum jaccard index between neighbors. Values below this will round to 0

return_type

Whether to return a sparse adjacency matrix or an edge list

threads

Number of threads to use during calculations

Details

knn_to_graph Create a knn graph

knn_to_snn_graph Convert a knn object into a shared nearest neighbors adjacency matrix. This follows the algorithm that Seurat uses to compute SNN graphs

knn_to_geodesic_graph Convert a knn object into an undirected weighted graph, using the same geodesic distance estimation method as the UMAP package. This matches the output of umap._umap.fuzzy_simplicial_set from the umap-learn python package, used by default in scanpy.pp.neighbors. Because this only re-weights and symmetrizes the KNN graph, it will usually use less memory and return a sparser graph than knn_to_snn_graph which computes 2nd-order neighbors. Note: when cells don't have themselves listed as the nearest neighbor, results may differ slightly from umap._umap.fuzzy_simplicial_set, which assumes self is always successfully found in the approximate nearest neighbor search.

Value

knn_to_graph Sparse matrix (dgCMatrix) where mat[i,j] = distance from cell i to cell j, or 0 if cell j is not in the K nearest neighbors of i

knn_to_snn_graph

  • return_type == "matrix": Sparse matrix (dgCMatrix) where mat[i,j] = jaccard index of the overlap in nearest neigbors between cell i and cell j, or 0 if the jaccard index is < min_val. Only the lower triangle is filled in, which is compatible with the BPCells clustering methods

  • return_type == "list": List of 3 equal-length vectors i, j, and weight, along with an integer dim. These correspond to the rows, cols, and values of non-zero entries in the lower triangle adjacency matrix. dim is the total number of vertices (cells) in the graph

knn_to_geodesic_graph

  • return_type == "matrix": Sparse matrix (dgCMatrix) where mat[i,j] = normalized similarity between cell i and cell j. Only the lower triangle is filled in, which is compatible with the BPCells clustering methods

  • return_type == "list": List of 3 equal-length vectors i, j, and weight, along with an integer dim. These correspond to the rows, cols, and values of non-zero entries in the lower triangle adjacency matrix. dim is the total number of vertices (cells) in the graph


Test for marker features

Description

Given a features x cells matrix, perform one-vs-all differential tests to find markers.

Usage

marker_features(mat, groups, method = "wilcoxon")

Arguments

mat

IterableMatrix object of dimensions features x cells

groups

Character/factor vector of cell groups/clusters. Length #cells

method

Test method to use. Current options are:

  • wilcoxon: Wilconxon rank-sum test a.k.a Mann-Whitney U test

Details

Tips for using the values from this function:

  • Use dplyr::mutate() to add columns for e.g. adjusted p-value and log fold change.

  • Use dplyr::filter() to get only differential genes above some given threshold

  • To get adjusted p-values, use R p.adjust(), recommended method is "BH"

  • To get log2 fold change: if your input matrix was already log-transformed, calculate (foreground_mean - background_mean)/log(2). If your input matrix was not log-transformed, calculate log2(forground_mean/background_mean)

Value

tibble with the following columns:

  • foreground: Group ID used for the foreground

  • background: Group ID used for the background (or NA if comparing to rest of cells)

  • feature: ID of the feature

  • p_val_raw: Unadjusted p-value for differential test

  • foreground_mean: Average value in the foreground group

  • background_mean: Average value in the background group


Gene symbol matching

Description

Correct alias gene symbols, Ensembl IDs, and Entrez IDs to canonical gene symbols. This is useful for matching gene names between different datasets which might not always use the same gene naming conventions.

Usage

match_gene_symbol(query, subject, gene_mapping = human_gene_mapping)

canonical_gene_symbol(query, gene_mapping = human_gene_mapping)

Arguments

query

Character vector of gene symbols or IDs

subject

Vector of gene symbols or IDs to index into

gene_mapping

Named vector where names are gene symbols or IDs and values are canonical gene symbols

Value

match_gene_symbol

Integer vector of indices v such that subject[v] corresponds to the gene symbols in query

canonical_gene_symbol

Character vector of canonical gene symbols for each symbol in query


Convert between BPCells matrix and R objects.

Description

BPCells matrices can be interconverted with Matrix package dgCMatrix sparse matrices, as well as base R dense matrices (though this may result in high memory usage for large matrices)

Usage

# Convert to R from BPCells
as(bpcells_mat, "dgCMatrix") # Sparse matrix conversion
as.matrix(bpcells_mat) # Dense matrix conversion

# Convert to BPCells from R
as(dgc_mat, "IterableMatrix")

Calculate matrix stats

Description

Calculate matrix stats

Usage

matrix_stats(
  matrix,
  row_stats = c("none", "nonzero", "mean", "variance"),
  col_stats = c("none", "nonzero", "mean", "variance"),
  threads = 0L
)

Arguments

matrix

Input matrix object

row_stats

Which row statistics to compute

col_stats

Which col statistics to compute

threads

Number of threads to use during execution

Details

The statistics will be calculated in a single pass over the matrix, so this method is desirable to use for efficiency purposes compared to the more standard rowMeans or colMeans if multiple statistics are needed. The stats are ordered by complexity: nonzero, mean, then variance. All less complex stats are calculated in the process of calculating a more complicated stat. So to calculate mean and variance simultaneously, just ask for variance, which will compute mean and nonzero counts as a side-effect

Value

List of row_stats: matrix of n_stats x n_rows, col_stats: matrix of n_stats x n_cols


Merge cells into pseudobulks

Description

Peak and tile matrix calculations can be sped up by reducing the number of cells. For cases where the outputs are going to be added together afterwards, this can provide a performance improvement

Usage

merge_cells(fragments, cell_groups)

Arguments

fragments

Input fragments object

cell_groups

Character or factor vector providing a group for each cell. Ordering is the same as cellNames(fragments)


Merge peaks

Description

Merge peaks according to ArchR's iterative merging algorithm. More details on the ArchR website

Usage

merge_peaks_iterative(peaks)

Arguments

peaks

Peaks given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

Must be ordered by priority and have columns chr, start, end.

Details

Properties of merged peaks:

  • No peaks in the merged set overlap

  • Peaks are prioritized according to their order in the original input

  • The output peaks are a subset of the input peaks, with no peak boundaries changed

Value

tibble::tibble() with a nonoverlapping subset of the rows in peaks. All metadata columns are preserved


Elementwise minimum

Description

min_scalar: Take minumum with a global constant

min_by_row: Take the minimum with a per-row constant

min_by_col: Take the minimum with a per-col constant

Usage

min_scalar(mat, val)

min_by_row(mat, vals)

min_by_col(mat, vals)

Arguments

mat

IterableMatrix

val

Single positive numeric value

Details

Take the minimum value of a matrix with a per-row, per-col, or global constant. This constant must be >0 to preserve sparsity of the matrix. This has the effect of capping the maximum value in the matrix.

Value

IterableMatrix


Normalize an object representing genomic ranges

Description

Normalize an object representing genomic ranges

Usage

normalize_ranges(
  ranges,
  metadata_cols = character(0),
  zero_based_coords = !is(ranges, "GRanges"),
  n = 1
)

Arguments

ranges

Genomic regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

metadata_cols

Optional list of metadata columns to require & extract

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

Value

data frame with zero-based coordinates, and elements chr (factor), start (int), and end (int). If ranges does not have chr level information, chr levels are the sorted unique values of chr.

If strand is in metadata_cols, then the output strand element will be TRUE for positive strand, and FALSE for negative strand. (Converted from a character vector of "+"/"-" if necessary)


Count fragments by nucleosomal size

Description

Count fragments by nucleosomal size

Usage

nucleosome_counts(fragments, nucleosome_width = 147)

Arguments

fragments

Fragments object

nucleosome_width

Integer cutoff to use as nucleosome width

Details

Shorter than nucleosome_width is subNucleosomal, nucleosome_width to 2*nucleosome_width-1 is monoNucleosomal, and anything longer is multiNucleosomal. The sum of all fragments is given as nFrags

Value

List with names subNucleosomal, monoNucleosomal, multiNucleosomal, and nFrags, containing the count vectors of fragments in each class per cell.


Read/write a 10x fragments file

Description

10x fragment files come in a bed-like format, with columns chr, start, end, cell_id, and pcr_duplicates. Unlike a standard bed format, the format from cellranger has an inclusive end-coordinate, meaning the end coordinate itself is what should be counted as the tagmentation site, rather than offset by 1.

Usage

open_fragments_10x(path, comment = "#", end_inclusive = TRUE)

write_fragments_10x(
  fragments,
  path,
  end_inclusive = TRUE,
  append_5th_column = FALSE
)

Arguments

path

File path (e.g. fragments.tsv or fragments.tsv.gz)

comment

Skip lines at beginning of file which start with comment string

end_inclusive

Whether the end coordinate of the bed is inclusive – i.e. there was an insertion at the end coordinate rather than the base before the end coordinate. This is the 10x default, though it's not quite standard for the bed file format.

fragments

Input fragments object

append_5th_column

Whether to include 5th column of all 0 for compatibility with 10x fragment file outputs (defaults to 4 columns chr,start,end,cell)

Details

open_fragments_10x

No disk operations will take place until the fragments are used in a function

write_fragments_10x

Fragments will be written to disk immediately, then returned in a readable object.

Value

10x fragments file object


Read/write a 10x feature matrix

Description

Read/write a 10x feature matrix

Usage

open_matrix_10x_hdf5(path, feature_type = NULL, buffer_size = 16384L)

write_matrix_10x_hdf5(
  mat,
  path,
  barcodes = colnames(mat),
  feature_ids = rownames(mat),
  feature_names = rownames(mat),
  feature_types = "Gene Expression",
  feature_metadata = list(),
  buffer_size = 16384L,
  chunk_size = 1024L,
  gzip_level = 0L,
  type = c("uint32_t", "double", "float", "auto")
)

Arguments

path

Path to the hdf5 file on disk

feature_type

Optional selection of feature types to include in output matrix. For multiome data, the options are "Gene Expression" and "Peaks". This option is only compatible with files from cellranger 3.0 and newer.

buffer_size

For performance tuning only. The number of items to be buffered in memory before calling writes to disk.

mat

IterableMatrix

barcodes

Vector of names for the cells

feature_ids

Vector of IDs for the features

feature_names

Vector of names for the features

feature_types

String or vector of feature types

feature_metadata

Named list of additional metadata vectors to store for each feature

chunk_size

For performance tuning only. The chunk size used for the HDF5 array storage.

gzip_level

Gzip compression level. Default is 0 (no compression)

type

Data type of the output matrix. Default is uint32_t to match a matrix of 10x UMI counts. Non-integer data types include float and double. If auto, will use the data type of mat.

Details

The 10x format makes use of gzip compression for the matrix data, which can slow down read performance. Consider writing into another format if the read performance is important to you.

Input matrices must be in column-major storage order, and if the rownames and colnames are not set, names must be provided for the relevant metadata parameters. Some of the metadata parameters are not read by default in BPCells, but it is possible to export them for use with other tools.

Value

BPCells matrix object


Read/write AnnData matrix

Description

Read or write a sparse matrix from an anndata hdf5 file. These functions will automatically transpose matrices when converting to/from the AnnData format. This is because the AnnData convention stores cells as rows, whereas the R convention stores cells as columns. If this behavior is undesired, call t() manually on the matrix inputs and outputs of these functions.

Usage

open_matrix_anndata_hdf5(path, group = "X", buffer_size = 16384L)

write_matrix_anndata_hdf5(
  mat,
  path,
  group = "X",
  buffer_size = 16384L,
  chunk_size = 1024L,
  gzip_level = 0L
)

Arguments

path

Path to the hdf5 file on disk

group

The group within the hdf5 file to write the data to. If writing to an existing hdf5 file this group must not already be in use

buffer_size

For performance tuning only. The number of items to be buffered in memory before calling writes to disk.

chunk_size

For performance tuning only. The chunk size used for the HDF5 array storage.

gzip_level

Gzip compression level. Default is 0 (no compression)

Value

AnnDataMatrixH5 object, with cells as the columns.


Get end-sorted ordering for genome ranges

Description

Use this function to order regioins prior to calling peak_matrix() or tile_matrix().

Usage

order_ranges(ranges, chr_levels, sort_by_end = TRUE)

Arguments

ranges

Genomic regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

chr_levels

Ordering of chromosome names

sort_by_end

If TRUE (defualt), sort by (chr, end, start). Else sort by (chr, start, end)

Value

Numeric vector analagous to the order function. Provides an index selection that will reorder the input ranges to be sorted by chr, end, start


Calculate ranges x cells overlap matrix

Description

Calculate ranges x cells overlap matrix

Usage

peak_matrix(
  fragments,
  ranges,
  mode = c("insertions", "fragments", "overlaps"),
  zero_based_coords = !is(ranges, "GRanges"),
  explicit_peak_names = TRUE
)

Arguments

fragments

Input fragments object. Must have cell names and chromosome names defined

ranges

Peaks/ranges to overlap, given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

mode

Mode for counting peak overlaps. (See "value" section for more details)

zero_based_coords

Whether to convert the ranges from a 1-based end-inclusive coordinate system to a 0-based end-exclusive coordinate system. Defaults to true for GRanges and false for other formats (see this archived UCSC blogpost)

explicit_peak_names

Boolean for whether to add rownames to the output matrix in format e.g chr1:500-1000, where start and end coords are given in a 0-based coordinate system. Note that either way, peak names will be written when the matrix is saved.

Value

Iterable matrix object with dimension ranges x cells. When saved, the column names of the output matrix will be in the format chr1:500-1000, where start and end coords are given in a 0-based coordinate system.

mode options

  • "insertions": Start and end coordinates are separately overlapped with each peak

  • "fragments": Like "insertions", but each fragment can contribute at most 1 count to each peak, even if both the start and end coordinates overlap

  • "overlaps": Like "fragments", but an overlap is also counted if the fragment fully spans the peak even if neither the start or end falls within the peak

Note

When calculating the matrix directly from a fragments tsv, it's necessary to first call select_chromosomes() in order to provide the ordering of chromosomes to expect while reading the tsv.


Dotplot

Description

Plot feature levels per group or cluster as a grid of dots. Dots are colored by z-score normalized average expression, and sized by percent non-zero.

Usage

plot_dot(
  source,
  features,
  groups,
  group_order = NULL,
  gene_mapping = human_gene_mapping,
  colors = c("lightgrey", "#4682B4"),
  return_data = FALSE,
  apply_styling = TRUE
)

Arguments

source

Feature x cell matrix or data.frame with features. For best results, features should be sparse and log-normalized (e.g. run log1p() so zero raw counts map to zero)

features

Character vector of features to plot

groups

Vector with one entry per cell, specifying the cell's group

group_order

Optional vector listing ordering of groups

gene_mapping

An optional vector for gene name matching with match_gene_symbol().

colors

Color scale for plot

return_data

If true, return data from just before plotting rather than a plot.

apply_styling

If false, return a plot without pretty styling applied


Plot UMAP or embeddings

Description

Plot one or more features by coloring cells in a UMAP plot.

Usage

plot_embedding(
  source,
  embedding,
  features = NULL,
  quantile_range = c(0.01, 0.99),
  randomize_order = TRUE,
  smooth = NULL,
  smooth_rounds = 3,
  gene_mapping = human_gene_mapping,
  size = NULL,
  rasterize = FALSE,
  raster_pixels = 512,
  legend_continuous = c("auto", "quantile", "value"),
  labels_quantile_range = TRUE,
  colors_continuous = c("lightgrey", "#4682B4"),
  legend_discrete = TRUE,
  labels_discrete = TRUE,
  colors_discrete = discrete_palette("stallion"),
  return_data = FALSE,
  return_plot_list = FALSE,
  apply_styling = TRUE
)

Arguments

source

Matrix, or data frame to pull features from, or a vector of feature values for a single feature. For a matrix, the features must be rows.

embedding

A matrix of dimensions cells x 2 with embedding coordinates

features

Character vector of features to plot if source is not a vector.

quantile_range

(optional) Length 2 vector giving the quantiles to clip the minimum and maximum color scale values, as fractions between 0 and 1. NULL or NA values to skip clipping

randomize_order

If TRUE, shuffle cells to prevent overplotting biases. Can pass an integer instead to specify a random seed to use.

smooth

(optional) Sparse matrix of dimensions cells x cells with cell-cell distance weights for smoothing.

smooth_rounds

Number of multiplication rounds to apply when smoothing.

gene_mapping

An optional vector for gene name matching with match_gene_symbol(). Ignored if source is a data frame.

size

Point size for plotting

rasterize

Whether to rasterize the point drawing to speed up display in graphics programs.

raster_pixels

Number of pixels to use when rasterizing. Can provide one number for square dimensions, or two numbers for width x height.

legend_continuous

Whether to label continuous features by quantile or value. "auto" labels by quantile only when all features are continuous and quantile_range is not NULL. Quantile labeling adds text annotation listing the range of displayed values.

labels_quantile_range

Whether to add a text label with the value range of each feature when the legend is set to quantile

colors_continuous

Vector of colors to use for continuous color palette

legend_discrete

Whether to show the legend for discrete (categorical) features.

labels_discrete

Whether to add text labels at the center of each group for discrete (categorical) features.

colors_discrete

Vector of colors to use for discrete (categorical) features.

return_data

If true, return data from just before plotting rather than a plot.

return_plot_list

If TRUE, return multiple plots as a list, rather than a single plot combined using patchwork::wrap_plots()

apply_styling

If false, return a plot without pretty styling applied

Details

Smoothing

Smoothing is performed as follows: first, the smoothing matrix is normalized so the sum of incoming weights to every cell is 1. Then, the raw data values are repeatedly multiplied by the smoothing matrix and re-scaled so the average value stays the same.

Value

By default, returns a ggplot2 object with all the requested features plotted in a grid. If return_data or return_plot_list is called, the return value will match that argument.


Fragment size distribution

Description

Plot the distribution of fragment lengths, with length in basepairs on the x-axis, and proportion of fragments on the y-axis. Typical plots will show 10-basepair periodicity, as well as humps spaced at multiples of a nucleosome width (about 150bp).

Usage

plot_fragment_length(
  fragments,
  max_length = 500,
  return_data = FALSE,
  apply_styling = TRUE
)

Arguments

fragments

Fragments object

max_length

Maximum length to show on the plot

return_data

If true, return data from just before plotting rather than a plot.

apply_styling

If false, return a plot without pretty styling applied

Value

Numeric vector where index i contans the number of length-i fragments


Knee plot of single cell read counts

Description

Plots read count rank vs. number of reads on a log-log scale.

Usage

plot_read_count_knee(
  read_counts,
  cutoff = NULL,
  return_data = FALSE,
  apply_styling = TRUE
)

Arguments

read_counts

Vector of read counts per cell

cutoff

(optional) Read cutoff to mark on the plot

return_data

If true, return data from just before plotting rather than a plot.

apply_styling

If false, return a plot without pretty styling applied

Details

Performs logarithmic downsampling to reduce the number of points plotted

Value

ggplot2 plot object


Plot TF footprint

Description

Plot the footprinting around TF motif sites

Usage

plot_tf_footprint(
  fragments,
  motif_positions,
  cell_groups = rlang::rep_along(cellNames(fragments), "all"),
  flank = 250L,
  smooth = 0L,
  zero_based_coords = !is(genes, "GRanges"),
  colors = discrete_palette("stallion"),
  return_data = FALSE,
  apply_styling = TRUE
)

Arguments

fragments

IterableFragments object

motif_positions

Coordinate ranges for motifs (must include strand) and have constant width

cell_groups

Character or factor assigning a group to each cell, in order of cellNames(fragments)

flank

Number of flanking basepairs to include on either side of the motif

smooth

(optional) Sparse matrix of dimensions cells x cells with cell-cell distance weights for smoothing.

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

return_data

If true, return data from just before plotting rather than a plot.

apply_styling

If false, return a plot without pretty styling applied

See Also

footprint(), plot_tss_profile()


Plot TSS profile

Description

Plot the enrichmment of insertions relative to transcription start sites (TSS). Typically, this plot shows strong enrichment of insertions near a TSS, and a small bump downstream around 220bp downstream of the TSS for the +1 nucleosome.

Usage

plot_tss_profile(
  fragments,
  genes,
  cell_groups = rlang::rep_along(cellNames(fragments), "all"),
  flank = 2000L,
  smooth = 0L,
  zero_based_coords = !is(genes, "GRanges"),
  colors = discrete_palette("stallion"),
  return_data = FALSE,
  apply_styling = TRUE
)

Arguments

fragments

IterableFragments object

genes

Coordinate ranges for genes (must include strand)

cell_groups

Character or factor assigning a group to each cell, in order of cellNames(fragments)

flank

Number of flanking basepairs to include on either side of the motif

smooth

Number of bases to smooth over (rolling average)

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

return_data

If true, return data from just before plotting rather than a plot.

apply_styling

If false, return a plot without pretty styling applied

See Also

footprint(), plot_tf_footprint()


TSS Enrichment vs. Fragment Counts plot

Description

Density scatter plot with log10(fragment_count) on the x-axis and TSS enrichment on the y-axis. This plot is most useful to select which cell barcodes in an experiment correspond to high-quality cells

Usage

plot_tss_scatter(
  atac_qc,
  min_frags = NULL,
  min_tss = NULL,
  bins = 100,
  apply_styling = TRUE
)

Arguments

atac_qc

Tibble as returned by qc_scATAC(). Must have columns nFrags and TSSEnrichment

min_frags

Minimum fragment count cutoff

min_tss

Minimum TSS Enrichment cutoff

bins

Number of bins for density calculation

apply_styling

If false, return a plot without pretty styling applied


Add sample prefix to cell names

Description

Rename cells by adding a prefix to the names. Most commonly this will be a sample name. All cells will recieve the exact text of prefix added to the beginning, so any separator characters like "_" must be included in the given prefix. Use this prior to merging fragments from different experiments with c() in order to help prevent cell name clashes.

Usage

prefix_cell_names(fragments, prefix)

Arguments

fragments

Input fragments object.

prefix

String to add as the prefix

Value

Fragments object with prefixed names


Calculate ArchR-compatible per-cell QC statistics

Description

Calculate ArchR-compatible per-cell QC statistics

Usage

qc_scATAC(fragments, genes, blacklist)

Arguments

fragments

IterableFragments object

genes

Gene coordinates given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

blacklist

Blacklisted regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

Details

This implementation mimics ArchR's default parameters. For uses requiring more flexibility to tweak default parameters, the best option is to re-implement this function with required changes. Output columns of data.frame:

  • cellName: cell name for each cell

  • nFrags: number of fragments per cell

  • subNucleosomal, monoNucleosomal, multiNucleosomal: number of fragments of size 1-146bp, 147-254bp, and 255bp + respectively. equivalent to ArchR's nMonoFrags, nDiFrags, nMultiFrags respectively

  • TSSEnrichment: AvgInsertInTSS / max(AvgInsertFlankingTSS, 0.1), where AvgInsertInTSS is ReadsInTSS / 101 (window size), and AvgInsertFlankingTSS is ReadsFlankingTSS / (100*2) (window size). The max(0.1) ensures that very low-read cells do not get assigned spuriously high TSSEnrichment.

  • ReadsInPromoter: Number of reads from 2000bp upstream of TSS to 101bp downstream of TSS

  • ReadsInBlacklist: Number of reads in the provided blacklist region

  • ReadsInTSS: Number of reads overlapping the 101bp centered around each TSS

  • ReadsFlankingTSS: Number of reads overlapping 1901-2000bp +/- each TSS

Differences from ArchR: Note that ArchR by default uses a different set of annotations to derive TSS sites and promoter sites. This function uses just one annotation for gene start+end sites, so must be called twice to exactly re-calculate the ArchR QC stats.

ArchR's PromoterRatio and BlacklistRatio are not included in the output, as they can be easily calculated from ReadsInPromoter / nFrags and ReadsInBlacklist / nFrags. Similarly, ArchR's NucleosomeRatio can be calculated as (monoNucleosomal + multiNucleosomal) / subNucleosomal.

Value

data.frame with QC data


Find signed distance to nearest genomic ranges

Description

Given a set of genomic ranges, find the distance to the nearest neighbors both upstream and downstream.

Usage

range_distance_to_nearest(
  ranges,
  addArchRBug = FALSE,
  zero_based_coords = !is(ranges, "GRanges")
)

Arguments

ranges

Genomic regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

addArchRBug

boolean to reproduce ArchR's bug that incorrectly handles nested genes

zero_based_coords

If true, coordinates start and 0 and the end coordinate is not included in the range. If false, coordinates start at 1 and the end coordinate is included in the range

Value

A 2-column data.frame with columns upstream and downstream, containing the distances to the nearest neighbor in the respective directions. For ranges on + or * strand, distance is calculated as:

  • upstream = max(start(range) - end(upstreamNeighbor), 0)

  • downstream = max(start(downstreamNeighbor) - end(range), 0)

For ranges on - strand, the definition of upstream and downstream is flipped. Note that this definition of distance is one off from GenomicRanges::distance(), as ranges that neighbor but don't overlap are given a distance of 1 rather than 0.


Read a bed file into a data frame

Description

Bed files can contain peak or blacklist annotations. These utilities help read thos annotations

Usage

read_bed(
  path,
  additional_columns = character(0),
  backup_url = NULL,
  timeout = 300
)

read_encode_blacklist(
  dir,
  genome = c("hg38", "mm10", "hg19", "dm6", "dm3", "ce11", "ce10"),
  timeout = 300
)

Arguments

path

Path to file (or desired save location if backup_url is used)

additional_columns

Names for additional columns in the bed file

backup_url

If path does not exist, provides a URL to download the gtf from

timeout

Maximum time in seconds to wait for download from backup_url

dir

Output directory to cache the downloaded gtf file

genome

genome name

Details

read_bed

Read a bed file from disk or a url.

read_encode_blacklist

Downloads the Boyle Lab blacklist, as described in https://doi.org/10.1038/s41598-019-45839-z

Value

Data frame with coordinates using the 0-based convention.

See Also

read_gtf(), read_gencode_genes()


Read GTF gene annotations

Description

Read gene annotations from gtf format into a data frame. The source can be a URL, a gtf file on disk, or a gencode release version.

Usage

read_gtf(
  path,
  attributes = c("gene_id"),
  tags = character(0),
  features = c("gene"),
  keep_attribute_column = FALSE,
  backup_url = NULL,
  timeout = 300
)

read_gencode_genes(
  dir,
  release = "latest",
  annotation_set = c("basic", "comprehensive"),
  gene_type = "lncRNA|protein_coding|IG_.*_gene|TR_.*_gene",
  attributes = c("gene_id", "gene_type", "gene_name"),
  tags = character(0),
  features = c("gene"),
  timeout = 300
)

read_gencode_transcripts(
  dir,
  release = "latest",
  transcript_choice = c("MANE_Select", "Ensembl_Canonical", "all"),
  annotation_set = c("basic", "comprehensive"),
  gene_type = "lncRNA|protein_coding|IG_.*_gene|TR_.*_gene",
  attributes = c("gene_id", "gene_type", "gene_name", "transcript_id"),
  features = c("transcript", "exon"),
  timeout = 300
)

Arguments

path

Path to file (or desired save location if backup_url is used)

attributes

Vector of GTF attribute names to parse out as columns

tags

Vector of tags to parse out as boolean presence/absence

features

List of features types to keep from the GTF (e.g. gene, transcript, exon, intron)

keep_attribute_column

Boolean for whether to preserve the raw attribute text column

backup_url

If path does not exist, provides a URL to download the gtf from

timeout

Maximum time in seconds to wait for download from backup_url

dir

Output directory to cache the downloaded gtf file

release

release version (prefix with M for mouse versions). For most recent version, use "latest" or "latest_mouse"

annotation_set

Either "basic" or "comprehensive" annotation sets (see details section).

gene_type

Regular expression with which gene types to keep. Defaults to protein_coding, lncRNA, and IG/TR genes

transcript_choice

Method for selecting representative transcripts. Choices are:

  • MANE_Select: human-only, most conservative

  • Ensembl_Canonical: human+mouse, superset of MANE_Select for human

  • all: Preserve all transcript models (not recommended for plotting)

Details

read_gtf

Read gtf from a file or URL

read_gencode_genes

Read gene annotations directly from GENCODE. The file name will vary depending on the release and annotation set requested, but will be of the format gencode.v42.annotation.gtf.gz. GENCODE currently recommends the basic set: https://www.gencodegenes.org/human/. In release 42, both the comprehensive and basic sets had identical gene-level annotations, but the comprehensive set had additional transcript variants annotated.

read_gencode_transcripts

Read transcript models from GENCODE, for use with trackplot_gene()

Value

Data frame with coordinates using the 0-based convention. Columns are:

  • chr

  • source

  • feature

  • start

  • end

  • score

  • strand

  • frame

  • attributes (optional; named according to listed attributes)

  • tags (named according to listed tags)

See Also

read_bed(), read_encode_blacklist()


Read UCSC chromosome sizes

Description

Read chromosome sizes from UCSC and return as a tibble with one row per chromosome. The underlying data is pulled from here: https://hgdownload.soe.ucsc.edu/downloads.html

Usage

read_ucsc_chrom_sizes(
  dir,
  genome = c("hg38", "mm39", "mm10", "mm9", "hg19"),
  keep_chromosomes = "chr[0-9]+|chrX|chrY",
  timeout = 300
)

Rotate ggplot x axis labels

Description

Rotate ggplot x axis labels

Usage

rotate_x_labels(degrees = 45)

Arguments

degrees

Number of degrees to rotate by


SCTransform Pearson Residuals

Description

Calculate pearson residuals of a negative binomial sctransform model. Normalized values are calculated as (X - mu) / sqrt(mu + mu^2/theta). mu is calculated as cell_read_counts * gene_beta.

Usage

sctransform_pearson(
  mat,
  gene_theta,
  gene_beta,
  cell_read_counts,
  min_var = -Inf,
  clip_range = c(-10, 10),
  columns_are_cells = TRUE,
  slow = FALSE
)

Arguments

mat

IterableMatrix (raw counts)

gene_theta

Vector of per-gene thetas (overdispersion values)

gene_beta

Vector of per-gene betas (expression level values)

cell_read_counts

Vector of total reads per (umi count for RNA)

min_var

Minimum value for clipping variance

clip_range

Length 2 vector of min and max clipping range

columns_are_cells

Whether the columns of the matrix correspond to cells (default) or genes

slow

If TRUE, use a 10x slower but more precise implementation (default FALSE)

Details

The parameterization used is somewhat simplified compared to the original SCTransform paper, in particular it uses a linear-scale rather than log-scale to represent the cell_read_counts and gene_beta variables. It also does not support the addition of arbitrary cell metadata (e.g. batch) to add to the negative binomial regression.

Value

IterableMatrix


Subset, translate, or reorder cell IDs

Description

Subset, translate, or reorder cell IDs

Usage

select_cells(fragments, cell_selection)

Arguments

fragments

Input fragments object

cell_selection

List of chromosme IDs (numeric), or names (character). The output cell ID n will be taken from the input cell with ID/name cell_selection[n].


Subset, translate, or reorder chromosome IDs

Description

Subset, translate, or reorder chromosome IDs

Usage

select_chromosomes(fragments, chromosome_selection)

Arguments

fragments

Input fragments object

chromosome_selection

List of chromosme IDs (numeric), or names (character). The output chromosome ID n will be taken from the input fragments chromosome with ID/name chromosome_selection[n].


Subset fragments by genomic region

Description

Fragments can be subset based on overlapping (or not overlapping) a set of regions

Usage

select_regions(
  fragments,
  ranges,
  invert_selection = FALSE,
  zero_based_coords = !is(ranges, "GRanges")
)

Arguments

fragments

Input fragments object.

ranges

Peaks/ranges to overlap, given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

invert_selection

If TRUE, select fragments not overlapping selected regions instead of only fragments overlapping the selected regions.

zero_based_coords

Whether to convert the ranges from a 1-based end-inclusive coordinate system to a 0-based end-exclusive coordinate system. Defaults to true for GRanges and false for other formats (see this archived UCSC blogpost)

Value

Fragments object filtered according to the selected regions


Adjust trackplot properties

Description

Adjust labels and heights on trackplots. Labels are set as facet labels in ggplot2, and heights are additional properties read by trackplot_combine() to determine relative height of input plots.

Usage

set_trackplot_label(plot, labels)

set_trackplot_height(plot, height)

get_trackplot_height(plot)

Arguments

plot

ggplot object

labels

character vector of labels – must match existing number of facets in plot

height

New height. If numeric, adjusts relative height. If ggplot2::unit or grid::unit sets absolute height in specified units. "null" units are interpreted as relative height.

Value

set_trackplot_label: ggplot object with adjusted facet labels

set_trackplot_height: ggplot object with adjusted trackplot height

get_trackplot_height: ggplot2::unit object with height setting


Shift start or end coordinates

Description

Shifts start or end of fragments by a fixed amount, which can be useful to correct the Tn5 offset.

Usage

shift_fragments(fragments, shift_start = 0L, shift_end = 0L)

Arguments

fragments

Input fragments object

shift_start

How many basepairs to shift the start coords

shift_end

How many basepairs to shift the end coords

Details

The correct Tn5 offset is +/- 4bp since the Tn5 cut sites on opposite strands are offset by 9bp. However, +4/-5 bp is often applied to bed-format files, since the end coordinate in bed files is 1 past the last basepair of the sequenced DNA fragment. This results in a bed-like format except with inclusive end coordinates.

Value

Shifted fragments object


Subset fragments by length

Description

Subset fragments by length

Usage

subset_lengths(fragments, min_len = 0L, max_len = NA_integer_)

Arguments

fragments

Input fragments object

min_len

Minimum bases in fragment (inclusive)

max_len

Maximum bases in fragment (inclusive)

Details

Fragment length is calculated as end-start

Value

Fragments object


Calculate svds

Description

Use the C++ Spectra solver (same as RSpectra package), in order to compute the largest k values and corresponding singular vectors. Empirically, memory usage is much lower than using irlba::irlba(), likely due to avoiding R garbage creation while solving due to the pure-C++ solver. This documentation is a slightly-edited version of the RSpectra::svds() documentation.

Usage

svds(A, k, nu = k, nv = k, opts = list(), threads=0L, ...)

Arguments

A

The matrix whose truncated SVD is to be computed.

k

Number of singular values requested.

nu

Number of right singular vectors to be computed. This must be between 0 and 'k'. (Must be equal to 'k' for BPCells IterableMatrix)

opts

Control parameters related to computing algorithm. See Details below

threads

Control threads to use calculating mat-vec producs (BPCells specific)

Details

When RSpectra is installed, this function will just add a method to RSpectra::svds() for the IterableMatrix class.

The opts argument is a list that can supply any of the following parameters:

ncv

Number of Lanzcos basis vectors to use. More vectors will result in faster convergence, but with greater memory use. ncv must be satisfy k<ncvpk < ncv \le p where p = min(m, n). Default is min(p, max(2*k+1, 20)).

tol

Precision parameter. Default is 1e-10.

maxitr

Maximum number of iterations. Default is 1000.

center

Either a logical value (TRUE/FALSE), or a numeric vector of length nn. If a vector cc is supplied, then SVD is computed on the matrix A1cA - 1c', in an implicit way without actually forming this matrix. center = TRUE has the same effect as center = colMeans(A). Default is FALSE. Ignored in BPCells

scale

Either a logical value (TRUE/FALSE), or a numeric vector of length nn. If a vector ss is supplied, then SVD is computed on the matrix (A1c)S(A - 1c')S, where cc is the centering vector and S=diag(1/s)S = diag(1/s). If scale = TRUE, then the vector ss is computed as the column norm of A1cA - 1c'. Default is FALSE. Ignored in BPCells

Value

A list with the following components:

d

A vector of the computed singular values.

u

An m by nu matrix whose columns contain the left singular vectors. If nu == 0, NULL will be returned.

v

An n by nv matrix whose columns contain the right singular vectors. If nv == 0, NULL will be returned.

nconv

Number of converged singular values.

niter

Number of iterations used.

nops

Number of matrix-vector multiplications used.

References

Qiu Y, Mei J (2022). RSpectra: Solvers for Large-Scale Eigenvalue and SVD Problems. R package version 0.16-1, https://CRAN.R-project.org/package=RSpectra.


Calculate ranges x cells tile overlap matrix

Description

Calculate ranges x cells tile overlap matrix

Usage

tile_matrix(
  fragments,
  ranges,
  mode = c("insertions", "fragments"),
  zero_based_coords = !is(ranges, "GRanges"),
  explicit_tile_names = FALSE
)

Arguments

fragments

Input fragments object

ranges

Tiled regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • tile_width: Size of each tile in this region in basepairs

Must be non-overlapping and sorted by (chr, start), with chromosomes ordered according to the chromosome names of fragments

mode

Mode for counting tile overlaps. (See "value" section for more detail)

zero_based_coords

Whether to convert the ranges from a 1-based end-inclusive coordinate system to a 0-based end-exclusive coordinate system. Defaults to true for GRanges and false for other formats (see this archived UCSC blogpost)

explicit_tile_names

Boolean for whether to add rownames to the output matrix in format e.g chr1:500-1000, where start and end coords are given in a 0-based coordinate system. For whole-genome Tile matrices the names will take ~5 seconds to generate and take up 400MB of memory. Note that either way, tile names will be written when the matrix is saved.

Value

Iterable matrix object with dimension ranges x cells. When saved, the column names will be in the format chr1:500-1000, where start and end coords are given in a 0-based coordinate system.

mode options

  • "insertions": Start and end coordinates are separately overlapped with each tile

  • "fragments": Like "insertions", but each fragment can contribute at most 1 count to each tile, even if both the start and end coordinates overlap

Note

When calculating the matrix directly from a fragments tsv, it's necessary to first call select_chromosomes() in order to provide the ordering of chromosomes to expect while reading the tsv.


Combine track plots

Description

Combines multiple track plots of the same region into a single grid. Uses the patchwork package to perform the alignment.

Usage

trackplot_combine(
  tracks,
  side_plot = NULL,
  title = NULL,
  side_plot_width = 0.3
)

Arguments

tracks

List of tracks in order from top to bottom, generally ggplots as output from the other ⁠trackplot_*()⁠ functions.

side_plot

Optional plot to align to the right (e.g. RNA expression per cluster). Will be aligned to a trackplot_coverage() output if present, or else the first generic ggplot in the alignment. Should be in horizontal orientation and in the same cluster ordering as the coverage plots.

title

Text for overarching title of the plot

side_plot_width

Fraction of width that should be used for the side plot relative to the main track area

Value

A plot object with aligned genome plots. Each aligned row has the text label, y-axis, and plot body. The relative height of each row is given by heights. A shared title and x-axis are put at the top.

See Also

trackplot_coverage(), trackplot_gene(), trackplot_loop(), trackplot_scalebar()


Pseudobulk coverage trackplot

Description

Plot a pseudobulk genome track, showing the number of fragment insertions across a region for each cell type or group.

Usage

trackplot_coverage(
  fragments,
  region,
  groups,
  cell_read_counts,
  group_order = NULL,
  bins = 500,
  clip_quantile = 0.999,
  colors = discrete_palette("stallion"),
  legend_label = "group",
  zero_based_coords = !is(region, "GRanges"),
  return_data = FALSE
)

Arguments

fragments

Fragments object

region

Region to plot, e.g. output from gene_region(). String of format "chr1:100-200", or list/data.frame/GRanges of length 1 specifying chr, start, end. See help("genomic-ranges-like") for details

groups

Vector with one entry per cell, specifying the cell's group

cell_read_counts

Numeric vector of read counts for each cell (used for normalization)

group_order

Optional vector listing ordering of groups

bins

Number of bins to plot across the region

clip_quantile

(optional) Quantile of values for clipping y-axis limits. Default of 0.999 will crop out just the most extreme outliers across the region. NULL to disable clipping

colors

Character vector of color values (optionally named by group)

legend_label

Custom label to put on the legend

zero_based_coords

Whether to convert the ranges from a 1-based end-inclusive coordinate system to a 0-based end-exclusive coordinate system. Defaults to true for GRanges and false for other formats (see this archived UCSC blogpost)

return_data

If true, return data from just before plotting rather than a plot.

scale_bar

Whether to include a scale bar in the top track (TRUE or FALSE)

Value

Returns a combined plot of pseudobulk genome tracks. For compatability with draw_trackplot_grid(), the extra attribute ⁠$patches$labels⁠ will be added to specify the labels for each track. If return_data or return_plot_list is TRUE, the return value will be modified accordingly.

See Also

trackplot_combine(), trackplot_gene(), trackplot_loop(), trackplot_scalebar()


Plot transcript models

Description

Plot transcript models

Usage

trackplot_gene(
  transcripts,
  region,
  exon_size = 2.5,
  gene_size = 0.5,
  label_size = 11 * 0.8/ggplot2::.pt,
  track_label = "Genes",
  return_data = FALSE
)

Arguments

transcripts

Transcipt features given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

  • strand: +/- or TRUE/FALSE for positive or negative strand

  • feature: Only entries marked as "transcript" or "exon" will be considered

  • gene_name: Symbol or gene ID to display

  • transcript_id: Transcritp identifier to link transcripts and exons

Usually given as the output from read_gencode_transcripts()

region

Region to plot, e.g. output from gene_region(). String of format "chr1:100-200", or list/data.frame/GRanges of length 1 specifying chr, start, end. See help("genomic-ranges-like") for details

exon_size

size for exon lines in units of mm

label_size

size for transcript labels in units of mm

return_data

If true, return data from just before plotting rather than a plot.

labels

Character vector with labels for each item in transcripts. NA for items that should not be labeled

transcript_size

size for transcript lines in units of mm

Value

Plot of gene locations

See Also

trackplot_combine(), trackplot_coverage(), trackplot_loop(), trackplot_scalebar()


Plot loops

Description

Plot loops

Usage

trackplot_loop(
  loops,
  region,
  color_by = NULL,
  colors = c("#bfd3e6", "#8c96c6", "#88419d", "#4d004b"),
  allow_truncated = TRUE,
  curvature = 0.75,
  track_label = "Links",
  return_data = FALSE
)

Arguments

loops

Genomic regions given as GRanges, data.frame, or list. See help("genomic-ranges-like") for details on format and coordinate systems. Required attributes:

  • chr, start, end: genomic position

region

Region to plot, e.g. output from gene_region(). String of format "chr1:100-200", or list/data.frame/GRanges of length 1 specifying chr, start, end. See help("genomic-ranges-like") for details

color_by

Name of a metadata column in loops to use for coloring, or a numeric vector with same length as loops

colors

Vector of hex color codes to use for the color gradient

allow_truncated

If FALSE, remove any loops that are not fully contained within region

curvature

Curvature value between 0 and 1. 1 is a 180-degree arc, and 0 is flat lines.

return_data

If true, return data from just before plotting rather than a plot.

Value

Plot of loops connecting genomic coordinates

See Also

trackplot_combine(), trackplot_coverage(), trackplot_gene(), trackplot_scalebar()


Plot scale bar

Description

Plots a human-readable scale bar and coordinates of the region being plotted

Usage

trackplot_scalebar(region, font_pt = 11)

Arguments

region

Region to plot, e.g. output from gene_region(). String of format "chr1:100-200", or list/data.frame/GRanges of length 1 specifying chr, start, end. See help("genomic-ranges-like") for details

font_pt

Font size for scale bar labels in units of pt.

Value

Plot with coordinates and scalebar for plotted genomic region

See Also

trackplot_combine(), trackplot_coverage(), trackplot_gene(), trackplot_loop()


Transpose the storage order for a matrix

Description

Transpose the storage order for a matrix

Usage

transpose_storage_order(
  matrix,
  outdir = tempfile("transpose"),
  tmpdir = tempdir(),
  load_bytes = 4194304L,
  sort_bytes = 1073741824L
)

Arguments

matrix

Input matrix

outdir

Directory to store the output

tmpdir

Temporary directory to use for intermediate storage

load_bytes

The minimum contiguous load size during the merge sort passes

sort_bytes

The amount of memory to allocate for re-sorting chunks of entries

Details

This re-sorts the entries of a matrix to change the storage order from row-major to col-major. For large matrices, this can be slow – around 2 minutes to transpose a 500k cell RNA-seq matrix The default load_bytes (4MiB) and sort_bytes (1GiB) parameters allow ~85GB of data to be sorted with two passes through the data, and ~7.3TB of data to be sorted in three passes through the data.

Value

MatrixDir object with a copy of the input matrix, but the storage order flipped


Read/write BPCells fragment objects

Description

BPCells fragments can be read/written in compressed (bitpacked) or uncompressed form in a variety of storage locations: in memory (as an R object), in an hdf5 file, or in a directory on disk (containing binary files).

Usage

write_fragments_memory(fragments, compress = TRUE)

write_fragments_dir(
  fragments,
  dir,
  compress = TRUE,
  buffer_size = 1024L,
  overwrite = FALSE
)

open_fragments_dir(dir, buffer_size = 1024L)

write_fragments_hdf5(
  fragments,
  path,
  group = "fragments",
  compress = TRUE,
  buffer_size = 8192L,
  chunk_size = 1024L,
  overwrite = FALSE,
  gzip_level = 0L
)

open_fragments_hdf5(path, group = "fragments", buffer_size = 16384L)

Arguments

fragments

Input fragments object

compress

Whether or not to compress the data. With compression, storage size is be about half the size of a gzip-compressed 10x fragments file.

dir

Directory to read/write the data from

buffer_size

For performance tuning only. The number of items to be bufferred in memory before calling writes to disk.

overwrite

If TRUE, write to a temp dir then overwrite existing data. Alternatively, pass a temp path as a string to customize the temp dir location.

path

Path to the hdf5 file on disk

group

The group within the hdf5 file to write the data to. If writing to an existing hdf5 file this group must not already be in use

chunk_size

For performance tuning only. The chunk size used for the HDF5 array storage.

gzip_level

Gzip compression level. Default is 0 (no compression). This is recommended when both compression and compatibility with outside programs is required. Otherwise, using compress=TRUE is recommended as it is >10x faster with often similar compression levels.

Details

Saving in a directory on disk is a good default for local analysis, as it provides the best I/O performance and lowest memory usage. The HDF5 format allows saving within existing hdf5 files to group data together, and the in memory format provides the fastest performance in the event memory usage is unimportant.

Value

Fragment object


Write insertion counts to bedgraph file

Description

Write insertion counts data for one or more pseudobulks to bedgraph format. This reports the total number insertions at each basepair for each group listed in cell_groups.

Usage

write_insertion_bedgraph(
  fragments,
  path,
  cell_groups = NULL,
  insertion_mode = c("both", "start_only", "end_only")
)

Arguments

fragments

IterableFragments object

path

Path(s) to save bedgraph to, optionally ending in ".gz" to add gzip compression. If cell_groups is provided, path must be a named character vector, with one name for each level in cell_groups

cell_groups

Character or factor assigning a group to each cell, in order of cellNames(fragments)

insertion_mode

Which fragment ends to use for insertion counts calculation. One of "both", "start_only", or "end_only"


Read/write sparse matrices

Description

BPCells matrices are stored in sparse format, meaning only the non-zero entries are stored. Matrices can store integer counts data or decimal numbers (float or double). See details for more information.

Usage

write_matrix_memory(mat, compress = TRUE)

write_matrix_dir(
  mat,
  dir,
  compress = TRUE,
  buffer_size = 8192L,
  overwrite = FALSE
)

open_matrix_dir(dir, buffer_size = 8192L)

write_matrix_hdf5(
  mat,
  path,
  group,
  compress = TRUE,
  buffer_size = 8192L,
  chunk_size = 1024L,
  overwrite = FALSE,
  gzip_level = 0L
)

open_matrix_hdf5(path, group, buffer_size = 16384L)

Arguments

compress

Whether or not to compress the data.

dir

Directory to save the data into

buffer_size

For performance tuning only. The number of items to be buffered in memory before calling writes to disk.

overwrite

If TRUE, write to a temp dir then overwrite existing data. Alternatively, pass a temp path as a string to customize the temp dir location.

path

Path to the hdf5 file on disk

group

The group within the hdf5 file to write the data to. If writing to an existing hdf5 file this group must not already be in use

chunk_size

For performance tuning only. The chunk size used for the HDF5 array storage.

gzip_level

Gzip compression level. Default is 0 (no compression). This is recommended when both compression and compatibility with outside programs is required. Otherwise, using compress=TRUE is recommended as it is >10x faster with often similar compression levels.

matrix

Input matrix, either IterableMatrix or dgCMatrix

Details

Storage locations

Matrices can be stored in a directory on disk, in memory, or in an HDF5 file. Saving in a directory on disk is a good default for local analysis, as it provides the best I/O performance and lowest memory usage. The HDF5 format allows saving within existing hdf5 files to group data together, and the in memory format provides the fastest performance in the event memory usage is unimportant.

Bitpacking Compression

For typical RNA counts matrices holding integer counts, this bitpacking compression will result in 6-8x less space than an R dgCMatrix, and 4-6x smaller than a scipy csc_matrix. The compression will be more effective when the count values in the matrix are small, and when the rows of the matrix are sorted by rowMeans. In tests on RNA-seq data optimal ordering could save up to 40% of storage space. On non-integer data only the row indices are compressed, not the values themselves so space savings will be smaller.

For non-integer data matrices, bitpacking compression is much less effective, as it can only be applied to the indexes of each entry but not the values. There will still be some space savings, but far less than for counts matrices.

Value

BPCells matrix object