Package 'iTALK'

Convert Expression Matrix Between Species

Description

Applies species conversion mapping to an expression matrix, handling one-to-many gene mappings with specified aggregation method.

Usage

convert_expression_matrix(
  expr_matrix,
  gene_mapping,
  handle_duplicates = c("mean", "sum", "max")
)

Arguments

expr_matrix	Matrix or data.frame. Expression data (genes x cells/samples)
gene_mapping	Data.frame. Output from convert_species_biomart()$mapping
handle_duplicates	Character. Method for aggregating expression when multiple source genes map to one target gene: "mean": Average expression (default, conservative) "sum": Sum expression (appropriate for count data) "max": Maximum expression

Value

List with elements:

• expr_matrix: Converted expression matrix (target genes x cells)

• conversion_info: data.frame with mapping details

• stats: list with conversion statistics

Examples

## Not run: 
# After obtaining mapping
mapping_result <- convert_species_biomart(
  genes = rownames(mouse_expr),
  from_species = "Mus_musculus"
)

# Convert expression matrix
converted <- convert_expression_matrix(
  expr_matrix = mouse_expr,
  gene_mapping = mapping_result$mapping,
  handle_duplicates = "mean"
)

# Use converted matrix
human_expr <- converted$expr_matrix

## End(Not run)

---

Convert Genes Between Species Using BioMart

Description

Converts gene symbols between species using Ensembl BioMart ortholog mappings. Provides accurate, biologically-validated homolog mappings rather than simple name transformation.

Usage

convert_species_biomart(
  genes,
  from_species,
  to_species = "Homo_sapiens",
  ensembl_version = 103,
  mirror = NULL,
  cache = TRUE,
  max_tries = 5
)

Arguments

genes	Character vector. Gene symbols to convert
from_species	Character. Source species: "Homo_sapiens" (human) "Mus_musculus" (mouse)
to_species	Character. Target species (default: "Homo_sapiens")
ensembl_version	Character or numeric. Ensembl version (default: 103). Using a fixed version ensures reproducibility. Use "current_release" for latest version.
mirror	Character or NULL. Ensembl mirror for faster access: "www": Main server (Europe) "uswest": US West Coast "useast": US East Coast "asia": Asia
cache	Logical. Cache BioMart results for faster repeated queries (default: TRUE)
max_tries	Integer. Maximum retry attempts for network operations (default: 5)

Details

**Ortholog Mapping**: Uses Ensembl's "associated_gene_name" attribute which provides the primary ortholog symbol. For mouse→human conversion, this maps:

• Tgfb1 → TGFB1

• Vegfa → VEGFA

• Ctnnb1 → CTNNB1

**One-to-Many Mappings**: Some genes have multiple orthologs (e.g., Tgfb1 might map to TGFB1, TGFB2, TGFB3). By default, all mappings are returned. Downstream functions handle aggregation.

**Caching**: When cache=TRUE, results are stored using R.cache with key based on:

• Gene set (hashed)

• Source and target species

• Ensembl version

Cache dramatically speeds up repeated analyses.

**Network Requirements**: Requires internet connection to query Ensembl BioMart (first time). Queries typically take 10-30 seconds depending on gene count and network speed.

Value

List with elements:

• mapping: data.frame with columns from_gene, to_gene

• unmapped: character vector of genes without orthologs

• stats: list with mapping statistics (n_input, n_mapped, mapping_rate, etc.)

• cache_key: cache identifier (if cache=TRUE)

Examples

## Not run: 
# Convert mouse genes to human
result <- convert_species_biomart(
  genes = c("Tgfb1", "Vegfa", "Ctnnb1"),
  from_species = "Mus_musculus",
  to_species = "Homo_sapiens"
)

# Check mapping
result$mapping
#   from_gene to_gene
#   Tgfb1     TGFB1
#   Vegfa     VEGFA
#   Ctnnb1    CTNNB1

# Check statistics
result$stats$mapping_rate  # Proportion successfully mapped

# Unmapped genes
result$unmapped

## End(Not run)

---

Ligand-Receptor Interaction Database

Description

A data frame containing ligand-receptor pairs for cell-cell communication analysis.

Usage

database

Format

A data frame with columns:

Pair.Name

Name of the ligand-receptor pair

Ligand.ApprovedSymbol

Official gene symbol of the ligand

Ligand.Name

Full name of the ligand

Receptor.ApprovedSymbol

Official gene symbol of the receptor

Receptor.Name

Full name of the receptor

Classification

Type of interaction (e.g., Cytokine, Growth factor)

Source

Curated from multiple public databases including CellPhoneDB, CellChatDB, and literature.

Examples

data(database)
head(database)

---

Call DEGenes

Description

This function loads the data as a dataframe, and method as a string. It assumes that each line contains gene expression profile of one single cell, and each column contains the one single gene expression profile in different cells. The dataframe should also contain the cell type information with column name 'cell_type', as well as group information as 'compare_group' Batch information as 'batch' is optional. If included, users may want to use the raw count data for later analysis. Differential expressed genes will be called within each cell type by the method users select. For bulk RNAseq, we provide edgeR, DESeq2. And for scRNA-seq, popular methods in packages scde, monocle, DEsingle and MAST are available.

Usage

DEG(
  data,
  method,
  min_gene_expressed = 0,
  min_valid_cells = 0,
  contrast = NULL,
  q_cut = 0.05,
  add = TRUE,
  top = 50,
  stats = "mean",
  ...
)

Arguments

data	Input raw or normalized count data with column 'cell_type' and 'compare_group'
method	Method used to call DEGenes. Available options are: Wilcox: Wilcoxon rank sum test DESeq2: Negative binomial model based differential analysis (Love et al, Genome Biology, 2014) SCDE: Bayesian approach to single-cell differential expression analysis (Kharchenko et al, Nature Method, 2014) monocle: Census based differential analysis (Qiu et al, Nature Methods, 2017) edgeR: Negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests based differential analysis (McCarthy et al, Nucleic Acids Research, 2012) DESingle: Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes (Miao et al, Bioinformatics, 2018) MAST: GLM-framework that treates cellular detection rate as a covariate (Finak et al, Genome Biology, 2015)
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
q_cut	Cut-off for q value
add	Whether add genes that are not differentially expressed but highly expressed for finding the significant pairs later
top	Same as in function rawParse
stats	Same as in function rawParse
...	Additional arguments passed to the specific differential expression test function

Value

A matrix of the differential expressed genes

---

Differential expression using DESeq2

Description

Identifies differentially expressed genes between two groups of cells using DESeq2

Usage

DESeq2Test(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  test = "Wald",
  fitType = "parametric",
  sfType = "ratio",
  betaPrior = FALSE,
  quiet = FALSE,
  modelMatrixType = "standard",
  minReplicatesForReplace = 7,
  useT = FALSE,
  minmu = 0.5,
  parallel = FALSE,
  BPPARAM = NULL
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
test	either "Wald" or "LRT", which will then use either Wald significance tests (defined by nbinomWaldTest), or the likelihood ratio test on the difference in deviance between a full and reduced model formula (defined by nbinomLRT)
fitType	either "parametric", "local", or "mean" for the type of fitting of dispersions to the mean intensity. See estimateDispersions in DESeq2 for description.
sfType	either "ratio", "poscounts", or "iterate" for the type of size factor estimation. See estimateSizeFactors in DESeq2 for description.
betaPrior	whether or not to put a zero-mean normal prior on the non-intercept coefficients. See nbinomWaldTest for description of the calculation of the beta prior. In versions >=1.16, the default is set to FALSE, and shrunken LFCs are obtained afterwards using lfcShrink.
quiet	whether to print messages at each step
modelMatrixType	either "standard" or "expanded", which describe how the model matrix, X of the GLM formula is formed. "standard" is as created by model.matrix using the design formula. "expanded" includes an indicator variable for each level of factors in addition to an intercept. For more information see the Description of nbinomWaldTest. betaPrior must be set to TRUE in order for expanded model matrices to be fit.
minReplicatesForReplace	the minimum number of replicates required in order to use replaceOutliers on a sample. If there are samples with so many replicates, the model will be refit after these replacing outliers, flagged by Cook's distance. Set to Inf in order to never replace outliers.
useT	logical, passed to nbinomWaldTest, default is FALSE, where Wald statistics are assumed to follow a standard Normal
minmu	lower bound on the estimated count for fitting gene-wise dispersion and for use with nbinomWaldTest and nbinomLRT
parallel	if FALSE, no parallelization. if TRUE, parallel execution using BiocParallel, see next argument BPPARAM. A note on running in parallel using BiocParallel: it may be advantageous to remove large, unneeded objects from your current R environment before calling DESeq, as it is possible that R's internal garbage collection will copy these files while running on worker nodes.
BPPARAM	an optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, the parameters last registered with register will be used.

Details

This test does not support pre-processed genes. To use this method, please install DESeq2, using the instructions at https://bioconductor.org/packages/release/bioc/html/DESeq2.html

Value

A matrix of differentially expressed genes and related statistics.

References

Love MI, Huber W and Anders S (2014). "Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2." Genome Biology. https://bioconductor.org/packages/release/bioc/html/DESeq2.html

---

Differential expression using DEsingle

Description

Identifies differentially expressed genes between two groups of cells using DEsingle

Usage

DESingleTest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  parallel = FALSE,
  BPPARAM = NULL
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
parallel	If FALSE (default), no parallel computation is used; if TRUE, parallel computation using BiocParallel, with argument BPPARAM.
BPPARAM	An optional parameter object passed internally to bplapply when parallel=TRUE. If not specified, bpparam() (default) will be used.

Details

This test does not support pre-processed genes. To use this method, please install DEsingle, using the instructions at https://github.com/miaozhun/DEsingle

Value

A matrix of differentially expressed genes and related statistics.

References

Zhun Miao, Ke Deng, Xiaowo Wang, Xuegong Zhang (2018). DEsingle for detecting three types of differential expression in single-cell RNA-seq data. Bioinformatics, bty332. 10.1093/bioinformatics/bty332.

---

Detect Species from Gene Names

Description

Automatically detects species based on gene naming patterns with high confidence. Uses statistical analysis of naming conventions to distinguish human vs mouse genes.

Usage

detect_species(genes, confidence_threshold = 0.7)

Arguments

genes	Character vector. Gene symbols to analyze
confidence_threshold	Numeric. Minimum confidence score (0-1) to return a species determination (default: 0.7)

Details

**Detection Logic**:

• Human genes: ALL UPPERCASE (TGFB1, VEGFA, CD8A)

• Mouse genes: First letter uppercase, rest lowercase (Tgfb1, Vegfa, Cd8a)

Analyzes up to 100 genes and calculates proportion matching each pattern. Species is determined if confidence exceeds threshold (default 70

**Marker Gene Validation** (future enhancement): Could be enhanced to check for species-specific marker genes like:

• Human-specific: HBA1, HBB (hemoglobin)

• Mouse-specific: Gm genes (predicted genes)

Value

List with elements:

• species: "Homo_sapiens", "Mus_musculus", or "unknown"

• confidence: Confidence score (0-1)

• method: Detection method used

• patterns: List of pattern statistics

Examples

## Not run: 
# Detect human genes
detect_species(c("TGFB1", "VEGFA", "CTNNB1"))
# Returns: list(species = "Homo_sapiens", confidence = 1.0)

# Detect mouse genes
detect_species(c("Tgfb1", "Vegfa", "Ctnnb1"))
# Returns: list(species = "Mus_musculus", confidence = 1.0)

# Mixed or ambiguous
detect_species(c("TGFB1", "Vegfa", "CD8A", "Ctnnb1"))
# Returns: list(species = "unknown", confidence = 0.5)

## End(Not run)

---

Differential expression using edgeR

Description

Identifies differentially expressed genes between two groups of cells using edgeR

Usage

edgeRTest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  calcNormMethod = "TMM",
  trend.method = "locfit",
  tagwise = TRUE,
  robust = FALSE
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
calcNormMethod	normalization method to be used
trend.method	method for estimating dispersion trend. Possible values are "none", "movingave", "loess" and "locfit" (default).
tagwise	logical, should the tagwise dispersions be estimated
robust	logical, should the estimation of prior.df be robustified against outliers

Details

This test does not support pre-processed genes. To use this method, please install edgeR, using the instructions at http://bioconductor.org/packages/release/bioc/html/edgeR.html

Value

A matrix of differentially expressed genes and related statistics.

References

McCarthy, J. D, Chen, Yunshun, Smyth, K. G (2012). “Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation.” Nucleic Acids Research, 40(10), 4288-4297.

Robinson MD, McCarthy DJ, Smyth GK (2010). “edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.” Bioinformatics, 26(1), 139-140. https://github.com/cole-trapnell-lab/monocle-release

---

Finding ligand-receptor pairs

Description

This function loads the highly expressed genes or differentail expressed genes as a dataframe. Significant interactions are found through mapping these genes to our ligand-receptor database.

Usage

FindLR(
  data_1,
  data_2 = NULL,
  datatype,
  comm_type,
  database = NULL,
  convert_species = TRUE,
  ensembl_version = 103,
  mirror = NULL,
  cache = TRUE
)

Arguments

data_1	Data used to find the ligand-receptor pairs
data_2	Second dataset used to find ligand-receptor pairs. If set NULL, paris will be found within data_1. Otherwise, pairs will be found between data_1 and data_2. Default is NULL.
datatype	Type of data used as input. Options are "mean count" and "DEG"
comm_type	Communication type. Available options are "cytokine", "checkpoint", "growth factor", "other"
database	Database used to find ligand-receptor pairs. If set NULL, the build-in database will be used.
convert_species	Logical. Enable automatic species conversion (default: TRUE). When TRUE, automatically detects mouse genes and converts to human orthologs.
ensembl_version	Ensembl version for gene conversion (default: 103)
mirror	Ensembl mirror for faster access (default: NULL)
cache	Cache conversion results (default: TRUE)

Value

A dataframe of the significant interactions

References

Cytokines, Inflammation and Pain. Zhang et al,2007.

Cytokines, Chemokines and Their Receptors. Cameron et al, 2000-2013

Robust prediction of response to immune checkpoint blockade therapy in metastatic melanoma. Auslander et al, 2018.

A draft network of ligand-receptor-mediated multicellular signalling in human, Jordan A. Ramilowski, Nature Communications, 2015

---

Plotting ligand-receptor pairs

Description

This function loads the significant interactions as a dataframe. A circle plot will be generated using package circlize. The width of the arrow represents the expression level/log fold change of the ligand; while the width of arrow head represents the expression level/log fold change of the receptor. Different color and the type of the arrow stands for whether the ligand and/or receptor are upregulated or downregulated. Users can select the colors represent the cell type by their own or chosen randomly by default.

Usage

LRPlot(
  data,
  datatype,
  gene_col = NULL,
  transparency = 0.5,
  link.arr.lwd = 1,
  link.arr.lty = NULL,
  link.arr.col = NULL,
  link.arr.width = NULL,
  link.arr.type = NULL,
  facing = "clockwise",
  cell_col = NULL,
  print.cell = TRUE,
  track.height_1 = uh(2, "mm"),
  track.height_2 = uh(12, "mm"),
  annotation.height_1 = 0.01,
  annotation.height_2 = 0.01,
  text.vjust = "0.4cm",
  ...
)

Arguments

data	A dataframe contains significant ligand-receptor pairs and related information such as expression level/log fold change and cell type
datatype	Type of data. Options are "mean count" and "DEG"
gene_col	Colors used to represent different categories of genes.
transparency	Transparency of link colors, 0 means no transparency and 1 means full transparency. If transparency is already set in col or row.col or column.col, this argument will be ignored. NAalso ignores this argument.
link.arr.lwd	line width of the single line link which is put in the center of the belt.
link.arr.lty	line type of the single line link which is put in the center of the belt.
link.arr.col	color or the single line link which is put in the center of the belt.
link.arr.width	size of the single arrow head link which is put in the center of the belt.
link.arr.type	Type of the arrows, pass to Arrowhead. Default value is triangle. There is an additional option big.arrow
facing	Facing of text.
cell_col	Colors used to represent types of cells. If set NULL, it will be generated randomly
print.cell	Whether or not print the type of cells on the outer layer of the graph.
track.height_1	height of the cell notation track
track.height_2	height of the gene notation track
annotation.height_1	Track height corresponding to values in annotationTrack.
annotation.height_2	Track height corresponding to values in annotationTrack.
text.vjust	adjustment on 'vertical' (radical) direction. Besides to set it as numeric values, the value can also be a string contain absoute unit, e.g. "2.1mm", "-1 inche", but only "mm", "cm", "inches"/"inche" are allowed.
...	Additional arguments passed to circlize plotting functions

Value

A figure of the significant interactions

References

Gu, Z. (2014) circlize implements and enhances circular visualization in R. Bioinformatics.

---

Differential expression using MAST

Description

Identifies differentially expressed genes between two groups of cells using MAST

Usage

MASTTest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  method = "glm",
  silent = FALSE,
  check_logged = TRUE
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
method	Character vector, either 'glm', 'glmer' or 'bayesglm'
silent	Common problems with fitting some genes
check_logged	Set FALSE to override sanity checks that try to ensure that the default assay is log-transformed and has at least one exact zero

Details

To use this method, please install MAST, using the instructions at https://github.com/RGLab/MAST

Value

A matrix of differentially expressed genes and related statistics.

References

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data G Finak, A McDavid, M Yajima, J Deng, V Gersuk, AK Shalek, CK Slichter et al Genome biology 16 (1), 278

---

Differential expression using monocle

Description

Identifies differentially expressed genes between two groups of cells using monocle

Usage

MonocleTest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  batch = NULL,
  cores = 4
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
batch	Different batch identifier
cores	The number of cores to be used while testing each gene for differential expression.

Details

This test does not support pre-processed genes. To use this method, please install monocle, using the instructions at https://bioconductor.org/packages/release/bioc/html/monocle.html

Value

A matrix of differentially expressed genes and related statistics.

References

Qiu X, Hill A, Packer J, Lin D, Ma Y, Trapnell C (2017). “Single-cell mRNA quantification and differential analysis with Census.” Nature Methods. https://github.com/cole-trapnell-lab/monocle-release

---

Network Viewing of cell-cell communication

Description

This function loads the significant interactions as a dataframe, and colors represent different types of cells as a structure. The width of edges represent the strength of the communication. Labels on the edges show exactly how many interactions exist between two types of cells.

Usage

NetView(
  data,
  col,
  label = TRUE,
  edge.curved = 0.5,
  shape = "circle",
  layout = igraph::nicely(),
  vertex.size = 20,
  margin = 0.2,
  vertex.label.cex = 1.5,
  vertex.label.color = "black",
  arrow.width = 1.5,
  edge.label.color = "black",
  edge.label.cex = 1,
  edge.max.width = 10
)

Arguments

data	A dataframe containing ligand-receptor pairs and corresponding cell typesused to do the plotting
col	Colors used to represent different cell types
label	Whether or not shows the label of edges (number of connections between different cell types)
edge.curved	Specifies whether to draw curved edges, or not. This can be a logical or a numeric vector or scalar. First the vector is replicated to have the same length as the number of edges in the graph. Then it is interpreted for each edge separately. A numeric value specifies the curvature of the edge; zero curvature means straight edges, negative values means the edge bends clockwise, positive values the opposite. TRUE means curvature 0.5, FALSE means curvature zero
shape	The shape of the vertex, currently “circle”, “square”, “csquare”, “rectangle”, “crectangle”, “vrectangle”, “pie” (see vertex.shape.pie), ‘sphere’, and “none” are supported, and only by the plot.igraph command. “none” does not draw the vertices at all, although vertex label are plotted (if given). See shapes for details about vertex shapes and vertex.shape.pie for using pie charts as vertices.
layout	The layout specification. It must be a call to a layout specification function.
vertex.size	The size of vertex
margin	The amount of empty space below, over, at the left and right of the plot, it is a numeric vector of length four. Usually values between 0 and 0.5 are meaningful, but negative values are also possible, that will make the plot zoom in to a part of the graph. If it is shorter than four then it is recycled.
vertex.label.cex	The label size of vertex
vertex.label.color	The color of label for vertex
arrow.width	The width of arrows
edge.label.color	The color for single arrow
edge.label.cex	The size of label for arrows
edge.max.width	The maximum arrow size

Value

A network graph of the significant interactions

References

Csardi G, Nepusz T: The igraph software package for complex network research, InterJournal, Complex Systems 1695. 2006. http://igraph.org

---

Parsing the data to get top expressed genes

Description

This function loads the count data as a dataframe. It assumes that each line contains gene expression profile of one single cell, and each column contains the one single gene expression profile in different cells. The dataframe should also contain the cell type information with column name 'cell_type'. Group information should also be included as 'compare_group' if users want to call differntial expressed ligand-receptor pairs. Batch information as 'batch' is optional. If included, users may want to use the raw count data for later analysis.

Usage

rawParse(data, top_genes = 50, stats = "mean")

Arguments

data	Input data, raw or normalized count with 'cell_type' column
top_genes	(scale 1 to 100) Top percent highly expressed genes used to find ligand-receptor pairs, default is 50
stats	Whether calculates the mean or the median of the data. Available options are 'mean' and 'median'.

Value

A dataframe of the data

---

Differential expression using scde

Description

Identifies differentially expressed genes between two groups of cells using scde

Usage

SCDETest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  batch = NULL,
  n.randomizations = 150,
  n.cores = 10,
  batch.models = NULL,
  return.posteriors = FALSE,
  verbose = 1
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
batch	Different batch identifier
n.randomizations	number of bootstrap randomizations to be performed
n.cores	number of cores to utilize
batch.models	(optional) separate models for the batch data (if generated using batch-specific group argument). Normally the same models are used.
return.posteriors	whether joint posterior matrices should be returned
verbose	integer verbose level (1 for verbose)

Details

This test does not support pre-processed genes. To use this method, please install scde, using the instructions at http://hms-dbmi.github.io/scde/tutorials.html

Value

A matrix of differentially expressed genes and related statistics.

References

"Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi:10.1038/nmeth.2967) https://github.com/hms-dbmi/scde

---

Species Conversion System for iTALK

Description

Complete species conversion framework enabling iTALK to work seamlessly with mouse, human, and other species data by mapping genes to orthologs using Ensembl BioMart.

Details

iTALK's ligand-receptor database uses human gene symbols (e.g., TGFB1, VEGFA). This module automatically detects input species and converts gene names to human orthologs for database matching, then optionally converts results back.

---

Plotting ligand-receptor pairs

Description

This function loads count data as dataframe, ligand, receptor and two interactive cells' names as strings. The plot shows the expression level of ligand and receptor at different time, thus illustrates a dynamic change of a ligand-receptor pairs.

Usage

TimePlot(data, ligand, receptor, cell_from, cell_to, Time = NULL)

Arguments

data	A dataframe contains significant ligand-receptor pairs and related information such as expression level/log fold change and cell type
ligand	String as selected ligand
receptor	String as selected receptor
cell_from	The cell type ligand gene belongs to
cell_to	The cell type receptor gene belongs to
Time	Different time points showing on the plot

Value

A figure of the paired interactions

---

Differential expression using wilcox

Description

Identifies differentially expressed genes between two groups of cells using a Wilcoxon Rank Sum test

Usage

WilcoxTest(
  sub_data,
  min_gene_expressed,
  min_valid_cells,
  contrast = unique(sub_data$compare_group),
  datatype = "raw count",
  verbose = FALSE
)

Arguments

sub_data	Count data removed cell_type and selected certain two compare_group
min_gene_expressed	Genes expressed in minimum number of cells
min_valid_cells	Minimum number of genes detected in the cell
contrast	String vector specifying the contrast to be tested against the log2-fold-change threshold
datatype	Type of data. Available options are: 'raw data': Raw count data without any pre-processing 'log count': Normalized and log-transformed data
verbose	Whether show the progress of computing

Value

A matrix of differentially expressed genes and related statistics.

---