Package 'sccore' reference manual

Title:	Core Utilities for Single-Cell RNA-Seq
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.
Authors:	Viktor Petukhov [aut], Rasmus Rydbirk [aut], Peter Kharchenko [aut], Evan Biederstedt [aut, cre]
Maintainer:	Evan Biederstedt <[email protected]>
License:	GPL-3
Version:	1.0.5
Built:	2024-11-22 05:38:22 UTC
Source:	https://github.com/kharchenkolab/sccore

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

## End(Not run)

List of adjacent vertices from igraph object

Description

List of adjacent vertices from igraph object

Usage

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

## 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
)
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)
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
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
)
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)

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

## End(Not run)

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)
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)
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)
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)


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

Calculates factor-stratified sums for each column

Description

Calculates factor-stratified sums for each column

Usage

colSumByFactor(sY, rowSel)
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

Conos graph

Description

Conos graph

Usage

conosGraph
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,
  ...
)
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"))

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"))

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
)
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,
  ...
)
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

Plot embedding with provided labels / colors using ggplot2

Description

Plot embedding with provided labels / colors using ggplot2

Usage

embeddingPlot(
  embedding,
  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),
  ...
)
embeddingPlot(
  embedding,
  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),
  ...
)

Arguments

`embedding`	two-column matrix with x and y coordinates of the embedding, rownames contain cell names and are used to match coordinates with groups or colors
`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
`...`	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 that overlap too many things. 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`.

Value

ggplot2 object

Examples

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

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

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,
  ...
)
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,
  ...
)
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

Extend matrix to include new columns in matrix

Description

Extend matrix to include new columns in matrix

Usage

extendMatrix(mtx, col.names)
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)

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
)
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)

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")
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

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
)
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

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,
  ...
)
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))


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

Convert igraph graph into an adjacency list

Description

Convert igraph graph into an adjacency list

Usage

graphToAdjList(graph)
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)

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)
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'

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)
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)

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, ...)
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"

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")
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
)
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)

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

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
)
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

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", ...)
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)

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
)
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)

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")
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)

propagateLabelsSolver(conosGraph, labels=cellAnnotations)

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
)
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

## 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)
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)

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

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
)
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

Smooth Signal on Graph

Description

Smooth Signal on Graph

Usage

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

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

Description

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

Usage

sn(x)
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)

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

splitVectorByNodes

Description

splitVectorByNodes

Usage

splitVectorByNodes(vec, nodes, n.nodes)
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

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
)
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
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)
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) )

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,
  ...
)
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

Package 'sccore'

Help Index

List of adjacent vertex weights from igraph object

Description

Usage

Arguments

Value

Examples

List of adjacent vertices from igraph object

Description

Usage

Arguments

Value

Examples

Append specificity metrics to DE

Description

Usage

Arguments

Value

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

Description

Usage

Arguments

Value

Conos cell annotations

Description

Usage

Format

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

Description

Usage

Arguments

Examples

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

Description

Usage

Arguments

Value

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

Usage

Arguments

Value

Collapse Graph By Sum

Description

Usage

Arguments

Value

Examples

Calculates factor-stratified sums for each column

Description

Usage

Arguments

Value

Conos clusters list

Description

Usage

Format

Conos graph

Description

Usage

Format

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

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

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

Description

Usage

Arguments

Value

Plot embedding with provided labels / colors using ggplot2

Description

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