Title: Core Utilities for Single-Cell RNA-Seq
Version: 1.0.6
Description: Core utilities for single-cell RNA-seq data analysis. Contained within are utility functions for working with differential expression (DE) matrices and count matrices, a collection of functions for manipulating and plotting data via 'ggplot2', and functions to work with cell graphs and cell embeddings. Graph-based methods include embedding kNN cell graphs into a UMAP <doi:10.21105/joss.00861>, collapsing vertices of each cluster in the graph, and propagating graph labels.
License: GPL-3
Encoding: UTF-8
LazyData: true
Imports: dplyr, ggplot2, ggrepel, graphics, grDevices, igraph, irlba, magrittr, Matrix, methods, parallel, pbmcapply, pROC, Rcpp, rlang, scales, Seurat, stats, tibble, utils, uwot, withr
Depends: R (≥ 3.5.0)
Suggests: ggrastr (≥ 0.1.7), jsonlite, rmumps, testthat
RoxygenNote: 7.3.2
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppEigen
NeedsCompilation: yes
URL: https://github.com/kharchenkolab/sccore
BugReports: https://github.com/kharchenkolab/sccore/issues
Author: Viktor Petukhov [aut], Rasmus Rydbirk [aut], Peter Kharchenko [aut], Evan Biederstedt [aut, cre]
Maintainer: Evan Biederstedt <evan.biederstedt@gmail.com>
Packaged: 2025-04-13 18:47:19 UTC; evanbiederstedt
Repository: CRAN
Date/Publication: 2025-04-13 19:20:02 UTC

List of adjacent vertices from igraph object

Description

List of adjacent vertices from igraph object

Usage

adjacentVertices(edge_verts)

Arguments

edge_verts

edge vertices of igraph graph object

Value

list of adjacent vertices

Examples

## Not run: 
edges <- igraph::as_edgelist(conosGraph)
adjacentVertices(edges)

## End(Not run)

List of adjacent vertex weights from igraph object

Description

List of adjacent vertex weights from igraph object

Usage

adjacent_vertex_weights(edge_verts, edge_weights)

Arguments

edge_verts

edge vertices of igraph graph object

edge_weights

edge weights of igraph graph object

Value

list of adjacent vertices

Examples

## Not run: 
edges <- igraph::as_edgelist(conosGraph)
edge.weights <- igraph::edge.attributes(conosGraph)$weight
adjacent_vertex_weights(edges, edge.weights)

## End(Not run)

Append specificity metrics to DE

Description

Append specificity metrics to DE

Usage

appendSpecificityMetricsToDE(
  de.df,
  clusters,
  cluster.id,
  p2.counts,
  low.expression.threshold = 0,
  append.auc = FALSE
)

Arguments

de.df

data.frame of differential expression values

clusters

factor of clusters

cluster.id

names of 'clusters' factor. If a cluster.id doesn't exist in cluster names, an error is thrown.

p2.counts

counts from Pagoda2, refer to <https://github.com/kharchenkolab/pagoda2>

low.expression.threshold

numeric Threshold to remove expression values (default=0). Values under this threshold are discarded.

append.auc

boolean If TRUE, append AUC values (default=FALSE)

Value

data.frame of differential expression values with metrics attached

convert character vector into a factor with names "values" and "levels"

Description

convert character vector into a factor with names "values" and "levels"

Usage

as_factor(vals)

Arguments

vals

vector of values to evaluate

Value

factor with names "values" and "levels"

Conos cell annotations

Description

Conos cell annotations

Usage

cellAnnotations

Format

An object of class character of length 3000.

Check whether a package is installed and suggest how to install from CRAN, Bioconductor, or other external source

Description

Check whether a package is installed and suggest how to install from CRAN, Bioconductor, or other external source

Usage

checkPackageInstalled(
  pkgs,
  details = "to run this function",
  install.help = NULL,
  bioc = FALSE,
  cran = FALSE
)

Arguments

pkgs

character Package name(s)

details

character Helper text (default = "to run this function")

install.help

character Additional information on how to install package (default = NULL)

bioc

logical Package(s) is/are available from Bioconductor (default = FALSE)

cran

logical Package(s) is/are available from CRAN (default = FALSE)

Examples

## Not run: 
checkPackageInstalled("sccore", cran = TRUE)

## End(Not run)

Calculates factor-stratified sums for each column

Description

Calculates factor-stratified sums for each column

Usage

colSumByFactor(sY, rowSel)

Arguments

sY

sparse matrix (dgCmatrix)

rowSel

integer factor. Note that the 0-th column will return sums for any NA values; 0 or negative values will be omitted

Value

Matrix

Collapse count matrices by cell type, given min/max number of cells

Description

Collapse count matrices by cell type, given min/max number of cells

Usage

collapseCellsByType(cm, groups, min.cell.count = 10, max.cell.count = Inf)

Arguments

cm

count matrix

groups

factor specifying cell types

min.cell.count

numeric Minimum number of cells to include (default=10)

max.cell.count

numeric Maximum number of cells to include (default=Inf). If Inf, there is no maximum.

Value

Subsetted factor of collapsed cells by type, with NA cells omitted

Collapse graph using PAGA 1.2 algorithm, Wolf et al 2019, Genome Biology (2019) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x>

Description

Collapse graph using PAGA 1.2 algorithm, Wolf et al 2019, Genome Biology (2019) <https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1663-x>

Usage

collapseGraphPaga(graph, groups, linearize = TRUE, winsorize = FALSE)

Arguments

graph

igraph graph object Graph to be collapsed

groups

factor on vertices describing cluster assignment (can specify integer vertex ids, or character vertex names which will be matched)

linearize

should normally be always TRUE (default=TRUE)

winsorize

winsorize final connectivity statistics value (default=FALSE) Note: Original PAGA has it as always TRUE, but in this case there is no way to distinguish level of connectivity for highly connected groups.

Value

collapsed graph

Collapse Graph By Sum

Description

Collapse Graph By Sum

Usage

collapseGraphSum(graph, groups, normalize = TRUE)

Arguments

graph

igraph graph object Graph to be collapsed

groups

factor on vertices describing cluster assignment (can specify integer vertex ids, or character vertex names which will be matched)

normalize

boolean Whether to recalculate edge weight as observed/expected (default=TRUE)

Value

collapsed graph

Examples


collapsed = collapseGraphPaga(conosGraph, igraph::V(conosGraph), linearize=TRUE, winsorize=FALSE)

Compute Chebyshev Coefficients

Description

Compute Chebyshev Coefficients

Usage

computeChebyshevCoeffs(filt, l.max, m = 30, n = m + 1)

Arguments

filt

graph filter function

l.max

Maximum eigenvalue of the graph

m

numeric Maximum order of Chebyshev coeff to compute (default=30)

n

numeric grid order used to compute quadrature (default=m+1)

Value

vector of Chebyshev coefficients

See Also

Other graph smoothing: heatFilter(), smoothChebyshev(), smoothSignalOnGraph()

Conos clusters list

Description

Conos clusters list

Usage

conosClusterList

Format

An object of class list of length 2.

Conos graph

Description

Conos graph

Usage

conosGraph

Format

An object of class igraph of length 100.

Dot plot adapted from Seurat:::DotPlot, see ?Seurat:::DotPlot for details

Description

Dot plot adapted from Seurat:::DotPlot, see ?Seurat:::DotPlot for details

Usage

dotPlot(
  markers,
  count.matrix,
  cell.groups,
  marker.colour = "black",
  cluster.colour = "black",
  xlab = "Marker",
  ylab = "Cluster",
  n.cores = 1,
  text.angle = 45,
  gene.order = NULL,
  cols = c("blue", "red"),
  col.min = -2.5,
  col.max = 2.5,
  dot.min = 0,
  dot.scale = 6,
  scale.by = "radius",
  scale.center = FALSE,
  scale.min = NA,
  scale.max = NA,
  verbose = FALSE,
  ...
)

Arguments

markers

Vector of gene markers to plot

count.matrix

Merged count matrix, cells in rows and genes in columns

cell.groups

Named factor containing cell groups (clusters) and cell names as names

marker.colour

Character or numeric vector (default="black")

cluster.colour

Character or numeric vector (default="black")

xlab

string X-axis title (default="Marker")

ylab

string Y-axis title (default="Cluster")

n.cores

integer Number of cores (default=1)

text.angle

numeric Angle of text displayed (default=45)

gene.order

Either factor of genes passed to dplyr::mutate(levels=gene.order), or a boolean. (default=NULL) If TRUE, gene.order is set to the unique markers. If FALSE, gene.order is set to NULL. If NULL, the argument is ignored.

cols

Colors to plot (default=c("blue", "red")). The name of a palette from 'RColorBrewer::brewer.pal.info', a pair of colors defining a gradient, or 3+ colors defining multiple gradients (if 'split.by' is set).

col.min

numeric Minimum scaled average expression threshold (default=-2.5). Everything smaller will be set to this.

col.max

numeric Maximum scaled average expression threshold (default=2.5). Everything larger will be set to this.

dot.min

numeric The fraction of cells at which to draw the smallest dot (default=0). All cell groups with less than this expressing the given gene will have no dot drawn.

dot.scale

numeric Scale the size of the points, similar to cex (default=6)

scale.by

string Scale the size of the points by 'size' or by 'radius' (default="radius")

scale.center

boolean Center scaling, see 'scale()' argument 'center' (default=FALSE)

scale.min

numeric Set lower limit for scaling, use NA for default (default=NA)

scale.max

numeric Set upper limit for scaling, use NA for default (default=NA)

verbose

boolean Verbose output (default=TRUE)

...

Additional inputs passed to sccore::plapply(), see man for description.

Value

ggplot2 object

Examples

library(dplyr)
## Create merged count matrix
## In this example, cms is a list of count matrices from, e.g., Cellranger count,
## where cells are in columns and genes in rows
## cm <- sccore:::mergeCountMatrices(cms, transposed = FALSE) %>% Matrix::t()

## If coming from Conos, this can be extracted like so
## cm <- conos.obj$getJointCountMatrix(raw = FALSE) # Either normalized or raw values can be used

## Here, we create a random sparse matrix
cm <- Matrix::rsparsematrix(30,3,0.5) %>% abs(.) %>%
            `dimnames<-`(list(1:30,c("gene1","gene2","gene3")))

## Create marker vector
markers <- c("gene1","gene2","gene3")

## Additionally, color vectors can be included.
## These should have the same length as the input (markers, cell groups)
## Otherwise, they are recycled
col.markers <- c("black","black","red") # or c(1,1,2)
col.clusters <- c("black","red","black") # or c(1,2,1)

## Create annotation vector
annotation <- c(rep("cluster1",10),rep("cluster2",10),rep("cluster3",10)) %>%
    factor() %>% setNames(1:30)

## Plot. Here, the expression colours range from gray (low expression) to purple (high expression)
sccore:::dotPlot(markers = markers, count.matrix = cm, cell.groups = annotation,
    marker.colour = col.markers, cluster.colour = col.clusters, cols=c("gray","purple"))

Embed a graph into a UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction, <https://github.com/lmcinnes/umap>, <doi:10.21105/joss.00861>

Description

Embed a graph into a UMAP, Uniform Manifold Approximation and Projection for Dimension Reduction, <https://github.com/lmcinnes/umap>, <doi:10.21105/joss.00861>

Usage

embedGraphUmap(
  graph,
  min.prob = 0.001,
  min.visited.verts = 1000,
  n.cores = 1,
  max.hitting.nn.num = 0,
  max.commute.nn.num = 0,
  min.prob.lower = 1e-07,
  n.neighbors = 40,
  n.epochs = 1000,
  spread = 15,
  min.dist = 0.001,
  return.all = FALSE,
  n.sgd.cores = n.cores,
  verbose = TRUE,
  ...
)

Arguments

graph

input igraph object

min.prob

numeric Minimum probability for proximity when calculating hitting time per neighbors (default=1e-3)

min.visited.verts

numeric Minimum number of vertices visted when calculating hitting time per neighbors (default=1000)

n.cores

numeric Number of cores to use (default=1)

max.hitting.nn.num

numeric Maximum adjacencies for calculating hitting time per neighbor, hitting_time_per_neighbors() (default=0)

max.commute.nn.num

numeric Maximum adjacencies for calculating commute time per neighbor, commute_time_per_node() (default=0)

min.prob.lower

numeric Probability threshold to continue iteration in depth first search hitting time, dfs_hitting_time() (default=1e-7)

n.neighbors

numeric Number of neighbors (default=40)

n.epochs

numeric Number of epochs to use during the optimization of the embedded coordinates (default=1000). See 'n_epochs' in uwot::umap()

spread

numeric The effective scale of embedded points (numeric default=15). See 'spread' in uwot::umap()

min.dist

numeric The effective minimum distance between embedded points (default=0.001). See 'min.dist' in uwot::umap()

return.all

boolean If TRUE, return list(adj.info=adj.info, commute.times=commute.times, umap=umap). Otherwise, just return UMAP(default=FALSE)

n.sgd.cores

numeric Number of cores to use during stochastic gradient descent. If set to > 1, then results will not be reproducible, even if 'set.seed' is called with a fixed seed before running (default=n_threads) See 'n_sgd_threads' in uwot::umap()

verbose

boolean Verbose output (default=TRUE)

...

Additional arguments passed to embedKnnGraph()

Value

resulting UMAP embedding

Embed a k-nearest neighbor (kNN) graph within a UMAP. Used within embedGraphUmap(). Please see McInnes et al <doi:10.21105/joss.00861> for the UMAP description and implementation.

Description

Embed a k-nearest neighbor (kNN) graph within a UMAP. Used within embedGraphUmap(). Please see McInnes et al <doi:10.21105/joss.00861> for the UMAP description and implementation.

Usage

embedKnnGraph(
  commute.times,
  n.neighbors,
  names = NULL,
  n.cores = 1,
  n.epochs = 1000,
  spread = 15,
  min.dist = 0.001,
  n.sgd.cores = n.cores,
  target.dims = 2,
  verbose = TRUE,
  ...
)

Arguments

commute.times

graph commute times from get_nearest_neighbors(). The definition of commute_time(u, v) is the expected time starting at u = to reach v and then return to u .

n.neighbors

numeric Number of neighbors

names

vector of names for UMAP rownames (default=NULL)

n.cores

numeric Number of cores to use (except during stochastic gradient descent) (default=1). See 'n_threads' in uwot::umap()

n.epochs

numeric Number of epochs to use during the optimization of the embedded coordinates (default=1000). See 'n_epochs' in uwot::umap()

spread

numeric The effective scale of embedded points (numeric default=15). See 'spread' in uwot::umap()

min.dist

numeric The effective minimum distance between embedded points (default=0.001). See 'min.dist' in uwot::umap()

n.sgd.cores

numeric Number of cores to use during stochastic gradient descent. If set to > 1, then results will not be reproducible, even if 'set.seed' is called with a fixed seed before running (default=n.cores) See 'n_sgd_threads' in uwot::umap()

target.dims

numeric Dimensions for 'n_components' in uwot::umap(n_components=target.dims) (default=2)

verbose

boolean Verbose output (default=TRUE)

...

arguments passed to uwot::umap()

Value

resulting kNN graph embedding within a UMAP

Set colors for embedding plot. Used primarily in embeddingPlot().

Description

Set colors for embedding plot. Used primarily in embeddingPlot().

Usage

embeddingColorsPlot(
  plot.df,
  colors,
  groups = NULL,
  geom_point_w = ggplot2::geom_point,
  gradient.range.quantile = 1,
  color.range = "symmetric",
  legend.title = NULL,
  palette = NULL,
  plot.na = TRUE
)

Arguments

plot.df

data.frame for plotting. In embeddingPlot(), this is a tibble from tibble::rownames_to_column().

colors

vector of numbers, which must be shown with point colors, names contain cell names (default=NULL). This argument is ignored if groups are provided.

groups

vector of cluster labels, names contain cell names (default=NULL)

geom_point_w

function to work with geom_point layer from ggplot2 (default=ggplot2::geom_point)

gradient.range.quantile

Winsorization quantile for the numeric colors and gene gradient (default=1)

color.range

controls range, in which colors are estimated (default="symmetric"). Pass "all" to estimate range based on all values of "colors", pass "data" to estimate it only based on colors, presented in the embedding. Alternatively you can pass vector of length 2 with (min, max) values.

legend.title

legend title (default=NULL)

palette

vector or list or function (default=NULL). Accepts number of colors and return list of colors (i.e. see 'colorRampPalette') (default=NULL)

plot.na

boolean/numeric Whether to plot points, for which groups / colors are missed (default=is.null(subgroups), i.e. FALSE). If plot.na passed a numeric value below 0, the NA symbols are plotted below the cells. Otherwise if values >=0, they’re plotted above the cells. Note that this argument is FALSE if 'subgroups' is NULL

Value

ggplot2 object

Plotting function for cluster labels, names contain cell names. Used primarily in embeddingPlot().

Description

Plotting function for cluster labels, names contain cell names. Used primarily in embeddingPlot().

Usage

embeddingGroupPlot(
  plot.df,
  groups,
  geom_point_w,
  min.cluster.size,
  mark.groups,
  font.size,
  legend.title,
  shuffle.colors,
  palette,
  plot.na,
  ...
)

Arguments

plot.df

data.frame for plotting. In embeddingPlot(), this is a tibble from tibble::rownames_to_column().

groups

vector of cluster labels, names contain cell names (default=NULL)

geom_point_w

function to work with geom_point layer from ggplot2 (default=ggplot2::geom_point)

min.cluster.size

labels for all groups with number of cells fewer than this parameter are considered as missed (default=0). This argument is ignored if groups aren't provided

mark.groups

plot cluster labels above points (default=TRUE)

font.size

font size for cluster labels (default=c(3, 7)). It can either be single number for constant font size or pair (min, max) for font size depending on cluster size

legend.title

legend title (default=NULL)

shuffle.colors

shuffle colors (default=FALSE)

palette

vector or list or function (default=NULL). Accepts number of colors and return list of colors (i.e. see 'colorRampPalette') (default=NULL)

plot.na

boolean/numeric Whether to plot points, for which groups / colors are missed (default=is.null(subgroups), i.e. FALSE). If plot.na passed a numeric value below 0, the NA symbols are plotted below the cells. Otherwise if values >=0, they’re plotted above the cells. Note that this argument is FALSE if 'subgroups' is NULL

...

Additional arguments passed to ggplot2::geom_label_repel()

Value

ggplot2 object

embeddingPlot generic Plot embedding with provided labels / colors using ggplot2

Description

embeddingPlot generic Plot embedding with provided labels / colors using ggplot2

Plot embedding with provided labels / colors using ggplot2

Plot embedding from Seurat object

Usage

embeddingPlot(object, ...)

## S4 method for signature 'ANY'
embeddingPlot(
  object,
  groups = NULL,
  colors = NULL,
  subgroups = NULL,
  plot.na = is.null(subgroups),
  min.cluster.size = 0,
  mark.groups = TRUE,
  show.legend = FALSE,
  alpha = 0.4,
  size = 0.8,
  title = NULL,
  plot.theme = NULL,
  palette = NULL,
  color.range = "symmetric",
  font.size = c(3, 7),
  show.ticks = FALSE,
  show.labels = FALSE,
  legend.position = NULL,
  legend.title = NULL,
  gradient.range.quantile = 1,
  raster = FALSE,
  raster.dpi = 300,
  shuffle.colors = FALSE,
  keep.limits = !is.null(subgroups),
  ...
)

## S4 method for signature 'Seurat'
embeddingPlot(object, reduction = NULL, groups = NULL, colors = NULL, ...)

Arguments

object

Seurat object

...

Arguments passed on to ggrepel::geom_label_repel

mapping

Set of aesthetic mappings created by aes or aes_. If specified and inherit.aes = TRUE (the default), is combined with the default mapping at the top level of the plot. You only need to supply mapping if there isn't a mapping defined for the plot.

data

A data frame. If specified, overrides the default data frame defined at the top level of the plot.

stat

The statistical transformation to use on the data for this layer, as a string.

position

Position adjustment, either as a string, or the result of a call to a position adjustment function.

parse

If TRUE, the labels will be parsed into expressions and displayed as described in ?plotmath

box.padding

Amount of padding around bounding box, as unit or number. Defaults to 0.25. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

label.padding

Amount of padding around label, as unit or number. Defaults to 0.25. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

point.padding

Amount of padding around labeled point, as unit or number. Defaults to 0. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

label.r

Radius of rounded corners, as unit or number. Defaults to 0.15. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

label.size

Size of label border, in mm.

min.segment.length

Skip drawing segments shorter than this, as unit or number. Defaults to 0.5. (Default unit is lines, but other units can be specified by passing unit(x, "units")).

arrow

specification for arrow heads, as created by arrow

force

Force of repulsion between overlapping text labels. Defaults to 1.

force_pull

Force of attraction between a text label and its corresponding data point. Defaults to 1.

max.time

Maximum number of seconds to try to resolve overlaps. Defaults to 0.5.

max.iter

Maximum number of iterations to try to resolve overlaps. Defaults to 10000.

max.overlaps

Exclude text labels when they overlap too many other things. For each text label, we count how many other text labels or other data points it overlaps, and exclude the text label if it has too many overlaps. Defaults to 10.

nudge_x,nudge_y

Horizontal and vertical adjustments to nudge the starting position of each text label. The units for nudge_x and nudge_y are the same as for the data units on the x-axis and y-axis.

xlim,ylim

Limits for the x and y axes. Text labels will be constrained to these limits. By default, text labels are constrained to the entire plot area.

na.rm

If FALSE (the default), removes missing values with a warning. If TRUE silently removes missing values.

direction

"both", "x", or "y" – direction in which to adjust position of labels

seed

Random seed passed to set.seed. Defaults to NA, which means that set.seed will not be called.

verbose

If TRUE, some diagnostics of the repel algorithm are printed

inherit.aes

If FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g. borders.

groups

vector of cluster labels, names contain cell names (default=NULL)

colors

vector of numbers, which must be shown with point colors, names contain cell names (default=NULL). This argument is ignored if groups are provided.

subgroups

subset of 'groups', selecting the cells for plot (default=NULL). Ignored if 'groups' is NULL

plot.na

boolean/numeric Whether to plot points, for which groups / colors are missed (default=is.null(subgroups), i.e. FALSE). If plot.na passed a numeric value below 0, the NA symbols are plotted below the cells. Otherwise if values >=0, they’re plotted above the cells. Note that this argument is FALSE if 'subgroups' is NULL

min.cluster.size

labels for all groups with number of cells fewer than this parameter are considered as missed (default=0). This argument is ignored if groups aren't provided

mark.groups

plot cluster labels above points (default=TRUE)

show.legend

show legend (default=FALSE)

alpha

opacity level [0, 1] (default=0.4)

size

point size (default=0.8)

title

plot title (default=NULL)

plot.theme

theme for the plot (default=NULL)

palette

vector or list or function (default=NULL). Accepts number of colors and return list of colors (i.e. see 'colorRampPalette') (default=NULL)

color.range

controls range, in which colors are estimated (default="symmetric"). Pass "all" to estimate range based on all values of "colors", pass "data" to estimate it only based on colors, presented in the embedding. Alternatively you can pass vector of length 2 with (min, max) values.

font.size

font size for cluster labels (default=c(3, 7)). It can either be single number for constant font size or pair (min, max) for font size depending on cluster size

show.ticks

show ticks and tick labels (default=FALSE)

show.labels

show labels (default=FALSE)

legend.position

vector with (x, y) positions of the legend (default=NULL)

legend.title

legend title (default=NULL)

gradient.range.quantile

Winsorization quantile for the numeric colors and gene gradient (default=1)

raster

boolean whether layer with the points be rasterized (default=FALSE). Setting of this argument to TRUE is useful when you need to export a plot with large number of points

raster.dpi

dpi of the rasterized plot. (default=300). Ignored if raster == FALSE.

shuffle.colors

shuffle colors (default=FALSE)

keep.limits

Keep axis limits from original plot (default=!is.null(subgroups)). Useful when plotting subgroups, only meaningful it plot.na=FALSE

reduction

Reduction to use for embedding (default=NULL)

Value

ggplot2 object

ggplot2 object

Examples

library(sccore)
embeddingPlot(umapEmbedding, show.ticks=TRUE, show.labels=TRUE, title="UMAP embedding")

## Not run: 
# so = example Seurat object
embeddingPlot(so, groups="seurat_clusters", reduction="umap")

## End(Not run)

Extend matrix to include new columns in matrix

Description

Extend matrix to include new columns in matrix

Usage

extendMatrix(mtx, col.names)

Arguments

mtx

Matrix

col.names

Columns that should be included in matrix

Value

Matrix with new columns but rows retained

Examples

library(dplyr)
gene.union <- lapply(conosClusterList, colnames) %>% Reduce(union, .)
extendMatrix(conosClusterList[[1]], col.names=gene.union)

Utility function to translate a factor into colors

Description

Utility function to translate a factor into colors

Usage

fac2col(
  x,
  s = 1,
  v = 1,
  shuffle = FALSE,
  min.group.size = 1,
  return.details = FALSE,
  unclassified.cell.color = "gray50",
  level.colors = NULL
)

Arguments

x

input factor

s

numeric The "saturation" to be used to complete the HSV color descriptions (default=1) See ?rainbow in Palettes, grDevices

v

numeric The "value" to be used to complete the HSV color descriptions (default=1) See ?rainbow in Palettes, grDevices

shuffle

boolean If TRUE, shuffles columns with shuffle(columns) (default=FALSE)

min.group.size

integer Exclude groups of size less than the min.group.size (default=1)

return.details

boolean If TRUE, returns a list list(colors=y, palette=col). Otherwise, just returns the factor (default=FALSE)

unclassified.cell.color

Color for unclassified cells (default='gray50')

level.colors

(default=NULL)

Value

vector or list of colors

Examples

genes = factor(c("BRAF", "NPC1", "PAX3", "BRCA2", "FMR1"))
fac2col(genes)

Encodes logic of how to handle named-vector and functional palettes. Used primarily within embeddingGroupPlot()

Description

Encodes logic of how to handle named-vector and functional palettes. Used primarily within embeddingGroupPlot()

Usage

fac2palette(groups, palette, unclassified.cell.color = "gray50")

Arguments

groups

vector of cluster labels, names contain cell names

palette

vector or list or function (default=NULL). Accepts number of colors and return list of colors (i.e. see 'colorRampPalette')

unclassified.cell.color

Color for unclassified cells (default='gray50')

Value

vector or palette

Collapse vertices belonging to each cluster in a graph

Description

Collapse vertices belonging to each cluster in a graph

Usage

getClusterGraph(
  graph,
  groups,
  method = "sum",
  plot = FALSE,
  node.scale = 50,
  edge.scale = 50,
  edge.alpha = 0.3,
  seed = 1,
  ...
)

Arguments

graph

igraph graph object Graph to be collapsed

groups

factor on vertices describing cluster assignment (can specify integer vertex ids, or character vertex names which will be matched)

method

string Method to be used, either "sum" or "paga" (default="sum")

plot

boolean Whether to show collapsed graph plot (default=FALSE)

node.scale

numeric Scaling to control value of 'vertex.size' in plot.igraph() (default=50)

edge.scale

numeric Scaling to control value of 'edge.width' in plot.igraph() (default=50)

edge.alpha

numeric Scaling to control value of 'alpha.f' in adjustcolor() within plot.igraph() (default=0.3)

seed

numeric Set seed via set.seed() for plotting (default=1)

...

arguments passed to collapseGraphSum()

Value

collapsed graph

Examples


cluster.graph = getClusterGraph(conosGraph, igraph::V(conosGraph))

Get nearest neighbors method on graph

Description

Get nearest neighbors method on graph

Usage

get_nearest_neighbors(
  adjacency_list,
  transition_probabilities,
  n_verts = 0L,
  n_cores = 1L,
  min_prob = 0.001,
  min_visited_verts = 1000L,
  min_prob_lower = 1e-05,
  max_hitting_nn_num = 0L,
  max_commute_nn_num = 0L,
  verbose = TRUE
)

Arguments

adjacency_list

igraph adjacency list

transition_probabilities

vector of transition probabilites

n_verts

numeric Number of vertices (default=0)

n_cores

numeric Number of cores to use (default=1)

min_prob

numeric Minimum probability for proximity when calculating hitting time per neighbors (default=1e-3)

min_visited_verts

numeric Minimum number of vertices visted when calculating hitting time per neighbors (default=1000)

min_prob_lower

numeric Probability threshold to continue iteration in depth first search hitting time, dfs_hitting_time() (default=1e-5)

max_hitting_nn_num

numeric Maximum adjacencies for calculating hitting time per neighbor, hitting_time_per_neighbors() (default=0)

max_commute_nn_num

numeric Maximum adjacencies for calculating commute time per neighbor, commute_time_per_node() (default=0)

verbose

boolean Whether to have verbose output (default=TRUE)

Value

list of commute times based on adjacencies

Convert igraph graph into an adjacency list

Description

Convert igraph graph into an adjacency list

Usage

graphToAdjList(graph)

Arguments

graph

input igraph object

Value

adjacency list, defined by list(idx=adj.list, probabilities=probs, names=edge.list.fact$levels

Examples

library(dplyr)
edge.list.fact <- igraph::as_edgelist(conosGraph) %>% as_factor()
edge.list <- matrix(edge.list.fact$values, ncol=2)
n.nodes <- length(igraph::V(conosGraph))
splitVectorByNodes(edge.list[,1], edge.list[,2], n.nodes)

Graph filter with the heat kernel: f(x) = exp(-\beta |x / \lambda_m - a|^b)

Description

Graph filter with the heat kernel: f(x) = exp(-\beta |x / \lambda_m - a|^b)

Usage

heatFilter(x, l.max, order = 1, offset = 0, beta = 30)

Arguments

x

numeric Values to be filtered. Normally, these are graph laplacian engenvalues.

l.max

numeric Maximum eigenvalue on the graph (\lambda_m in the equation)

order

numeric Parameter b in the equation. Larger values correspond to the sharper kernel form (default=1). The values should be positive.

offset

numeric Mean kernel value (a in the equation), must be in [0:1] (default=0)

beta

numeric Parameter \beta in the equation. Larger values provide stronger smoothing. \beta=0 corresponds to no smoothing (default=30).

Value

smoothed values for 'x'

See Also

Other graph smoothing: computeChebyshevCoeffs(), smoothChebyshev(), smoothSignalOnGraph()

Jensen–Shannon distance metric (i.e. the square root of the Jensen–Shannon divergence) between the columns of a dense matrix m

Description

Jensen–Shannon distance metric (i.e. the square root of the Jensen–Shannon divergence) between the columns of a dense matrix m

Usage

jsDist(m)

Arguments

m

Input matrix

Value

Vectorized version of the lower triangle as an R distance object, stats::dist()

Examples

ex = matrix(1:9, nrow = 3, ncol = 3)
jsDist(ex)

Merge list of count matrices into a common matrix, entering 0s for the missing entries

Description

Merge list of count matrices into a common matrix, entering 0s for the missing entries

Usage

mergeCountMatrices(cms, transposed = FALSE, ...)

Arguments

cms

List of count matrices

transposed

boolean Indicate whether 'cms' is transposed, e.g. cells in rows and genes in columns (default=FALSE)

...

Parameters for 'plapply' function

Value

A merged extended matrix, with 0s for missing entries

Examples

mergeCountMatrices(conosClusterList, n.cores=1)
## 12 x 67388 sparse Matrix of class "dgCMatrix"

Translate multilevel segmentation into a dendrogram, with the lowest level of the dendrogram listing the cells

Description

Translate multilevel segmentation into a dendrogram, with the lowest level of the dendrogram listing the cells

Usage

multi2dend(cl, counts, deep = FALSE, dist = "cor")

Arguments

cl

igraph communities object, returned from igraph community detection functions

counts

dgCmatrix of counts

deep

boolean If TRUE, take (cl$memberships[1,]). Otherwise, uses as.integer(membership(cl)) (default=FALSE)

dist

Distance metric used (default='cor'). Eiether 'cor' for the correlation distance in log10 space, or 'JS' for the Jensen–Shannon distance metric (i.e. the square root of the Jensen–Shannon divergence)

Value

resulting dendrogram

Parallel, optionally verbose lapply. See ?parallel::mclapply for more info.

Description

Parallel, optionally verbose lapply. See ?parallel::mclapply for more info.

Usage

plapply(
  ...,
  progress = FALSE,
  n.cores = parallel::detectCores(),
  mc.preschedule = FALSE,
  mc.allow.recursive = TRUE,
  fail.on.error = FALSE
)

Arguments

...

Additional arguments passed to mclapply(), lapply(), or pbmcapply::pbmclapply()

progress

Show progress bar via pbmcapply::pbmclapply() (default=FALSE).

n.cores

Number of cores to use (default=parallel::detectCores()). When n.cores=1, regular lapply() is used. Note: doesn't work when progress=TRUE

mc.preschedule

if set to TRUE then the computation is first divided to (at most) as many jobs are there are cores and then the jobs are started, each job possibly covering more than one value. If set to FALSE then one job is forked for each value of X. The former is better for short computations or large number of values in X, the latter is better for jobs that have high variance of completion time and not too many values of X compared to mc.cores.

mc.allow.recursive

boolean Unless true, calling mclapply in a child process will use the child and not fork again (default=TRUE)

fail.on.error

boolean Whether to fail and report and error (using stop()) as long as any of the individual tasks has failed (default =FALSE)

Value

list, as returned by lapply

Examples

square = function(x){ x**2 }
plapply(1:10, square, n.cores=1, progress=TRUE)

Estimate labeling distribution for each vertex, based on provided labels.

Description

Estimate labeling distribution for each vertex, based on provided labels.

Usage

propagateLabels(graph, labels, method = "diffusion", ...)

Arguments

graph

igraph graph object

labels

vector of factor or character labels, named by cell names, used in propagateLabelsSolver() and propagateLabelsDiffusion()

method

string Type of propagation. Either 'diffusion' or 'solver'. (default='diffusion') 'solver' gives better result but has bad asymptotics, so it is inappropriate for datasets > 20k cells.

...

additional arguments passed to either propagateLabelsSolver() or propagateLabelsDiffusion()

Value

matrix with distribution of label probabilities for each vertex by rows.

Examples

propagateLabels(conosGraph, labels=cellAnnotations)

Estimate labeling distribution for each vertex, based on provided labels using a Random Walk on graph

Description

Estimate labeling distribution for each vertex, based on provided labels using a Random Walk on graph

Usage

propagateLabelsDiffusion(
  graph,
  labels,
  max.iters = 100,
  diffusion.fading = 10,
  diffusion.fading.const = 0.1,
  tol = 0.025,
  fixed.initial.labels = TRUE,
  verbose = TRUE
)

Arguments

graph

igraph graph object Graph input

labels

vector of factor or character labels, named by cell names

max.iters

integer Maximal number of iterations (default=100)

diffusion.fading

numeric Constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=10.0)

diffusion.fading.const

numeric Another constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=0.1)

tol

numeric Absolute tolerance as a stopping criteria (default=0.025)

fixed.initial.labels

boolean Prohibit changes of initial labels during diffusion (default=TRUE)

verbose

boolean Verbose mode (default=TRUE)

Value

matrix from input graph, with labels propagated

Examples

propagateLabelsDiffusion(conosGraph, labels=cellAnnotations)

Propagate labels using Zhu, Ghahramani, Lafferty (2003) algorithm, "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions" <http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf>

Description

Propagate labels using Zhu, Ghahramani, Lafferty (2003) algorithm, "Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions" <http://mlg.eng.cam.ac.uk/zoubin/papers/zgl.pdf>

Usage

propagateLabelsSolver(graph, labels, solver = "mumps")

Arguments

graph

igraph graph object Graph input

labels

vector of factor or character labels, named by cell names

solver

Method of solver to use (default="mumps"), either "Matrix" or "mumps" (i.e. "rmumps::Rmumps")

Value

result from Matrix::solve() or rmumps::Rmumps

Examples


propagateLabelsSolver(conosGraph, labels=cellAnnotations)

Label propagation

Description

Label propagation

Usage

propagate_labels(
  edge_verts,
  edge_weights,
  vert_labels,
  max_n_iters = 10L,
  verbose = TRUE,
  diffusion_fading = 10,
  diffusion_fading_const = 0.5,
  tol = 0.005,
  fixed_initial_labels = FALSE
)

Arguments

edge_verts

edge vertices of igraph graph object

edge_weights

edge weights of igraph graph object

vert_labels

vector of factor or character labels, named by cell names

max_n_iters

integer Maximal number of iterations (default=10)

verbose

boolean Verbose mode (default=TRUE)

diffusion_fading

numeric Constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=10.0)

diffusion_fading_const

numeric Another constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=0.5)

tol

numeric Absolute tolerance as a stopping criteria (default=5e-3)

fixed_initial_labels

boolean Prohibit changes of initial labels during diffusion (default=FALSE)

Value

matrix from input graph, with labels propagated

Save DE results as JSON tables for viewing in browser

Description

Save DE results as JSON tables for viewing in browser

Usage

saveDeAsJson(
  de.raw,
  sample.groups = NULL,
  saveprefix = NULL,
  dir.name = "JSON",
  gene.metadata = NULL,
  verbose = TRUE
)

Arguments

de.raw

List of DE results from e.g. cacoa, conos

sample.groups

Sample groups as named list, each element containing a vector of samples. Can be retrieved from e.g. package cacoa (default=NULL)

saveprefix

Prefix for created files (default=NULL)

dir.name

Name for directory with results. If it doesn't exist, it will be created. To disable, set as NULL (default="JSON")

gene.metadata

(default=NULL) # Needs explanation

verbose

Show progress (default=TRUE)

Value

JSON files, table of content, and viewer files for viewing DE results in browser

Examples

## Not run: 
saveDeAsJson(de.raw, sample.groups)

## End(Not run)
## The results can be viewed in a webbrowser by opening toc.html

Set range for values in object. Changes values outside of range to min or max. Adapted from Seurat::MinMax

Description

Set range for values in object. Changes values outside of range to min or max. Adapted from Seurat::MinMax

Usage

setMinMax(obj, min, max)

Arguments

obj

Object to manipulate

min

Minimum value

max

Maximum value

Value

An object with the same dimensions as input but with altered range in values

Examples

example_matrix =  matrix(rep(c(1:5), 3), 5)
setMinMax(example_matrix, 2, 4)

Smooth with Chebyshev Polynomials

Description

Smooth with Chebyshev Polynomials

Usage

smoothChebyshev(
  lap,
  coeffs,
  signal,
  l.max,
  n.cores = 1,
  progress.chunks = 5,
  progress = (progress.chunks > 1)
)

Arguments

lap

graph laplacian

coeffs

numeric vector Chebyshev coefficients for a filter

signal

Matrix or vector Signal to smooth

l.max

numeric maximal eigenvalue of the graph

n.cores

numeric Number of cores for parallel run (default=1)

progress.chunks

numeric Number of chunks per core for estimating progress (default=5). Large values are not suggested, as it may bring overhead.

progress

boolean Flag on whether progress must be shown (default=TRUE, i.e. 'progress.chunks > 1')

Value

smoothed signal

See Also

Other graph smoothing: computeChebyshevCoeffs(), heatFilter(), smoothSignalOnGraph()

Smooth Signal on Graph

Description

Smooth Signal on Graph

Usage

smoothSignalOnGraph(
  signal,
  filter,
  graph = NULL,
  lap = NULL,
  l.max = NULL,
  m = 50,
  ...
)

Arguments

signal

signal to be smoothed

filter

function that accepts signal 'x' and the maximal Laplacian eigenvalue 'l.max'. See heatFilter as an example.

graph

igraph object with the graph (default=NULL)

lap

graph laplacian (default=NULL). If NULL, 'lap' estimated from graph.

l.max

maximal eigenvalue of 'lap' (default=NULL). If NULL, estimated from 'lap'.

m

numeric Maximum order of Chebyshev coeff to compute (default=50)

...

Arguments passed on to smoothChebyshev

n.cores

numeric Number of cores for parallel run (default=1)

progress.chunks

numeric Number of chunks per core for estimating progress (default=5). Large values are not suggested, as it may bring overhead.

progress

boolean Flag on whether progress must be shown (default=TRUE, i.e. 'progress.chunks > 1')

See Also

Other graph smoothing: computeChebyshevCoeffs(), heatFilter(), smoothChebyshev()

Smooth gene expression, used primarily within conos::correctGenes. Used to smooth gene expression values in order to better represent the graph structure. Use diffusion of expression on graph with the equation dv = exp(-a * (v + b))

Description

Smooth gene expression, used primarily within conos::correctGenes. Used to smooth gene expression values in order to better represent the graph structure. Use diffusion of expression on graph with the equation dv = exp(-a * (v + b))

Usage

smooth_count_matrix(
  edge_verts,
  edge_weights,
  count_matrix,
  is_label_fixed,
  max_n_iters = 10L,
  diffusion_fading = 1,
  diffusion_fading_const = 0.1,
  tol = 0.001,
  verbose = TRUE,
  normalize = FALSE
)

Arguments

edge_verts

edge vertices of igraph graph object

edge_weights

edge weights of igraph graph object

count_matrix

gene count matrix

is_label_fixed

boolean Whether label is fixed

max_n_iters

integer Maximal number of iterations (default=10)

diffusion_fading

numeric Constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=1.0)

diffusion_fading_const

numeric Another constant used for diffusion on the graph, exp(-diffusion.fading * (edge_length + diffusion.fading.const)) (default=0.1)

tol

numeric Absolute tolerance as a stopping criteria (default=1e-3)

verbose

boolean Verbose mode (default=TRUE)

normalize

boolean Whether to normalize values (default=FALSE)

Value

matrix from input graph, with labels propagated

Set names equal to values, a stats::setNames wrapper function

Description

Set names equal to values, a stats::setNames wrapper function

Usage

sn(x)

Arguments

x

an object for which names attribute will be meaningful

Value

An object with names assigned equal to values

Examples

vec = c(1, 2, 3, 4)
sn(vec)

splitVectorByNodes

Description

splitVectorByNodes

Usage

splitVectorByNodes(vec, nodes, n.nodes)

Arguments

vec

input vector to be divided

nodes

nodes used to divide the vector 'vec' via split()

n.nodes

numeric The number of nodes for splitting

Value

list from vec with names given by the nodes

Examples

adjList = graphToAdjList(conosGraph)
print(names(adjList))
## [1] "idx" "probabilities" "names" 
length(adjList$names)
## [1] 12000

Set plot.theme, legend, ticks for embedding plot. Used primarily in embeddingPlot().

Description

Set plot.theme, legend, ticks for embedding plot. Used primarily in embeddingPlot().

Usage

styleEmbeddingPlot(
  gg,
  plot.theme = NULL,
  title = NULL,
  legend.position = NULL,
  show.legend = TRUE,
  show.ticks = TRUE,
  show.labels = TRUE,
  relabel.axis = TRUE
)

Arguments

gg

ggplot2 object to plot

plot.theme

theme for the plot (default=NULL)

title

plot title (default=NULL)

legend.position

vector with (x, y) positions of the legend (default=NULL)

show.legend

show legend (default=TRUE)

show.ticks

show ticks and tick labels (default=TRUE)

show.labels

show labels (default=TRUE)

relabel.axis

boolean If TRUE, relabel axes with ggplot2::labs(x='Component 1', y='Component 2') (default=TRUE)

Value

ggplot2 object

UMAP embedding

Description

UMAP embedding

Usage

umapEmbedding

Format

An object of class matrix (inherits from array) with 12000 rows and 2 columns.

Utility function to translate values into colors.

Description

Utility function to translate values into colors.

Usage

val2col(x, gradientPalette = NULL, zlim = NULL, gradient.range.quantile = 0.95)

Arguments

x

input values

gradientPalette

gradient palette (default=NULL). If NULL, use colorRampPalette(c('gray90','red'), space = "Lab")(1024) if the values are non-negative; otherwise colorRampPalette(c("blue", "grey90", "red"), space = "Lab")(1024) is used

zlim

a two-value vector specifying limits of the values that should correspond to the extremes of the color gradient

gradient.range.quantile

extreme quantiles of values that should be trimmed prior to color mapping (default=0.95)

Examples

colors <- val2col( rnorm(10) )

Helper function to return a ggplot color gradient for a numeric vector ggplot(aes(color=x, ...), ...) + val2ggcol(x)

Description

Helper function to return a ggplot color gradient for a numeric vector ggplot(aes(color=x, ...), ...) + val2ggcol(x)

Usage

val2ggcol(
  values,
  gradient.range.quantile = 1,
  color.range = "symmetric",
  palette = NULL,
  midpoint = NULL,
  oob = scales::squish,
  return.fill = FALSE,
  ...
)

Arguments

values

values by which the color gradient is determined

gradient.range.quantile

numeric Trimming quantile (default=1). Either a single number or two numbers - for lower and upper quantile.

color.range

either a vector of two values explicitly specifying the values corresponding to the start/end of the gradient, or string "symmetric" or "all" (default="symmetric"). "symmetric": range will fit data, but will be symmetrized around zeros, "all": gradient will match the span of the range of the data (after gradient.range.quantile)

palette

an optional palette (default=NULL). The default becomes blue-gray90-red; if the values do not straddle 0, then truncated gradients (blue-gray90 or gray90-red) will be used

midpoint

optional midpoint (default=NULL). Set for the center of the resulting range by default

oob

function to determine what to do with the values outside of the range (default =scales::squish). Refer to 'oob' parameter in ggplot

return.fill

boolean Whether to return fill gradients instead of color (default=FALSE)

...

additional arguments are passed to ggplot2::scale_color_gradient* functions, i.e. scale_color_gradient(), scale_color_gradient2(), scale_color_gradientn()

Value

ggplot2::scale_colour_gradient object