Package 'cytosignal' reference manual

Title:	What the Package Does (One Line, Title Case)
Description:	What the package does (one paragraph).
Authors:	Jialin Liu [aut, cre], Yichen Wang [aut]
Maintainer:	Jialin Liu <[email protected]>
License:	GPL (>= 3)
Version:	0.2.0
Built:	2024-11-19 06:16:52 UTC
Source:	https://github.com/welch-lab/cytosignal

#' Compute the LR velo for specific ligand-receptor imputation obj pairs #' #' @param object A Cytosignal object #' @param lig.slot The ligand slot to use #' @param recep.slot The receptor slot to use #' @param intr.db.name The intr database name to use #' @param nn.use The neighbor index as niche #' #' @return A Cytosignal object #' @export #' inferVeloLR <- function( object, ... ) UseMethod(generic = 'inferVeloLR', object = object)

Description

#' Compute the LR velo for specific ligand-receptor imputation obj pairs #' #' @param object A Cytosignal object #' @param lig.slot The ligand slot to use #' @param recep.slot The receptor slot to use #' @param intr.db.name The intr database name to use #' @param nn.use The neighbor index as niche #' #' @return A Cytosignal object #' @export #' inferVeloLR <- function( object, ... ) UseMethod(generic = 'inferVeloLR', object = object)

Usage

.inferVeloLR.matrix_like(
  dge.lig,
  dge.recep,
  dge.lig.velo,
  dge.recep.velo,
  lig.fac,
  recep.fac
)
.inferVeloLR.matrix_like(
  dge.lig,
  dge.recep,
  dge.lig.velo,
  dge.recep.velo,
  lig.fac,
  recep.fac
)

Extract metadata variable from mergedCytoSignal object

Description

Fast expression for getting and setting metadata variables in a mergedCytoSignal object.

Usage

## S3 method for class 'mergedCytoSignal'
x$name

## S3 replacement method for class 'mergedCytoSignal'
x$name <- value
## S3 method for class 'mergedCytoSignal'
x$name

## S3 replacement method for class 'mergedCytoSignal'
x$name <- value

Add interaction database to CytoSignal object

Description

Add interaction database to CytoSignal object

Usage

addIntrDB(
  object,
  gene_to_uniprot,
  intr.db.diff_dep,
  intr.db.cont_dep,
  inter.index
)
addIntrDB(
  object,
  gene_to_uniprot,
  intr.db.diff_dep,
  intr.db.cont_dep,
  inter.index
)

Arguments

`object`	CytoSignal object
`gene_to_uniprot`	df, gene to uniprot symbols mapping
`intr.db.diff_dep`	intr.db for diffusable ligands + non-diffusable receptors
`intr.db.cont_dep`	intr.db for contact dependent ligands + receptors
`inter.index`	df, collection of intraction information

Value

a CytoSignal object

Add velocity data to CytoSignal object

Description

Add velocity data to CytoSignal object

Usage

addVelo(object, velo.s, velo.u)
addVelo(object, velo.s, velo.u)

Arguments

`object`	CytoSignal object
`velo.s`	matrix of spliced velo
`velo.u`	matrix of unspliced velo

Subset gene expression matrix according to availability in the UNIPROT database.

Description

Subset genes to those available in g_to_u, the gene name to UNIPROT ID databse provided with addIntrDB

Usage

changeUniprot(object, ...)

## S3 method for class 'matrix_like'
changeUniprot(object, gene_to_uniprot, verbose = TRUE, ...)

## S3 method for class 'CytoSignal'
changeUniprot(object, verbose = TRUE, ...)
changeUniprot(object, ...)

## S3 method for class 'matrix_like'
changeUniprot(object, gene_to_uniprot, verbose = TRUE, ...)

## S3 method for class 'CytoSignal'
changeUniprot(object, verbose = TRUE, ...)

Arguments

`object`	A CytoSignal object or a matrix.
`...`	Parameters passed to S3 methods.
`gene_to_uniprot`	Please use `g_to_u`.
`verbose`	Whether to show information of the progress.

Value

CytoSignal object with subset matrix updated in counts slot, or the subset matrix.

Create a CytoSignal object

Description

Create a CytoSignal object

Usage

createCytoSignal(
  raw.data,
  cells.loc,
  clusters = NULL,
  name = NULL,
  parameters = NULL,
  log = NULL
)
createCytoSignal(
  raw.data,
  cells.loc,
  clusters = NULL,
  name = NULL,
  parameters = NULL,
  log = NULL
)

Arguments

`raw.data`	raw data matrix
`cells.loc`	cells location matrix
`clusters`	cluster information
`name`	name of the dataset
`parameters`	parameters used
`log`	log of the processing

Value

a CytoSignal object

The CytoSignal Class

Description

The CytoSignal object is created from one spatial transcriptomic dataset. A CytoSignal object can be created with createCytoSignal.

Slots

counts: Raw gene expression count matrix, sparse dgCMatrix, gene x cell.
cell.loc: Matrix of cell locations, cell by axis.
clusters: cluster assignments, named factor.
imputation: List of imputation objects.
lrscore: List of lrScore objects.
velo: List of velo objects.
intr.valid: List of interaction database. Added with addIntrDB.
cell.data: data.frame of cell data.
parameters: List of parameters for gaussian imputation
log: Log of each main steps
version: Version of package used to create object. Should not be modified by users.

Interaction database derived from CellphoneDB V2

Description

The referencing database that contains the list of curated interactions, the meta-information of each interaction, the ligand and receptor of each interaction, the protein components of both part of each interaction. The protein components are presented with UNIPROT ID, so we also provide g_to_u that maps UNIPROT ID to gene names.

Usage

db.diff

db.cont

inter.index

g_to_u
db.diff

db.cont

inter.index

g_to_u

Format

db.diff and db.cont are lists of factor objects. db.diff is for diffusion dependent interactions and db.cont is for contact dependent interactions. Both lists have the following three entries of the same format:

$ligands - A factor object where the values are IDs of interactions and names are UNIPROT IDs of the ligand protein components that match to the interaction.
$receptors - A factor object of the same format as $ligands but for the receptor part of the interactions.
$combined - The concatenation of the two factors above.

inter.index is a data.frame object with the meta-information of each interaction.

g_to_u is a data.frame object that map gene names to UNIPROT ID of their protein product.

An object of class list of length 3.

An object of class data.frame with 1396 rows and 11 columns.

An object of class data.frame with 977 rows and 3 columns.

Source

CellphoneDB V2

dim Method for mergedCytoSignal object

Description

Only returns nrow(x) which indicates the number of spots/cells

Usage

## S3 method for class 'mergedCytoSignal'
dim(x)
## S3 method for class 'mergedCytoSignal'
dim(x)

Arguments

`x`	A mergedCytoSignal object

Value

A numeric vector with the number of spots/cells and NA

Identify nearest neighbors for each location using different strategies for different types of interactions

Description

This is a wrapper function of findNNGauEB, findNNDT and findNNRaw. We use a Gaussian Epsilon ball to identify the nearest neighbors that can contribute to the diffusible ligands. And then uses a Delaunay triangulation to identify the nearest neighbors that can contribute to the contact-dependent ligands. The identified nearest neighbors will then be used for imputing the $L$ or $R$ value for each location.

Usage

findNN(
  object,
  eps = NULL,
  sigma = NULL,
  diff.weight = "auto",
  dt.weight = 2,
  max.r = NULL
)
findNN(
  object,
  eps = NULL,
  sigma = NULL,
  diff.weight = "auto",
  dt.weight = 2,
  max.r = NULL
)

Arguments

`object`	A CytoSignal object.
`eps`	Gaussian Epsilon Ball method parameter. A numeric scalar. The radius of the Gaussian Epsilon ball. Default `NULL` uses parameter inferred with `inferEpsParams`.
`sigma`	Gaussian Epsilon Ball method parameter. The $\sigma$ of the Gaussian kernel. Default `NULL` uses parameter inferred with `inferEpsParams`.
`max.r`	Delaunay Triangulation method parameter. A numeric scalar for the maximum radius of the edges. Default `NULL` uses parameter inferred with `inferEpsParams`.
`self.weight`	Gaussian Epsilon Ball method parameter. Weight of the index cell. Use a number between 0-1 or a string `"auto"` or `"sum_1"`. Default `"auto"`.
`weight`	Delaunay Triangulation method parameter. A numeric scalar for the sum of the weights of the edges of the Delaunay Triangulation. Default `2`.

Value

A CytoSignal object updated. object@imputation slot will be updated with three new entries: object@imputation$GauEps, object@imputation$DT and object@imputation$Raw.

Examples

## Not run: 
object <- findNN(object)

## End(Not run)
## Not run: 
object <- findNN(object)

## End(Not run)

Find the direct connected neighbor of each cell, using Delaunay triangulation

Description

Find the direct connected neighbor of each cell, using Delaunay triangulation

Usage

findNNDT(object, ...)

## S3 method for class 'matrix'
findNNDT(object, weight.sum = 2, max.r = NULL, ...)

## S3 method for class 'CytoSignal'
findNNDT(object, weight = 2, max.r = NULL, ...)
findNNDT(object, ...)

## S3 method for class 'matrix'
findNNDT(object, weight.sum = 2, max.r = NULL, ...)

## S3 method for class 'CytoSignal'
findNNDT(object, weight = 2, max.r = NULL, ...)

Arguments

`object`	A CytoSignal object or a matrix of cell location.
`...`	Arguments passed to other S3 methods
`weight.sum`	The sum of the weights
`max.r`	The maximum radius of the edges, by default is the r.diffuse.scale,

Value

For CytoSignal object, the original object with result updated. For matrix object, a list of neighbors.

Find the neighbors of each cell in the Epsilon Ball

Description

Find the neighbors of each cell in the Epsilon Ball

Usage

findNNGauEB(object, eps = NULL, sigma = NULL, self.weight = "auto", ...)

## S3 method for class 'matrix'
findNNGauEB(object, eps, sigma = 0.15, self.weight = "auto", ...)

## S3 method for class 'CytoSignal'
findNNGauEB(
  object,
  eps = NULL,
  sigma = NULL,
  self.weight = "auto",
  tag = NULL,
  ...
)
findNNGauEB(object, eps = NULL, sigma = NULL, self.weight = "auto", ...)

## S3 method for class 'matrix'
findNNGauEB(object, eps, sigma = 0.15, self.weight = "auto", ...)

## S3 method for class 'CytoSignal'
findNNGauEB(
  object,
  eps = NULL,
  sigma = NULL,
  self.weight = "auto",
  tag = NULL,
  ...
)

Arguments

`object`	A CytoSignal object or a matrix object for the cell spatial location.
`eps`	The radius of the epsilon ball. For CytoSignal object, by default use parameter inferred with `inferEpsParams`.
`sigma`	The sigma of the Gaussian kernel. Default `0.15` for matrix method. For CytoSignal object, by default use parameter inferred with `inferEpsParams`.
`self.weight`	weight of the index cell. Use a number between 0-1, or choose from `"auto"` or `"sum_1"`.
`...`	Arguments passed to other S3 methods.
`tag`	Name prefix of the analysis.

Value

For CytoSignal object, the orignal object with result updated. For matrix object, a list of neighbors and their distances.

Create a ImpData object using raw data without imputation

Description

Create a ImpData object using raw data without imputation

Usage

findNNRaw(object)
findNNRaw(object)

Arguments

object

A Cytosignal object

Value

A Cytosignal object

Format a CytosignalIntrDEG object to string

Description

Format a CytosignalIntrDEG object to string

Usage

## S3 method for class 'CytosignalIntrDEG'
format(x, ...)
## S3 method for class 'CytosignalIntrDEG'
format(x, ...)

Arguments

`x`	A `CytosignalIntrDEG` object.
`...`	Passed to other methods

Value

A string representation of the object

Compute the LR score for specific ligand-receptor imputation obj pairs

Description

Compute the LR score for specific ligand-receptor imputation obj pairs

Usage

graphNicheLR(object, ...)
graphNicheLR(object, ...)

Arguments

`object`	A Cytosignal object
`lig.slot`	The ligand slot to use
`recep.slot`	The receptor slot to use
`intr.db.name`	The intr database name to use
`nn.use`	The neighbor index as niche

Value

A Cytosignal object

Sub function for graphNicheLR, input a CytoSignal object

Description

Sub function for graphNicheLR, input a CytoSignal object

Usage

## S3 method for class 'CytoSignal'
graphNicheLR(object, lig.slot, recep.slot, intr.db.name, nn.use = NULL)

## S3 method for class 'CytoSignal'
graphNicheLR(object, lig.slot, recep.slot, intr.db.name, nn.use = NULL)
## S3 method for class 'CytoSignal'
graphNicheLR(object, lig.slot, recep.slot, intr.db.name, nn.use = NULL)

## S3 method for class 'CytoSignal'
graphNicheLR(object, lig.slot, recep.slot, intr.db.name, nn.use = NULL)

Arguments

`object`	A Cytosignal object
`lig.slot`	The ligand slot to use
`recep.slot`	The receptor slot to use
`intr.db.name`	The intr database name to use
`nn.use`	slot that the neighbor index should be taken from, by default is the same as the recep.slot. For example, if score.obj = GauEps-DT, then nn.use = "DT". nn.use could also be a user-defind factor.

Value

A Cytosignal object

Sub function for graphNicheLR, input a sparse matrix

Description

Sub function for graphNicheLR, input a sparse matrix

Usage

## S3 method for class 'dgCMatrix'
graphNicheLR(dge.lig, dge.recep, nb.id.fac, lig.fac, recep.fac)
## S3 method for class 'dgCMatrix'
graphNicheLR(dge.lig, dge.recep, nb.id.fac, lig.fac, recep.fac)

Arguments

`dge.lig`	A sparse matrix for ligand
`dge.recep`	A sparse matrix for receptor
`nb.id.fac`	A factor of neighbor indices
`lig.fac`	A factor of ligand indices
`recep.fac`	A factor of receptor indices

Value

A sparse matrix

Show significant genes across top GO term hits with coefficients from regression analysis of an interaction

Description

Create a heatmap for an interaction on GO terms by significant genes, colored by the coefficients of the genes in the regression model returned by inferIntrDEG. The GO term enrichment can be done with any tools available. The input data.frame GO must contain fields for 1. term description, character, pointed to by description.col, 2. p-value, numeric, pointed to by pval.col, 3. gene hit string, character, pointed to by gene.col. The gene hit string for each term must be form in a way that function gene.split.fun can split it into a character vector of gene names. For example, if a gene hit string is "gene1, gene2, gene3", then gene.split.fun should be function(x) unlist(strsplit(x, ", ")), so that a split result c("gene1", "gene2", "gene3") can be correctly obtained.

Usage

heatmap_GO(
  intrDEG,
  GO,
  intr,
  description.col = "description",
  pval.col = "pval",
  gene.col = "genes",
  gene.split.fun = function(x) unlist(strsplit(x, ",")),
  term.topN = 20,
  gene.topN = 20,
  binary_sign = FALSE,
  text.size = 10
)
heatmap_GO(
  intrDEG,
  GO,
  intr,
  description.col = "description",
  pval.col = "pval",
  gene.col = "genes",
  gene.split.fun = function(x) unlist(strsplit(x, ",")),
  term.topN = 20,
  gene.topN = 20,
  binary_sign = FALSE,
  text.size = 10
)

Arguments

`intrDEG`	A `CytosignalIntrDEG` object, output from `inferIntrDEG`.
`GO`	A data.frame object for GO enrichment analysis result.
`intr`	A single interaction ID (starts with "CPI") or its numeric index within the range of `intrDEG`.
`description.col`, `pval.col`, `gene.col`	The column names of the data.frame `GO` that contains the term description, p-value, and gene hit string. Default `"description"`, `"pval"`, `"genes"`.
`term.topN`	Use this number of top GO terms, ranked by p-values. Default `20`.
`gene.topN`	Use this number of top genes, ranked by absolute value of coefficients. Default `20`.
`binary_sign`	Whether to convert coefficient value to binary sign value. Default `FALSE`.
`color_num`	Number of colors in the heatmap. Can only use `2` or `3`, Default `2` use white-red color palette. `3` use scaled blue-white-red color palette.

GGPLOT2 FUNCTIONALITY FOR MAPPING TO HEXAGON SIZE AND COLOUR AESTHETICS by Robin Edwards, 2013 (geotheory.co.uk, @geotheory) This has been adapted from the ggplot bin_hex.R script that underpins geom_hex, etc (see https://github.com/hadley/densityvis/blob/master/R/bin-hex.r).

Description

These functions implement aesthetic mapping to hexagon size (area), in addition to the existing colour-mapping functionality. The key change is the addition of a new fourth variable (var4) to hex_bin(), which complements the inbuilt hexagon binning functionality. The 'frequency.to.area' argument enables the default mappings of binned data to colour and var4 to size to be interchanged. The hmin/hmax arguments 0,1 set area mapping constraints (hmax can exceed 1). xlim/xlat enable hexagon tesselation to be constrained independently of data range. There may be some bugs in the implementation. A legend for hexagon size has not been implemented. Bin data into hexagons (2d).

Usage

hex_bin(
  x,
  y,
  weight = NULL,
  var4 = NULL,
  width = NULL,
  height = NULL,
  xbins = 20,
  ybins = 20,
  var4.to.color = FALSE,
  na.rm = FALSE,
  hmin = 0,
  hmax = 1,
  xlim = NULL,
  ylim = NULL,
  ...
)
hex_bin(
  x,
  y,
  weight = NULL,
  var4 = NULL,
  width = NULL,
  height = NULL,
  xbins = 20,
  ybins = 20,
  var4.to.color = FALSE,
  na.rm = FALSE,
  hmin = 0,
  hmax = 1,
  xlim = NULL,
  ylim = NULL,
  ...
)

Arguments

`x`	a numeric vector of x positions
`y`	a numeric vector of y positions
`weight`	`NULL` or a numeric vector providing weights for each observation, replace `counts`, mapped to color
`var4`	`NULL` or a numeric vector providing weights for each observation, averaged within each bin, mapped to hex bin size
`width`	width of each hexagon, if `NULL` computed from ybins
`height`	height of each hexagon, if `NULL` computed from ybins
`xbins`	number of horizontal bins, if `width` unspecified
`ybins`	number of vertical bins, if `height` unspecified
`na.rm`	If `TRUE` missing values will be silently removed, otherwise they will be removed with a warning.

Value

A data frame with columns x, y and freq, and attributes width and height.

Examples

plot(hex_bin(runif(1e4), runif(1e4)))
plot(hex_bin(rnorm(1e4), rnorm(1e4)))

data(baseball, package = "plyr")
bin <- hex_bin(baseball$g, baseball$ab)
plot(hex_bin(runif(1e4), runif(1e4)))
plot(hex_bin(rnorm(1e4), rnorm(1e4)))

data(baseball, package = "plyr")
bin <- hex_bin(baseball$g, baseball$ab)

The ImpData Class

Description

The ImpData object is created from one ST dataset. User could choose a preferred imputation method and the class stores the imputed data, the imputed normalized data, the intr database, the intr database.

Details

The key slots used in the ImpData object are described below.

Slots

method: imputation method
imp.data: imputed data
imp.norm.data: imputed normalized data
intr.data: imputed normalized data subsetted by intr database
intr.data.null: permuted imputed normalized data subsetted by intr database
nn.id: nearest neighbor id
nn.dist: nearest neighbor distance
log: Log of each main steps

Impute the $L$ or $R$ value from the nearest neighbors of each location

Description

After running findNN, we can impute the $L$ or $R$ value from the nearest neighbors of each location basing on the types of nearest neighbors.

Usage

imputeLR(object, weights = c("none", "mean", "counts", "dist"))
imputeLR(object, weights = c("none", "mean", "counts", "dist"))

Arguments

`object`	A CytoSignal object, with `findNN` already run.
`weights`	The method to transform distances to weights. Choose from `"none"`, `"mean"`, `"counts"` and `"dist"`. Default `"none"` since weights are already calculated by Gaussian kernel and DT mean.

Value

A CytoSignal object updated. Entries in object@imputation slot will be updated with the imputation values.

Examples

## Not run: 
object <- findNN(object)
object <- imputeLR(object)

## End(Not run)
## Not run: 
object <- findNN(object)
object <- imputeLR(object)

## End(Not run)

Impute the data

Description

Impute the data

Usage

imputeNiche(object, weights = c("mean", "counts", "dist", "none"), ...)

## S3 method for class 'dgCMatrix'
imputeNiche(
  object,
  nb.id.fac,
  nb.dist.fac,
  weights = c("mean", "counts", "dist", "none"),
  ...
)

## S3 method for class 'CytoSignal'
imputeNiche(
  object,
  nn.type = NULL,
  weights = c("mean", "counts", "dist", "none"),
  ...
)
imputeNiche(object, weights = c("mean", "counts", "dist", "none"), ...)

## S3 method for class 'dgCMatrix'
imputeNiche(
  object,
  nb.id.fac,
  nb.dist.fac,
  weights = c("mean", "counts", "dist", "none"),
  ...
)

## S3 method for class 'CytoSignal'
imputeNiche(
  object,
  nn.type = NULL,
  weights = c("mean", "counts", "dist", "none"),
  ...
)

Arguments

`object`	A Cytosignal object or a dgCMatrix object of raw gene expression matrix
`weights`	The weight of the Delaunay triangulation. Choose from `"mean"`, `"counts"`, `"dist"` or `"none"`.
`...`	Arguments passed to other S3 methods
`nb.id.fac`	A factor of neighbors
`nb.dist.fac`	A factor of weights
`nn.type`	The type of neighbors. The `tag` used when finding nearest neighbors in the upstream step. If not customized, use `"GauEps"` for NN found with `findNNGauEB`, or use `"DT"` for NN found with `findNNDT`. Default use the most lastly computed NN.

Value

For CytoSignal object, the original object with imputation result updated. For dgCMatrix object, a sparse N x N graph presented as another dgCMatrix object.

Impute the velocity mtx using the specified method

Description

Impute the velocity mtx using the specified method

Usage

imputeNicheVelo(object, ...)
imputeNicheVelo(object, ...)

Arguments

`object`	A Cytosignal object
`...`	Other parameters
`method`	The method to use for imputation

Value

A Cytosignal object

Sub function for imputeNicheVelo, input a Cytosignal object

Description

Sub function for imputeNicheVelo, input a Cytosignal object

Usage

## S3 method for class 'CytoSignal'
imputeNicheVelo(object, nn.type = NULL)
## S3 method for class 'CytoSignal'
imputeNicheVelo(object, nn.type = NULL)

Arguments

`object`	A Cytosignal object
`nn.type`	The type of neighbors

Value

A Cytosignal object

Impute time derivative of $L$ or $R$ from the nearest neighbors of each location

Description

After running findNN, we can impute the temporal $L$ or $R$ change from the nearest neighbors of each location basing on the types of nearest neighbors.

Usage

imputeVeloLR(object)
imputeVeloLR(object)

Arguments

object

A CytoSignal object, with findNN already run and velocity information added with addVelo.

Value

A CytoSignal object updated. Entries in object@imputation slot will be updated with the imputation values.

Examples

## Not run: 
object <- findNN(object)
object <- imputeVeloLR(object)

## End(Not run)
## Not run: 
object <- findNN(object)
object <- imputeVeloLR(object)

## End(Not run)

Infer the correspondence between LR-scores and Significance

Description

Min-max the LRscores, substract the significant ones, sum and average.

Usage

inferCorrScore(object, correctBy = c("cell", "intr"), slot.use = NULL)
inferCorrScore(object, correctBy = c("cell", "intr"), slot.use = NULL)

Arguments

`object`	A Cytosignal object
`correctBy`	Spatial FDR correction method. Choose from `"cell"` or `"intr"`.
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.

Value

A Cytosignal object

Infer the parameters of the Gaussian kernel

Description

Infer the parameters of the Gaussian kernel

Usage

inferEpsParams(object, scale.factor = NULL, r.eps.real = 200, thresh = 0.001)
inferEpsParams(object, scale.factor = NULL, r.eps.real = 200, thresh = 0.001)

Arguments

`object`	A Cytosignal object
`r.eps.real`	The radius of the epsilon ball in tech resolution in um, default 200 um
`thresh`	The total signal out of the epsilon ball
`scale.facor`	1 spatial coord unit equals to how many µm

Value

A Cytosignal object

Infer significant genes for each interaction

Description

This function performs wilcoxon one-sided test for each cell type, between cells enriched with an interaction against other cells. The top significant genes are selected for each interaction. The expression profile of the selected genes, together with the cell type variable, are then feeded into a regression model for further refinement. This analysis infers the significant genes for each interaction, and refines the LRScore using the selected genes.

Usage

inferIntrDEG(
  object,
  intr = NULL,
  slot.use = NULL,
  signif.use = NULL,
  num.per.choose = 50,
  alpha.test = seq(0.5, 1, 0.1),
  seed = 1,
  minCell = 50,
  verbose = TRUE
)
inferIntrDEG(
  object,
  intr = NULL,
  slot.use = NULL,
  signif.use = NULL,
  num.per.choose = 50,
  alpha.test = seq(0.5, 1, 0.1),
  seed = 1,
  minCell = 50,
  verbose = TRUE
)

Arguments

`object`	A `Cytosignal` object with `inferIntrScore` already run.
`intr`	Specify interactions to be used. A vector of either the unique ID of interactions or the numeric rank indices. Available IDs can be shown with `showIntr(object)`. Availability of an interaction depends on the LRscore slot to be used as well as the significance metric to be used.
`slot.use`	The LRscore type to be used. See vignette for explanation.
`signif.use`	The significance metric to be used. See vignette for explanation.
`num.per.choose`	The maximum number of genes to be chosen for each wilcoxon test. A test happens for each interaction and each cell type. Default `50`.
`alpha.test`	A sequence of alpha values to be tested for the elastic net regression model. The best alpha value will be chosen based on the smallest loss. Larger alpha values result in less final selection. Default `seq(0.5, 1, 0.1)`.
`seed`	Random seed for controlling the cross validation. Default `1`.
`minCell`	Minimum number of cells where a gene is expressed to be considered. Default `50`.
`verbose`	Whether to print progress messages. Default `TRUE`.

Value

list object, each element is the result for each interaction. Each interaction result is a list object containing the following entries:

glmnet_model - A "cv.glmnet" object, the regression model.
score_refine - A 1-column matrix of refined LR scores of this interaction in each cell.
coef - A matrix of coefficients of the regression model.
sign_clusters - A character vector of significant clusters in selected by the model.
sign_genes - A character vector of significant genes selected by the model.
alpha - The alpha value used for the final chosen model.
slot.use - The LRScore type used for this analysis.
signif.use - The significance metric used for this analysis.

Calculate LRScore from the imputed L and R values

Description

After running imputeLR, we can calculate the LR score for each location. The LR score is calculated as the product of the imputed $L$ and $R$ values. With the LR score inferred, we subsequently perform permutation tests to construct the null distribution for testing the significance of the interactions.

Usage

inferIntrScore(
  object,
  recep.smooth = FALSE,
  intr.type = c("diff", "cont"),
  perm.size = 1e+05,
  norm.method = c("default", "cpm", "none", "scanpy"),
  numCores = 1
)
inferIntrScore(
  object,
  recep.smooth = FALSE,
  intr.type = c("diff", "cont"),
  perm.size = 1e+05,
  norm.method = c("default", "cpm", "none", "scanpy"),
  numCores = 1
)

Arguments

`object`	A CytoSignal object, with `imputeLR` already run.
`recep.smooth`	A logical scalar. Whether to use the smoothed $R$ values which is imputed with DT method. Default `FALSE`.
`intr.type`	A character vector. The type of interactions to calculate the LR score. Choose from one or both of `"diff"` and `"cont"` for diffusion-dependent and contact-dependent interactions, respectively. Default uses both.
`perm.size`	A numeric scalar. The number of permutations to perform. Default `1e5` times.
`norm.method`	The normalization method to apply to the imputed L and R
`numCores`	SPARK::sparkx parameter. The number of cores to use. Default `1`.

Value

A CytoSignal object updated. Entries in object@lrscore slot will be updated with the LR scores and the significance inferrence. When recep.smooth is by default FALSE, the object@lrscore slot will be updated with object@lrscore$`GauEps-Raw` and object@lrscore$`DT-Raw`. When recep.smooth is TRUE, the object@lrscore slot will be updated with object@lrscore$`GauEps-DT` and object@lrscore$`DT-DT`.

Examples

## Not run: 
object <- findNN(object)
object <- imputeLR(object)
object <- inferIntrScore(object)

## End(Not run)
## Not run: 
object <- findNN(object)
object <- imputeLR(object)
object <- inferIntrScore(object)

## End(Not run)

Calculate the interaction velocity from the imputed time derivative of $L$ or $R$ values

Description

After running imputeVeloLR, we can calculate the interaction velocity for each location. Please refer to the manualscript for detail of the calculation.

Usage

inferIntrVelo(object, recep.smooth = FALSE, norm.method = "scanpy")
inferIntrVelo(object, recep.smooth = FALSE, norm.method = "scanpy")

Arguments

`object`	A CytoSignal object, with `imputeVeloLR` already run.
`recep.smooth`	A logical scalar. Whether to use the smoothed $R$ values which is imputed with DT method. Default `FALSE`.
`norm.method`	The normalization method to apply to the velocity data, need to be consistent with the normalization method used when generating the input RNA velocity. Please consult the tool used for it, e.g. scVelo, VeloVAE. Default is `"scanpy"`. Can choose from `"scanpy"`, `"cpm"`, `"default"` or `"none"`.

Value

A CytoSignal object updated. Entries in object@lrvelo slot will be updated with the velocity scores. When recep.smooth is by default FALSE, the object@lrvelo slot will be updated with object@lrvelo$`GauEps-Raw` and object@lrvelo$`DT-Raw`. When recep.smooth is TRUE, the object@lrvelo slot will be updated with object@lrvelo$`GauEps-DT` and object@lrvelo$`DT-DT`.

Examples

## Not run: 
object <- addVelo(object, velo.s, velo.u)
object <- findNN(object)
object <- imputeVeloLR(object)
object <- inferIntrVelo(object)

## End(Not run)
## Not run: 
object <- addVelo(object, velo.s, velo.u)
object <- findNN(object)
object <- imputeVeloLR(object)
object <- inferIntrVelo(object)

## End(Not run)

Permute LR score for specific ligand-receptor imputation obj pairs

Description

This function is a follow-up function of inferScoreLR. It computes the NULL LR-scores using the NULL imputation results and stores the results in the LR score object. The null distribution of the LR scores can be used to test the significance of the LR scores.

Usage

inferNullScoreLR(object, slot.use = NULL)
inferNullScoreLR(object, slot.use = NULL)

Arguments

`object`	A Cytosignal object
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.

Value

A Cytosignal object

Compute the LR score for specific ligand-receptor imputation pairs

Description

Compute the LR score for specific ligand-receptor imputation pairs

Usage

inferScoreLR(
  object,
  lig.slot,
  recep.slot,
  norm.method = c("default", "cpm", "none", "scanpy"),
  intr.db.name = c("diff_dep", "cont_dep"),
  tag = paste0(lig.slot, "-", recep.slot)
)
inferScoreLR(
  object,
  lig.slot,
  recep.slot,
  norm.method = c("default", "cpm", "none", "scanpy"),
  intr.db.name = c("diff_dep", "cont_dep"),
  tag = paste0(lig.slot, "-", recep.slot)
)

Arguments

`object`	A Cytosignal object
`lig.slot`	The ligand slot to use
`recep.slot`	The receptor slot to use
`norm.method`	Method to normalize the data. Default is "default".
`intr.db.name`	The intr database name to use
`tag`	Name of the result to be stored in object.

Value

A Cytosignal object

Infer significance of LR scores

Description

Infer significance of LR scores

Usage

inferSignif(
  object,
  fdr.method = c("spatialFDR", "fdr"),
  p.value = 0.05,
  reads.thresh = 100,
  sig.thresh = 100,
  slot.use = NULL,
  nn.use = NULL
)
inferSignif(
  object,
  fdr.method = c("spatialFDR", "fdr"),
  p.value = 0.05,
  reads.thresh = 100,
  sig.thresh = 100,
  slot.use = NULL,
  nn.use = NULL
)

Arguments

`object`	A Cytosignal object
`fdr.method`	The false discovery rate method to use. Choose from `"spatialFDR"` and `"fdr"`. Default `"spatialFDR"`.
`p.value`	A numeric scalar. The p-value threshold to use for filtering significant interactions. Default `0.05`.
`reads.thresh`	A numeric scalar. The minimum number of reads for a ligand-receptor interaction to be considered. Default `100`.
`sig.thresh`	A numeric scalar. The minimum number of of beads for a ligand-receptor interaction to be considered. Default `100`.
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferIntrScore`. Default `NULL` apply specified significance and filtering criteria to all available LRscore slots.
`nn.use`	Which imputation to use. Default the imputation used for deriving the LRScore specified with `slot.use`. Use the name specified with `tag` when running `findNNGauEB`; use `"DT"` for imputation produced with `findNNDT`; or use `"Raw"` for imputation produced with `findNNRaw`.

Value

A Cytosignal object

Infer the correspondence between LR-scores and Significance

Description

Use DT neighbors to impute the p-value and LR-score for each cell, then compute the pearson correlation between the imputed LR-score and p-value.

Usage

inferSpatialCorr(object, correctBy = c("cell", "intr"), slot.use = NULL)
inferSpatialCorr(object, correctBy = c("cell", "intr"), slot.use = NULL)

Arguments

`object`	A Cytosignal object
`correctBy`	Spatial FDR correction method. Choose from `"cell"` or `"intr"`.
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.

Value

A Cytosignal object

Sub function for inferVeloLR, input a CytoSignal object

Description

Sub function for inferVeloLR, input a CytoSignal object

Usage

inferVeloLR(
  object,
  lig.slot,
  recep.slot,
  intr.db.name,
  norm.method = "scanpy",
  tag = NULL
)
inferVeloLR(
  object,
  lig.slot,
  recep.slot,
  intr.db.name,
  norm.method = "scanpy",
  tag = NULL
)

Arguments

`object`	A Cytosignal object
`lig.slot`	The ligand slot to use
`recep.slot`	The receptor slot to use
`intr.db.name`	The intr database name to use
`norm.method`	The normalization method to apply to the counts, need to be consistent with the normalization method used for the RNA velocity method. Default is "scanpy".
`nn.use`	slot that the neighbor index should be taken from, by default is the same as the recep.slot. For example, if velo.obj = GauEps-DT, then nn.use = "DT". nn.use could also be a user-defind factor.

Value

A Cytosignal object

The lrScores Class

Description

The lrScores object is created from one ST dataset. User could choose two imputation methods to calculate the ligand-receptor scores. The class stores the ligand, receptor, and interaction database, the ligand-receptor scores, the ligand-receptor scores for permuted data, the ligand-receptor scores for permuted data, and the log of each main steps.

Details

The key slots used in the lrScores object are described below.

Slots

lig.slot: ligand database
recep.slot: receptor database
intr.slot: interaction database
intr.list: list of interaction database
score: ligand-receptor scores
score.null: permuted ligand-receptor scores
res.list: list of results
log: Log of each main steps

The lrvelo Class

Description

The lrvelo object is created from one ST dataset. User could choose two imputation methods to calculate the ligand-receptor scores. The class stores the index of ligand, receptor, and interaction database, inferred lrvelo for each interaction, and the log of each main steps.

Details

The key slots used in the lrVelo object are described below.

Slots

lig.slot: ligand database
recep.slot: receptor database
intr.slot: interaction database
intr.list: list of interaction database
velo.s: A matrix of spliced velo for each gene
velo.u: A matrix of unspliced velo for each gene
velo.intr: A sparse matrix of velo for each intr
nn.id: A factor of nearest neighbor id. Re-do findNN since the order of cells may change!
nn.dist: A factor of nearest neighbor distance. Re-do findNN since the order of cells may change!
log: Log of each main steps

Create mergedCytoSignal object

Description

This function accepts a list of CytoSignal objects together with a metadata data.frame that provides dataset level metadata covariates, which will be required for the cross-dataset analysis.

The function will preprocess each CytoSignal object with cross-dataset analysis specific steps, including imputing diffusible LR values using Gaussion Epsilon model with weight 1.

Usage

mergeCytoSignal(
  objList,
  metadata = NULL,
  name.by = NULL,
  counts.thresh = 0,
  scale.factor = NULL
)
mergeCytoSignal(
  objList,
  metadata = NULL,
  name.by = NULL,
  counts.thresh = 0,
  scale.factor = NULL
)

Arguments

`objList`	A list of CytoSignal objects or paths to CytoSignal objects stored as RDS format. Can be mixed of two types.
`metadata`	A data.frame object of dataset level metadata, containing information like gender, age, treatment and etc. Default `NULL`. If provided, must have as many rows as the length of `objList` with rows in matching order to `objList`.
`name.by`	A column name in `metadata` indicating where to find the dataset names. Default `NULL` names datasets by "dataset1", "dataset2" and etc, and will create column "dataset" in the metadata.
`counts.thresh`	A number indicating the threshold for removing low-quality spots. Default 0.
`scale.factor`	A number indicating 1 spatial coord unit equals to how many µm.

Value

A mergedCytoSignal object preprocessed.

mergedCytoSignal Class for cross-dataset analysis of spatial cell-cell communication

Description

An object that holds necessary information from multiple CytoSignal objects that is required for performing cross-dataset analysis. Results from the cross-dataset analysis will also be held in this object.

Please use mergeCytoSignal() to create a mergedCytoSignal object

Slots

metadata

A data.frame with spot/cell level metadata covariates. Column name "dataset", "originalID", "clusters", "x" and "y" are reserved for downstream analysis and object validity.

diff.lrscore

A sparse matrix (dgCMatrix) of LR scores for diffusion-dependent interactions, across all datasets.

diff.results

A list of data.frame objects, each containing the results of NEBULA analysis for a specific covariate as specified when running the analysis.

cont.lrscore

A sparse matrix (dgCMatrix) of LR scores for contact-dependent interactions, across all datasets.

cont.results

A list of data.frame objects, each containing the results of NEBULA analysis for a specific covariate as specified when running the analysis.

cov.use

A list that organizes the covariates being used for the modeling. Each element is named by a variable name selected from metadata, and is a list with the following structure

"type": "factor" or "numeric" etc. Original class of the variable
"levels" (if a factor): levels of the factor that'll be found in the model.matrix outcome
"range" (if numeric): range of the variable

Dataset names of a mergedCytoSignal object

Description

Allows getting and setting dataset names of a mergedCytoSignal object.

Usage

## S3 method for class 'mergedCytoSignal'
names(x)

## S3 replacement method for class 'mergedCytoSignal'
names(x) <- value
## S3 method for class 'mergedCytoSignal'
names(x)

## S3 replacement method for class 'mergedCytoSignal'
names(x) <- value

Arguments

`x`	A mergedCytoSignal object
`value`	A character vector of up to the same length as x to rename datasets. or NULL to reset to default names.

Value

For names, a character vector of dataset names.

For names<-, a mergedCytoSignal object with dataset names updated in both metadata and the rownames of lrscore matrices

Permute Imputation Results of specific imputation method

Description

This function permutes the imputation methods from which a given LR score is calculated. Note that all rounds of permutation will use the same shuffle and sample index.

Usage

permuteLR(object, slot.use = NULL, norm.method = "default", perm.size = 1e+05)
permuteLR(object, slot.use = NULL, norm.method = "default", perm.size = 1e+05)

Arguments

`object`	A CytoSignal object with `inferLRScore` run beforehand.
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.
`perm.size`	Size of the permutation test. Defult `100000`.
`norm.me`	Normalization method. See `normCounts` for detail.

Value

A CytoSignal object

Plotting edge for a given interaction from a CytoSignal object

Description

Plotting edge for a given interaction from a CytoSignal object

Usage

plotEdge(
  object,
  intr,
  type = c("receiver", "sender"),
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  return.plot = TRUE,
  plot_dir = "csEdgePlot/",
  filename = NULL,
  plot.fmt = c("png", "pdf", "svg"),
  title = NULL,
  edge.size = 500,
  use.shape = 16,
  line.width = 0.01,
  use.phi = 30,
  use.theta = -17,
  z.scaler = 0.03,
  box = TRUE,
  z.pt.interval = 1,
  pt.size = 0.1,
  pt.sig.size = NULL,
  pt.stroke = 0.2,
  width = 5,
  height = 5,
  set.res = 300,
  verbose = TRUE
)
plotEdge(
  object,
  intr,
  type = c("receiver", "sender"),
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  return.plot = TRUE,
  plot_dir = "csEdgePlot/",
  filename = NULL,
  plot.fmt = c("png", "pdf", "svg"),
  title = NULL,
  edge.size = 500,
  use.shape = 16,
  line.width = 0.01,
  use.phi = 30,
  use.theta = -17,
  z.scaler = 0.03,
  box = TRUE,
  z.pt.interval = 1,
  pt.size = 0.1,
  pt.sig.size = NULL,
  pt.stroke = 0.2,
  width = 5,
  height = 5,
  set.res = 300,
  verbose = TRUE
)

Arguments

`object`	`CytoSignal` object.
`intr`	Interaction to plot. See available options with `showIntr`.
`type`	Type of plot, either `"sender"` or `"receiver"`. Default `"sender"`.
`slot.use`	Slot to use for plotting
`signif.use`	Significance level to use for plotting
`colors.list`	List of colors to use for plotting
`return.plot`	Whether to return "plist" object for figure organization. `TRUE` returns "plist" which can be shown on current display device with `plot()`. `FALSE` saves the figure to disk. Default `TRUE`.
`plot_dir`	Path where the figure will be saved when `return.plot = FALSE`. Default `"csEdgePlot/"` (under current working directory).
`filename`	Filename of the figure inside `plot_dir`. Please match extension name with `plot.fmt` and do not include path. Default `NULL` and the exact filename will be determined by interaction information and `plot.fmt`.
`plot.fmt`	Format of plot, either `"png"` or `"pdf"`.
`title`	Title of plot
`use.shape`	Shape of points
`line.width`	Width of lines
`use.phi`	Angle of 3D plot
`use.theta`	Angle of 3D plot
`z.scaler`	Scaling factor for z-axis
`z.pt.interval`	Interval of points on z-axis
`pt.stroke`	Width of points
`set.res`	Resolution of plot
`use.cex`	Size of points
`u_width`	Width of plot
`u_hgt`	Height of plot

Value

plist object when return.plot = TRUE, no in memory object is returned when return.plot = FALSE but the figure will be saved on disk.

Visualize NEBULA analysis result with dot plot that involves all covariates

Description

This visualization method uses the result for either diffusion-dependent or contact-dependent interactions from NEBULA analysis to generate a dot plot. Each dot represents an interaction. The x axis is grouped by covariate levels and jittered to avoid overlap. Therefore, each interaction appears in all the covariate groups. The y axis is the logFC value of the interaction. The size of the dots represents the significance ( $-log_{10}FDR$ ) of an interaction evaluated on a covariate level. Transparency of the dots shows overall significance combining the FDR threshold and logFC threshold.

Usage

plotNebulaAll(
  object,
  intr.type = c("diff", "cont"),
  fdrThresh = 0.05,
  logfcThresh = 1,
  title = NULL,
  titleSize = 16,
  xTextSize = 10,
  xTextAngle = 40,
  yTextSize = 10,
  yTitleSize = 12,
  legendTextSize = 12,
  legendTitleSize = 12,
  stripTitleSize = 12
)
plotNebulaAll(
  object,
  intr.type = c("diff", "cont"),
  fdrThresh = 0.05,
  logfcThresh = 1,
  title = NULL,
  titleSize = 16,
  xTextSize = 10,
  xTextAngle = 40,
  yTextSize = 10,
  yTitleSize = 12,
  legendTextSize = 12,
  legendTitleSize = 12,
  stripTitleSize = 12
)

Arguments

`object`	A mergedCytoSignal object, with `runNEBULA()` run in advance.
`intr.type`	A character string specifying the type of interaction to plot. Choose from `"diff"` (default) for diffusion-dependent or `"cont"` for contact-dependent interactions.
`fdrThresh`	A numeric value to set the FDR threshold for significance. FDR value higher than this threshold will be considered as not significant. Default `0.05`.
`logfcThresh`	A numeric value to set the threshold on the absolute value of logFC. Absoluate logFC value lower than this threshold will be considered as not significant. Default `1`.
`title`	A character string to set the title of the plot. Default `NULL` does not add a title.
`titleSize`	A numeric value to set the size of the title of the plot. Default `16`.
`xTextSize`	A numeric value to set the size of the text on x-axis. Default `10`.
`xTextAngle`	A numeric value to set the angle (in degree) to rotate the x-axis text labels. Default `40`.
`yTextSize`	A numeric value to set the size of the text on y-axis. Default `10`.
`yTitleSize`	A numeric value to set the size of the title on y-axis. Default `12`.
`legendTextSize`	A numeric value to set the size of the text in the legend. Default `12`.
`legendTitleSize`	A numeric value to set the size of the title in the legend. Default `12`.
`stripTitleSize`	A numeric value to set the size of the title in the strip on top of each grid for the covariate groups. Default `12`.

Value

A ggplot figure (gg, ggplot object).

Make volcano plot for NEBULA analysis result

Description

This function allows drawing a volcano plot for the Nebula analysis on a single covariate level, for either diffusion-dependent or contact-dependent interactions. Either static ggplot figure or interactive plotly figure can be generated. Interactions of interests can be labeled with: 1. choosing specific interactions by name or index. 2. choosing top N interactions ranked by significance (FDR and abs(logFC)). A non-NULL highlight argument is considered before topN. When both are not specified, highlight all significant interactions.

Usage

plotNebulaVolcano(
  object,
  covariate,
  intr.type = c("diff", "cont"),
  highlight = NULL,
  topN = NULL,
  fdrThresh = 0.05,
  logfcThresh = 1,
  interactive = TRUE,
  dotSize = 1.3,
  dotAlpha = 0.8,
  xTextSize = 10,
  xTitleSize = 12,
  yTextSize = 10,
  yTitleSize = 12,
  legendTextSize = 12,
  legendTitleSize = 12,
  labelTextSize = 4,
  titleSize = 16
)
plotNebulaVolcano(
  object,
  covariate,
  intr.type = c("diff", "cont"),
  highlight = NULL,
  topN = NULL,
  fdrThresh = 0.05,
  logfcThresh = 1,
  interactive = TRUE,
  dotSize = 1.3,
  dotAlpha = 0.8,
  xTextSize = 10,
  xTitleSize = 12,
  yTextSize = 10,
  yTitleSize = 12,
  legendTextSize = 12,
  legendTitleSize = 12,
  labelTextSize = 4,
  titleSize = 16
)

Arguments

`object`	A mergedCytoSignal object, with `runNEBULA()` run in advance.
`covariate`	A character string of the covariate to plot. Must be available in `showCov(object)`.
`intr.type`	A character string specifying the type of interaction to plot. Choose from `"diff"` (default) for diffusion-dependent or `"cont"` for contact-dependent interactions.
`highlight`	A character vector of interaction names, a numeric or logical vector of indices to select specific interactions to highlight; or a single `TRUE` to highlight all significant interactions. See `showIntr(object, intr.type)` for available options. Default `NULL`.
`topN`	An integer to select top N interactions to highlight ranked by significance. Rank by FDR first and abs(logFC) then. Default `NULL`.
`fdrThresh`	A numeric value to set the FDR threshold for significance. FDR value higher than this threshold will be considered as not significant. Default `0.05`.
`logfcThresh`	A numeric value to set the threshold on the absolute value if logFC. Absoluate logFC value lower than this threshold will be considered as not significant. Default `1`.
`interactive`	Logical, whether to generate an interactive plotly figure. Default `TRUE`. Generate a static ggplot figure if `FALSE`.
`dotSize`	A numeric value to set the size of the dots in the plot. Default `1.3`.
`dotAlpha`	A numeric value to set the transparency of the dots in the plot. Default `0.8`.
`xTextSize`	A numeric value to set the size of the text on x-axis. Default `10`.
`xTitleSize`	A numeric value to set the size of the title on x-axis. Default `12`.
`yTextSize`	A numeric value to set the size of the text on y-axis. Default `10`.
`yTitleSize`	A numeric value to set the size of the title on y-axis. Default `12`.
`legendTextSize`	A numeric value to set the size of the text in the legend. Default `12`.
`legendTitleSize`	A numeric value to set the size of the title in the legend. Default `12`.
`labelTextSize`	A numeric value to set the size of the text on the highlight labels. Default `4`.
`titleSize`	A numeric value to set the size of the main title of the plot. Default `16`.

Value

A plotly figure (plotly, htmlwidget object) if interactive = TRUE.
A ggplot figure (gg, ggplot object) if interactive = FALSE.

Plot the refined score of each interaction after regression model refinement

Description

Plot the refined score of each interaction after regression model refinement

Usage

plotRefinedScore(
  object,
  intrDEGRes,
  intr = NULL,
  pt.size = 0.5,
  pt.stroke = 0.1
)
plotRefinedScore(
  object,
  intrDEGRes,
  intr = NULL,
  pt.size = 0.5,
  pt.stroke = 0.1
)

Arguments

`object`	A `Cytosignal` object with `inferIntrScore` already run.
`intrDEGRes`	The direct output object of `inferIntrDEG`.
`intr`	A vector of unique interaction IDs that are available in `intrDEGRes`, or numerical index within its range. Default `NULL` use all the results.
`pt.size`	Size of the points in the plot. Default `0.5`.
`pt.stroke`	Stroke size of the points in the plot. Default `0.1`.

Value

List of ggplot objects, each shows the refined LR score of each selected interaction.

Create scatter plot from REVIGO result

Description

Create scatter plot from REVIGO result

Usage

plotREVIGO(
  rvg,
  labelBy = c("Name", "TermID"),
  labelIdx = NULL,
  labelSize = 4,
  max.overlaps = 5
)
plotREVIGO(
  rvg,
  labelBy = c("Name", "TermID"),
  labelIdx = NULL,
  labelSize = 4,
  max.overlaps = 5
)

Arguments

`rvg`	A data.frame from `revigo`. Must contain columns: `PC_0`, `PC_1`, `LogSize`, `Frequency`, and `Name`.
`labelBy`	From which field of the revigo result to retrieve label text. Choose from `"Name"` or `"TermID"`. Default `"Name"`.
`labelIdx`	Index matching to rows of `rvg` to indicate whether a label has to be shown for the bubble. Default `NULL` automatically shows tidy view with ggrepel.
`labelSize`	Size of text label for marked dots. Default `4`.
`max.overlaps`	`?ggrepel::geom_text_repel` parameter. Default `5`.

Value

A ggplot object

Examples

rvg <- revigo(c("GO:0003002", "GO:0007389", "GO:0007423", "GO:0009653"),
              speciesTaxon = 10090)
plotREVIGO(rvg)
rvg <- revigo(c("GO:0003002", "GO:0007389", "GO:0007423", "GO:0009653"),
              speciesTaxon = 10090)
plotREVIGO(rvg)

Plot edges by each cluster as sender or receiver cells

Description

By default ranked by the number of significant cells in each cluster

Usage

plotSigCluster(
  object,
  plot_dir,
  cluster.list = NULL,
  intr.num = 10,
  type = c("sender", "receiver"),
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  plot.fmt = "png",
  edge.size = 2000,
  all.in.one = T,
  plot.all.sig = F,
  use.cex = 0.1,
  use.shape = 16,
  line.width = 0.02,
  use.phi = 30,
  use.theta = -17,
  z.scaler = 0.03,
  z.pt.interval = 1,
  pt.stroke = 0.2,
  u_width = 6,
  u_hgt = 6,
  set.res = 400,
  return.plot = F
)
plotSigCluster(
  object,
  plot_dir,
  cluster.list = NULL,
  intr.num = 10,
  type = c("sender", "receiver"),
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  plot.fmt = "png",
  edge.size = 2000,
  all.in.one = T,
  plot.all.sig = F,
  use.cex = 0.1,
  use.shape = 16,
  line.width = 0.02,
  use.phi = 30,
  use.theta = -17,
  z.scaler = 0.03,
  z.pt.interval = 1,
  pt.stroke = 0.2,
  u_width = 6,
  u_hgt = 6,
  set.res = 400,
  return.plot = F
)

Arguments

`object`	A `CytoSignal` object
`plot_dir`	Directory to save plots
`cluster.list`	A list of clusters to plot
`intr.num`	Number of interactions to plot
`type`	Either "sender" or "receiver"
`slot.use`	Slot to use for plotting
`signif.use`	Significance threshold to use for plotting
`colors.list`	A list of colors to use for each cluster
`plot.fmt`	Plot format
`edge.size`	Number of edges to plot
`all.in.one`	Plot all clusters in one plot
`plot.all.sig`	Plot all significant edges
`use.shape`	Shape of points
`line.width`	Width of lines
`use.phi`	Angle of view
`use.theta`	Angle of view
`z.scaler`	Scale of z-axis
`z.pt.interval`	Interval of z-axis
`pt.stroke`	Stroke of points
`u_width`	Width of plot
`u_hgt`	Height of plot
`set.res`	Resolution of plot
`return.plot`	Return plot object
`pt.size`	Size of points
`...`	Other arguments

Value

A list of plots

Plot significant interactions ranked by the user-specified metric

Description

Plot significant interactions ranked by the user-specified metric

Usage

plotSignif(
  object,
  num.plot = NULL,
  res_dir,
  plot.details = T,
  slot.use = NULL,
  signif.use = NULL,
  plot.clusters = T,
  plot.velo = F,
  colors.list = NULL,
  pt.size = 0.1,
  pt.stroke = 0.2,
  u_width = 6,
  u_hgt = 5,
  set.res = 200,
  return.plot = F
)
plotSignif(
  object,
  num.plot = NULL,
  res_dir,
  plot.details = T,
  slot.use = NULL,
  signif.use = NULL,
  plot.clusters = T,
  plot.velo = F,
  colors.list = NULL,
  pt.size = 0.1,
  pt.stroke = 0.2,
  u_width = 6,
  u_hgt = 5,
  set.res = 200,
  return.plot = F
)

Arguments

`object`	A cytosignal object
`num.plot`	Number of interactions to plot
`res_dir`	Directory to save the plots
`plot.details`	Whether to plot NULL imputed values and scores
`slot.use`	The LRscore slot to use for plotting
`signif.use`	The metric used to rank the interactions, by default "result.hq.pear"
`plot.clusters`	Whether to plot the clusters
`plot.velo`	Whether to plot the velocity
`colors.list`	A list of colors to use for plotting
`pt.size`	Size of the points
`pt.stroke`	Stroke of the points
`u_width`	Width of the plot
`u_hgt`	Height of the plot
`set.res`	Resolution of the plot
`return.plot`	Whether to return the plot

Value

A plot if return.plot is TRUE. Otherwise, plots are saved to the specified directory.

Plot significant interactions ranked by the user-specified metric

Description

Plot significant interactions ranked by the user-specified metric

Usage

plotSignif2(
  object,
  intr,
  edge = FALSE,
  velo = FALSE,
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  pt.size = 0.1,
  pt.stroke = 0.2,
  return.plot = FALSE,
  plot_dir = "csSignifPlot/",
  plot.fmt = c("png", "pdf", "svg"),
  raster = NULL,
  resolution = 300,
  verbose = FALSE
)
plotSignif2(
  object,
  intr,
  edge = FALSE,
  velo = FALSE,
  slot.use = NULL,
  signif.use = NULL,
  colors.list = NULL,
  pt.size = 0.1,
  pt.stroke = 0.2,
  return.plot = FALSE,
  plot_dir = "csSignifPlot/",
  plot.fmt = c("png", "pdf", "svg"),
  raster = NULL,
  resolution = 300,
  verbose = FALSE
)

Arguments

`object`	A cytosignal object
`intr`	Specify interactions to be plotted. A vector of either the unique ID of interactions or the numeric indices. Available IDs can be shown with `showIntr(object)`. Availability of an interaction depends on the LRscore slot to be used as well as the significance metric to be used.
`edge`, `velo`	Logical, whether to plot edge or velocity, respectively.
`slot.use`	The LRscore slot to use for plotting
`signif.use`	The metric used to rank the interactions, by default "result.hq.pear"
`colors.list`	A list of colors to use for plotting
`pt.size`	Size of the points
`pt.stroke`	Stroke of the points
`return.plot`	Whether to return the plot
`plot_dir`	Directory to save the plots
`plot.fmt`	Format of output file. "png", "pdf", or "svg".
`resolution`	Resolution of the output figure.
`verbose`	Logical, whether to show progress.

Value

A plot if return.plot is TRUE. Otherwise, plots are saved to the specified directory.

Plot 3D LR-velo ranked by the user-specified metric

Description

Plot 3D LR-velo ranked by the user-specified metric

Usage

plotVelo(
  object,
  intr,
  return.plot = TRUE,
  plot_dir = "csVeloPlot/",
  filename = NULL,
  plot.fmt = c("png", "pdf"),
  slot.use = NULL,
  signif.use = NULL,
  use.clusters = NULL,
  colors.list = NULL,
  z.scaler = 0.03,
  title = NULL,
  pt.size = 0.1,
  use.shape = 16,
  use_xbins = 15,
  use_ybins = 15,
  arrow.line.width = 0.6,
  arrow.width = 0.06,
  pt.stroke = 0.2,
  use.phi = 30,
  use.theta = -17,
  box = TRUE,
  axis.arrow.len = 1,
  width = 6,
  height = 6,
  set.res = 300,
  verbose = TRUE
)
plotVelo(
  object,
  intr,
  return.plot = TRUE,
  plot_dir = "csVeloPlot/",
  filename = NULL,
  plot.fmt = c("png", "pdf"),
  slot.use = NULL,
  signif.use = NULL,
  use.clusters = NULL,
  colors.list = NULL,
  z.scaler = 0.03,
  title = NULL,
  pt.size = 0.1,
  use.shape = 16,
  use_xbins = 15,
  use_ybins = 15,
  arrow.line.width = 0.6,
  arrow.width = 0.06,
  pt.stroke = 0.2,
  use.phi = 30,
  use.theta = -17,
  box = TRUE,
  axis.arrow.len = 1,
  width = 6,
  height = 6,
  set.res = 300,
  verbose = TRUE
)

Arguments

`object`	A cytosignal object
`return.plot`	Whether to return the plot
`slot.use`	The LRscore slot to use for plotting
`signif.use`	The metric used to rank the interactions, by default "result.hq.pear"
`use.clusters`	Plot only selected clusters. Default `NULL` for all clusters.
`colors.list`	A list of colors to use for plotting
`z.scaler`	Scaling factor for the z-axis
`pt.size`	Point size. Default 0.1
`use.shape`	Point shape. Default 16
`use_xbins`	Number of bins for the x-axis. Default 15
`use_ybins`	Number of bins for the y-axis. Default 15
`arrow.line.width`	Width of the arrow line. Default 0.6
`arrow.width`	Width of the arrow. Default 0.06
`pt.stroke`	Stroke of the points
`use.phi`	Set view angel: phi the colatitude. Default 30
`use.theta`	Set view angel: theta gives the azimuthal direction. Default -17
`box`	Whether to show a box panel for the 3D region. Default TRUE
`axis.arrow.len`	When box = FALSE, set the length of the axis arrows.
`width`	Width of the plot. Default 6
`height`	Height of the plot. Default 6
`set.res`	Resolution of the plot. Default 300.
`num.plot`	Number of interactions to plot
`res_dir`	Directory to save the plots
`plot.velo`	Whether to plot the velocity

Value

A plot if return.plot is TRUE. Otherwise, plots are saved to the specified directory.

Print the CytosignalIntrDEG object representation to screen

Description

Print the CytosignalIntrDEG object representation to screen

Usage

## S3 method for class 'CytosignalIntrDEG'
print(x, ...)
## S3 method for class 'CytosignalIntrDEG'
print(x, ...)

Arguments

`x`	A `CytosignalIntrDEG` object.
`...`	Passed to other methods

Remove imputed data and normalized imputed data from CytoSignal object to save disk space

Description

Remove imputed data and normalized imputed data from CytoSignal object to save disk space

Usage

purgeBeforeSave(object, purge.raw = TRUE, purge.null = FALSE)
purgeBeforeSave(object, purge.raw = TRUE, purge.null = FALSE)

Arguments

object

CytoSignal object

Value

a CytoSignal object

Rank the inferred high-quality interactions by their spatial variability

Description

This function utilizes SPARK package to calculate the spatial variability of the high-quality interactions, using their LR scores. Please refer to Jiaqiang Zhu, et al., 2021, Genome Biology for more details of the method.

Usage

rankIntrSpatialVar(object, slot.use = NULL, numCores = 1, verbose = FALSE)
rankIntrSpatialVar(object, slot.use = NULL, numCores = 1, verbose = FALSE)

Arguments

`object`	A CytoSignal object, with `inferSignif` already run.
`slot.use`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.
`numCores`	SPARK::sparkx parameter, an integer specifying the number of threads.
`verbose`	SPARK::sparkx parameter, a logical value indicating whether to print details for debug purpose

Value

The input CytoSignal object with the spatially variable high-quality interaction list updated at object@lrscore[[slot.use]]@res.list$result.spx

Examples

## Not run: 
object <- findNN(object)
object <- imputeLR(object)
object <- inferScoreLR(object, lig.slot = "GauEps", recep.slot = "Raw",
                       intr.db.name = "diff_dep")
object <- permuteLR(object)
object <- inferNullScoreLR(object)
object <- inferSignif(object)
object <- rankIntrSpatialVar(object)

## End(Not run)
## Not run: 
object <- findNN(object)
object <- imputeLR(object)
object <- inferScoreLR(object, lig.slot = "GauEps", recep.slot = "Raw",
                       intr.db.name = "diff_dep")
object <- permuteLR(object)
object <- inferNullScoreLR(object)
object <- inferSignif(object)
object <- rankIntrSpatialVar(object)

## End(Not run)

Remove low quality cells and genes from raw counts

Description

Remove low quality cells and genes from raw counts

Usage

removeLowQuality(object, counts.thresh = 300, gene.thresh = 50)
removeLowQuality(object, counts.thresh = 300, gene.thresh = 50)

Arguments

`object`	CytoSignal object
`counts.thresh`	threshold for cell counts
`gene.thresh`	threshold for gene counts

Value

a CytoSignal object

Submit query to REVIGO and get result to R

Description

This function gets an input of vector of GO terms and submit query to REVIGO via HTTP, and retrieves the result from the job. The code is based on example provided by REVIGO, originally located at http://revigo.irb.hr/FAQ

Please visit http://revigo.irb.hr/ for the web-based interactive tool, and please see https://doi.org/10.1371/journal.pone.0021800 for detailed introduction to the functionality.

Usage

revigo(
  GOterms,
  cutoff = 0.7,
  value = NULL,
  valueType = c("PValue", "Higher", "Lower", "HigherAbsolute", "HigherAbsLog2"),
  speciesTaxon = "0",
  measure = c("SIMREL", "LIN", "RESNIK", "JIANG"),
  removeObsolete = TRUE
)
revigo(
  GOterms,
  cutoff = 0.7,
  value = NULL,
  valueType = c("PValue", "Higher", "Lower", "HigherAbsolute", "HigherAbsLog2"),
  speciesTaxon = "0",
  measure = c("SIMREL", "LIN", "RESNIK", "JIANG"),
  removeObsolete = TRUE
)

Arguments

`GOterms`	Character vector of GO term IDs.
`cutoff`	Size of the result, between 0.4 to 0.9. Larger value returns more result. Default `0.7`.
`value`	A vector of additional information for each GO term. Default `NULL` does not include additional information. Type of the value has to be specified with `valueType`.
`valueType`	Type of `value`. Default `"PValue"`. Optionally, `"Higher"`, `"Lower"`, `"HigherAbsolute"`, `"HigherAbsLog2"`.
`speciesTaxon`	Taxon ID of the species. Default `"0"` queries to all available species. `"10090"` is for mouse and `"9606"` is for human.
`measure`	The semantic similarity measure to use. Default `"SIMREL"`. Optionally, `"LIN"`, `"RESNIK"`, `"JIANG"`.
`removeObsolete`	Logical. Whether to remove obsolete GO terms. Default `TRUE`.

Value

A data.frame with many information

Examples

rvg <- revigo(c("GO:0003002", "GO:0007389", "GO:0007423", "GO:0009653"),
              speciesTaxon = 10090)
rvg <- revigo(c("GO:0003002", "GO:0007389", "GO:0007423", "GO:0009653"),
              speciesTaxon = 10090)

Run NEBULA's association analysis to compare datasets within mergedCytoSignal object

Description

After merging multiple CytoSignal objects and adding metadata covariates as needed for modeling the comparison, this function runs NEBULA's association analysis to identify significant interactions between cells from different datasets and of different covariate levels.

Usage

runNEBULA(object, covariates, ncore = 1L, verbose = TRUE)
runNEBULA(object, covariates, ncore = 1L, verbose = TRUE)

Arguments

`object`	A mergedCytoSignal object
`covariates`	A character vector of metadata covariates to include in the model.
`ncore`	Integer, number of cores to use for parallel computation. Default `1L`.
`verbose`	Logical, print progress messages. Default `TRUE`.

Value

Input object with slot diff.results and cont.results updated with the results from NEBULA analysis.

Identify spatially significant interactions using std-corrected pearson correlation Normal Moran's I test is not applicable here since the total number of the cell is too large, causing unnacceptable computation cost. Here we use a modified version of Moran's I test, which is to take only the top KNNs to compute the Moran's I test.

Description

Identify spatially significant interactions using std-corrected pearson correlation Normal Moran's I test is not applicable here since the total number of the cell is too large, causing unnacceptable computation cost. Here we use a modified version of Moran's I test, which is to take only the top KNNs to compute the Moran's I test.

Usage

runPears.std(object, k = 10, weight = 2, score.slot = NULL)
runPears.std(object, k = 10, weight = 2, score.slot = NULL)

Arguments

`object`	A Cytosignal object
`k`	The number of nearest neighbors to use
`weight`	The weight of the nearest neighbors
`score.slot`	Which LR score to use. Use the name specified with `tag` when running `inferLRScore`.

Value

A Cytosignal object

show method for CytoSignal

Description

show method for CytoSignal

Usage

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

Arguments

object

CytoSignal object

show method for cytosignal obj

Description

show method for cytosignal obj

Usage

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

Show method for mergedCytoSignal object

Description

Show method for mergedCytoSignal object

Usage

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

Arguments

object

A mergedCytoSignal object

Value

Show a message with object summary, no return value

Show available covariate levels used in NEBULA analysis

Description

Show available covariate levels used in NEBULA analysis

Usage

showCov(object, intr.type = NULL)
showCov(object, intr.type = NULL)

Arguments

`object`	A mergedCytoSignal object
`intr.type`	A character string specifying the type of interaction to show covariate levels for. Choose from `"diff"` for diffusion-dependent or `"cont"` for contact-dependent interactions. Default `NULL` returns the covariate levels used for both if they are the same, which is true most of the time.

Value

A character vector

show method for ImpData

Description

show method for ImpData

Usage

showImp(object, slot.use = NULL)
showImp(object, slot.use = NULL)

Arguments

`object`	CytoSignal object
`slot.use`	slot to use

show method for CytoSignal

Description

show method for CytoSignal

Usage

showIntr(object, ...)

## S3 method for class 'CytoSignal'
showIntr(object, slot.use = NULL, signif.use = NULL, return.name = FALSE, ...)

## S3 method for class 'mergedCytoSignal'
showIntr(object, intr.type, ...)
showIntr(object, ...)

## S3 method for class 'CytoSignal'
showIntr(object, slot.use = NULL, signif.use = NULL, return.name = FALSE, ...)

## S3 method for class 'mergedCytoSignal'
showIntr(object, intr.type, ...)

Arguments

`object`	A CytoSignal or mergedCytoSignal object
`...`	S3 method options for different classes.
`slot.use`	The LR score slot to use. Choose from `names(object@lrscore)`. Default `NULL` uses whichever last updated.
`signif.use`	Which level of significance inference to use for the LR score slot selected. Choose from `names(object@lrscore[[slot.use]]@res.list)`. Default `NULL` uses whichever last updated.
`return.name`	Whether to return interaction name that comes with the form of "ligand-receptor", instead of showing the unique IDs. Default `FALSE`.
`intr.type`	Which type of interactions to show. Choose from `"diff"` or `"cont"`.

Value

For CytoSignal object, a character vector of unique IDs of the interactions available to the specified slot. When return.name = TRUE, a named character vector where the names are the IDs and the values are interaction names with the form of "ligand-receptor".
For mergedCytoSignal object, a character vector "ligand-receptor" pair names.

show all current logs

Description

show all current logs

Usage

showLog(object)
showLog(object)

Arguments

object

CytoSignal object

show method for lrScores

Description

show method for lrScores

Usage

showScore(object, slot.use = NULL)
showScore(object, slot.use = NULL)

Arguments

`object`	CytoSignal object
`slot.use`	slot to use

show intr.data in ImpData

Description

show intr.data in ImpData

Usage

showUnpt(object, slot.use = NULL)
showUnpt(object, slot.use = NULL)

Arguments

`object`	CytoSignal object
`slot.use`	slot to use

show method for lrVelo

Description

show method for lrVelo

Usage

showVelo(object, slot.use = NULL)
showVelo(object, slot.use = NULL)

Arguments

`object`	CytoSignal object
`slot.use`	slot to use

Suggest scaling factor of real units to spatial units

Description

This function is used to estimate the scaling facor of real units to spatial units. Returns the top 5 possible scaling factors, users can choose to which to use.

Usage

suggestScaleFactor(object, cell.intervel = NULL)
suggestScaleFactor(object, cell.intervel = NULL)

Arguments

`object`	CytoSignal object
`cell.intervel`	Distance between two cell in physical units

Value

a vector of possible scaling factors

Generate character vector of unique color hex codes Base color palette adopts `ggsci:::ggsci_db$igv$default`

Description

Generate character vector of unique color hex codes Base color palette adopts ggsci:::ggsci_db$igv$default

Usage

uniqueColors(n)
uniqueColors(n)

Arguments

`n`	Number of unique colors to generate. When not exceeding existing number of colors, just return the first `n` colors, otherwise generate interpolated colors.

Package 'cytosignal'

Help Index

Description

Usage

Extract metadata variable from mergedCytoSignal object

Description

Usage

Add interaction database to CytoSignal object

Description

Usage

Arguments

Value

Add velocity data to CytoSignal object

Description

Usage

Arguments

Subset gene expression matrix according to availability in the UNIPROT database.

Description

Usage

Arguments

Value

Create a CytoSignal object

Description

Usage

Arguments

Value

The CytoSignal Class

Description

Slots

Interaction database derived from CellphoneDB V2

Description

Usage

Format

Source

dim Method for mergedCytoSignal object

Description

Usage

Arguments

Value

Identify nearest neighbors for each location using different strategies for different types of interactions

Description

Usage

Arguments

Value

Examples

Find the direct connected neighbor of each cell, using Delaunay triangulation

Description

Usage

Arguments

Value

Find the neighbors of each cell in the Epsilon Ball

Description

Usage

Arguments

Value

Create a ImpData object using raw data without imputation

Description

Usage

Arguments

Value

Format a CytosignalIntrDEG object to string

Description

Usage

Arguments

Value

Compute the LR score for specific ligand-receptor imputation obj pairs

Description

Usage

Arguments

Value

Sub function for graphNicheLR, input a CytoSignal object

Description

Usage

Arguments

Value

Sub function for graphNicheLR, input a sparse matrix

Description

Usage

Arguments

Value

Impute the $L$ or $R$ value from the nearest neighbors of each location

Impute time derivative of $L$ or $R$ from the nearest neighbors of each location

Calculate the interaction velocity from the imputed time derivative of $L$ or $R$ values