Title:	Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Version:	1.1.1
Description:	Provides a comprehensive toolkit for single-cell annotation with the 'CellMarker2.0' database (see Xia Li, Peng Wang, Yunpeng Zhang (2023) <doi:10.1093/nar/gkac947>). Streamlines biological label assignment in single-cell RNA-seq data and facilitates transcriptomic analysis, including preparation of TCGAhttps://portal.gdc.cancer.gov/ and GEOhttps://www.ncbi.nlm.nih.gov/geo/ datasets, differential expression analysis and visualization of enrichment analysis results. Additional utility functions support various bioinformatics workflows. See Wei Cui (2024) <doi:10.1101/2024.09.14.609619> for more details.
URL:	https://github.com/person-c/easybio
BugReports:	https://github.com/person-c/easybio/issues
License:	MIT + file LICENSE
Encoding:	UTF-8
RoxygenNote:	7.3.2
Imports:	data.table, ggplot2, httr2, R6, xml2
Depends:	R (≥ 4.1.0)
LazyData:	true
Suggests:	knitr, patchwork, rmarkdown, ggrepel, Seurat, limma, GEOquery, fgsea, edgeR, testthat (≥ 3.0.0)
biocViews:	limma, GEOquery, edgeR, fgsea
Language:	en-US
VignetteBuilder:	knitr
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-02-12 04:39:47 UTC; HUAWEI
Author:	Wei Cui [aut, cre, cph]
Maintainer:	Wei Cui <m2c.w@outlook.com>
Repository:	CRAN
Date/Publication:	2025-02-12 04:50:02 UTC

Visualization Artist for Custom Plots

Description

The Artist class offers a suite of methods designed to create a variety of plots using ggplot2 for data exploration. Any methods prefixed with plot_ or test_ will log the command history along with their results, allowing you to review all outcomes later via the get_all_results() method. Notably, methods starting with plot_ will check if the result of the preceding command is of the htest class. If so, it will incorporate the previous command and its p-value as the title and subtitle, respectively. This class encompasses methods for crafting dumbbell plots, bubble plots, divergence bar charts, lollipop plots, contour plots, scatter plots with ellipses, donut plots, and pie charts. Each method is tailored to map data to specific visual aesthetics and to apply additional customizations as needed.

Value

The R6 class Artist.

Public fields

Methods

Public methods

• Artist$new()

• Artist$get_all_result()

• Artist$test_wilcox()

• Artist$test_t()

• Artist$plot_scatter()

• Artist$plot_box()

• Artist$dumbbbell()

• Artist$bubble()

• Artist$barchart_divergence()

• Artist$lollipop()

• Artist$contour()

• Artist$scatter_ellipses()

• Artist$donut()

• Artist$pie()

• Artist$clone()

Method new()

Initializes the Artist class with an optional dataset.

Usage

Artist$new(data = NULL)

Arguments

Returns

An instance of the Artist class.

Method get_all_result()

Get all history result

Usage

Artist$get_all_result()

Returns

a data.table object

Method test_wilcox()

Conduct wilcox.test

Usage

Artist$test_wilcox(formula, data = self$data, ...)

Arguments

Returns

A ggplot2 scatter plot.

Method test_t()

Conduct wilcox.test

Usage

Artist$test_t(formula, data = self$data, ...)

Arguments

Returns

A ggplot2 scatter plot.

Method plot_scatter()

Creates a scatter plot.

Usage

Artist$plot_scatter(
  data = self$data,
  fun = function(x) x,
  x,
  y,
  ...,
  add = private$is_htest()
)

Arguments

Returns

A ggplot2 scatter plot.

Method plot_box()

Creates a box plot.

Usage

Artist$plot_box(
  data = self$data,
  fun = function(x) x,
  x,
  ...,
  add = private$is_htest()
)

Arguments

Returns

A ggplot2 box plot.

Method dumbbbell()

Create a dumbbell plot

This method generates a dumbbell plot using the provided data, mapping the specified columns to the x-axis, y-axis, and color aesthetic.

Usage

Artist$dumbbbell(data = self$data, x, y, col, ...)

Arguments

Returns

A ggplot object representing the dumbbell plot.

Method bubble()

Create a bubble plot

This method generates a bubble plot where points are mapped to the x and y axes, with their size and color representing additional variables.

Usage

Artist$bubble(data = self$data, x, y, size, col, ...)

Arguments

Returns

A ggplot object representing the bubble plot.

Method barchart_divergence()

Create a divergence bar chart

This method generates a divergence bar chart where bars are colored based on their positive or negative value.

Usage

Artist$barchart_divergence(data = self$data, group, y, fill, ...)

Arguments

Returns

A ggplot object representing the divergence bar chart.

Method lollipop()

Create a lollipop plot

This method generates a lollipop plot, where points are connected to a baseline by vertical segments, with customizable colors and labels.

Usage

Artist$lollipop(data = self$data, x, y, ...)

Arguments

Returns

A ggplot object representing the lollipop plot.

Method contour()

Create a contour plot

This method generates a contour plot that includes filled and outlined density contours, with data points overlaid.

Usage

Artist$contour(data = self$data, x, y, ...)

Arguments

Returns

A ggplot object representing the contour plot.

Method scatter_ellipses()

Create a scatter plot with ellipses

This method generates a scatter plot where data points are colored by group, with ellipses representing the confidence intervals for each group.

Usage

Artist$scatter_ellipses(data = self$data, x, y, col, ...)

Arguments

Returns

A ggplot object representing the scatter plot with ellipses.

Method donut()

Create a donut plot

This method generates a donut plot, which is a variation of a pie chart with a hole in the center. The sections of the donut represent the proportion of categories in the data.

Usage

Artist$donut(data = self$data, x, y, fill, ...)

Arguments

Returns

A ggplot object representing the donut plot.

Method pie()

Create a pie chart

This method generates a pie chart where sections represent the proportion of categories in the data.

Usage

Artist$pie(data = self$data, y, fill, ...)

Arguments

Returns

A ggplot object representing the pie chart.

Method clone()

The objects of this class are cloneable with this method.

Usage

Artist$clone(deep = FALSE)

Arguments

Examples

library(data.table)
air <- subset(airquality, Month %in% c(5, 6))
setDT(air)
cying <- Artist$new(data = air)
cying$plot_scatter(x = Wind, y = Temp)
cying$test_wilcox(
  formula = Ozone ~ Month,
)
cying$plot_scatter(x = Wind, y = Temp)
cying$plot_scatter(f = \(x) x[, z := Wind * Temp], x = Wind, y = z)

Example DEGs data from Limma-Voom workflow for TCGA-CHOL project

Description

The data were obtained by the limma-voom workflow

Retrieve Available Tissue Classes for a Given Species

Description

This function extracts and returns a unique list of available tissue classes from the CellMarker2.0 database for a specified species.

Usage

available_tissue_class(spc)

Arguments

Value

A character vector of unique tissue classes available for the given species. If no tissue classes are found, an empty vector is returned.

Examples

available_tissue_class("Human")
available_tissue_class("Mouse")

Retrieve Available Tissue Types for a Given Species

Description

This function extracts and returns a unique list of available tissue types from the CellMarker2.0 database for a specified species.

Usage

available_tissue_type(spc)

Arguments

Value

A character vector of unique tissue types available for the given species. If no tissue types are found, an empty vector is returned.

Examples

available_tissue_type("Human")
available_tissue_type("Mouse")

Verify Markers for Specific Clusters Using matchCellMarker

Description

This function checks the markers for specified clusters returned by the matchCellMarker2 function. It allows users to filter by species, cluster, and to specify whether to consider cis or trans interactions.

Usage

check_marker(
  marker,
  n,
  spc,
  tissueClass = available_tissue_class(spc),
  tissueType = available_tissue_type(spc),
  cl = c(),
  topcellN = 2,
  cis = FALSE
)

Arguments

Value

A named list where each name corresponds to a cell type and each element is a vector of marker names.

Examples

# Example usage:
# Check the top 50 markers for clusters 1, 4, and 7 in the Human species.
## Not run: 
library(easybio)
data(pbmc.markers)
check_marker(pbmc.markers, n = 50, spc = "Human", cl = c(1, 4, 7))

## End(Not run)

Construct a DGEList Object

Description

This function creates a DGEList object from a count matrix, sample information, and feature information. It is designed to facilitate the analysis of differential gene expression using the edgeR package.

Usage

dgeList(count, sample.info, feature.info)

Arguments

Value

A DGEList object as defined by the edgeR package, which includes the count data, sample information, and feature information.

Filter Low-Expressed Genes and Normalize DGEList Data

Description

This function filters out low-expressed genes from a DGEList object and normalizes the count data. It also provides diagnostic plots for raw and filtered data.

Usage

dprocess_dgeList(x, group.column, min.count = 10)

Arguments

Value

The function returns a DGEList object with low-expressed genes filtered out and normalization factors calculated.

Insert Specific Values into a Character Vector at Defined Positions

Description

This function constructs a character vector of a specified length, inserting given values at positions determined by numeric indices. It is designed for single cell annotation tasks, where specific annotations need to be placed at certain positions in a vector.

Usage

finsert(
  x = expression(c(0, 1, 3) == "Neutrophil", c(2, 4, 8) == "Macrophage"),
  len = integer(),
  setname = TRUE,
  na = "Unknown"
)

Arguments

Value

A named character vector with the specified values inserted at given positions and padded with the na value if necessary.

Examples

# Example usage:
# Insert "Neutrophil" at positions 0, 1, 3 and "Macrophage" at positions 2, 4, 8
# in a vector of length 10, with "Unknown" as the default value.
library(easybio)
annotated_vector <- finsert(
  x = expression(
    c(0, 1, 3) == "Neutrophil",
    c(2, 4, 8) == "Macrophage"
  ),
  len = 10,
  na = "Unknown"
)
print(annotated_vector)

Retrieve Attributes from an R Object

Description

This function extracts a specified attribute from an R object.

Usage

get_attr(x, attr_name)

Arguments

Value

The value of the attribute with the given name.

Retrieve Markers for Specific Cells from cellMarker2

Description

This function extracts a list of markers for one or more cell types from the cellMarker2 dataset. It allows filtering by species, cell type, the number of markers to retrieve, and a minimum count threshold for marker occurrences.

Usage

get_marker(
  spc,
  cell = character(),
  tissueClass = available_tissue_class(spc),
  tissueType = available_tissue_type(spc),
  number = 5,
  min.count = 1
)

Arguments

Value

A named list where each name corresponds to a cell type and each element is a vector of marker names.

Examples

# Example usage:
# Retrieve the top 5 markers for 'Macrophage' and 'Monocyte' cell types in humans,
# with a minimum count of 1.
library(easybio)
markers <- get_marker(spc = "Human", cell = c("Macrophage", "Monocyte"))
print(markers)

Perform Summary Analysis by Group Using Regular Expressions

Description

This function applies a specified function to each group defined by a regular expression pattern applied to the names of a data object. It is useful for summarizing data when groups are defined by a pattern in the names rather than a specific column or index.

Usage

groupStat(f, x, xname = colnames(x), patterns)

Arguments

Value

A list containing the summary statistics for each group.

Examples

library(easybio)
groupStat(f = \(x) x + 1, x = mtcars, patterns = list("mp", "t"))

Perform Summary Analysis by Group Using an column Index

Description

This function applies a specified function to each group defined by an column index, and returns a summary of the results. It is useful for summarizing data by group when the groups are defined by an column index.

Usage

groupStatI(f, x, idx)

Arguments

Value

A list containing the summary statistics for each group.

Examples

library(easybio)
groupStatI(f = \(x) x + 1, x = mtcars, idx = list(c(1, 10), 2))

Fit a Linear Model for RNA-seq data using limma

Description

This function fits a linear model to processed DGEList data using the limma package. It defines contrasts between groups and performs differential expression analysis.

Usage

limmaFit(x, group.column)

Arguments

Value

An eBayes object containing the fitted linear model and results of the differential expression analysis.

Convert a List with Vector Values to a Long Data.table

Description

This function converts a named list with vector values in each element to a long data.table. The list is first flattened into a single vector, and then the data.table is created with two columns: one for the name of the original list element and another for the value.

Usage

list2dt(x)

Arguments

Value

A long data.table with two columns: 'name' and 'value'.

Examples

library(easybio)
list2dt(list(a = c(1, 1), b = c(2, 2)))

Convert a Named List into a Graph Based on Overlap

Description

This function creates a graph from a named list, where the edges are determined by the overlap between the elements of the list. Each node in the graph represents an element of the list, and the weight of the edge between two nodes is the number of overlapping elements between the two corresponding lists.

Usage

list2graph(nodes)

Arguments

Value

A data.table representing the graph, with columns for the node names (node_1 and node_2) and the weight of the edge (interWeight).

Match Markers with cellMarker2 Dataset

Description

This function matches markers from the FindAllMarkers output with the cellMarker2 dataset, filtering by species and selecting the top genes based on their average log2 fold change and adjusted p-values.

Usage

matchCellMarker2(
  marker,
  n,
  spc,
  tissueClass = available_tissue_class(spc),
  tissueType = available_tissue_type(spc)
)

Arguments

Value

A data frame containing matched markers from the cellMarker2 dataset, with additional columns indicating the number of matches and ordered symbols.

Examples

# Example usage:
# Match the top 50 differential genes from the pbmc.markers dataset with the Human
# species in the cellMarker2 dataset.
## Not run: 
library(easybio)
data(pbmc.markers)
matchCellMarker2(pbmc.markers, n = 50, spc = "Human")[]

## End(Not run)

Example marker data from Seurat::FindAllMarkers()

Description

The data were obtained by the seurat PBMC workflow. exact script for this data is available as system.file("example-single-cell.R", package="easybio")

Plot Enrichment for a Specific Pathway in fgsea

Description

This function creates a plot of enrichment scores for a specified pathway. It provides a visual representation of the enrichment score (ES) along with the ranks and ticks indicating the GSEA walk length.

Usage

plotEnrichment2(pathways, pwayname, stats, gseaParam = 1, ticksSize = 0.2)

Arguments

Value

A ggplot object representing the enrichment plot.

Visualization of GSEA Result from fgsea::fgsea()

Description

The plotGSEA function visualizes the results of a GSEA (Gene Set Enrichment Analysis) using data from the fgsea package. It generates a composite plot that includes an enrichment plot and a ranked metric plot.

Usage

plotGSEA(fgseaRes, pathways, pwayname, stats, save = FALSE)

Arguments

Value

ggplot2 object.

Plot Distribution of a Marker Across Tissues and Cell Types

Description

This function creates a dot plot displaying the distribution of a specified marker across different tissues and cell types, based on data from the CellMarker2.0 database.

Usage

plotMarkerDistribution(mkr = character())

Arguments

Value

A ggplot2 object representing the distribution of the marker.

Examples

## Not run: 
plotMarkerDistribution("CD14")

## End(Not run)

Visualization of ORA Test Results

Description

The plotORA function visualizes the results of an ORA (Over-Representation Analysis) test. It generates a plot with customizable aesthetics for x, y, point size, and fill, with an option to flip the axes.

Usage

plotORA(data, x, y, size, fill, flip = FALSE)

Arguments

Value

ggplot2 object.

Plot Possible Cell Distribution Based on matchCellMarker2() Results

Description

This function creates a dot plot to visualize the distribution of possible cell types based on the results from the matchCellMarker2() function, utilizing data from the CellMarker2.0 database.

Usage

plotPossibleCell(marker, min.uniqueN = 2)

Arguments

Value

A ggplot2 object representing the distribution of possible cell types.

Visualization of GSEA Rank Statistics

Description

The plotRank function visualizes the ranked statistics of a GSEA (Gene Set Enrichment Analysis) analysis. The function creates a plot where the x-axis represents the rank of each gene, and the y-axis shows the corresponding ranked list metric.

Usage

plotRank(stats)

Arguments

Value

ggplot2 object

Create Dot Plots for Markers from check_marker

Description

This function generates dot plots for the markers obtained from the check_marker function for specified cluster groups within a Seurat object. The plots are saved to a temporary directory.

Usage

plotSeuratDot(srt, cls, ...)

Arguments

Value

A list containing multiple DotPlot.

Plot Volcano Plot for Differentially Expressed Genes

Description

This function generates a volcano plot for differentially expressed genes (DEGs) using ggplot2. It allows for customization of the plot with different aesthetic parameters.

Usage

plotVolcano(data, data.text, x, y, color, label)

Arguments

Value

A ggplot object representing the volcano plot.

Download and Process GEO Data

Description

This function downloads gene expression data from the Gene Expression Omnibus (GEO) database. It retrieves either the expression matrix or the supplementary tabular data if the expression data is not available. The function also allows for the conversion of probe identifiers to gene symbols and can combine multiple probes into a single symbol.

Usage

prepare_geo(geo, dir = ".", combine = TRUE, method = "max")

Arguments

Value

A list containing:

Prepare TCGA Data for Analysis

Description

This function prepares TCGA data for downstream analyses such as differential expression analysis with limma or survival analysis. It extracts and processes the necessary information from the TCGA data object, separating tumor and non-tumor samples.

Usage

prepare_tcga(data)

Arguments

Value

A list.

Set a Directory for Saving Files

Description

This function sets a directory path for saving files, creating the directory if it does not already exist. The directory path is created with the given arguments, which are passed directly to file.path().

Usage

setSavedir(...)

Arguments

Value

The path to the newly created or existing directory.

Rename Column Names of a Data Frame or Matrix

Description

This function renames the column names of a data frame or matrix to the specified names.

Usage

setcolnames(object, nm)

Arguments

Value

A data frame or matrix with the new column names.

Rename Row Names of a Data Frame or Matrix

Description

This function renames the row names of a data frame or matrix to the specified names.

Usage

setrownames(object, nm)

Arguments

Value

A data frame or matrix with the new row names.

Split a Matrix into Smaller Sub-matrices by Column or Row

Description

This function splits a matrix into multiple smaller matrices by column or row. It is useful for processing large matrices in chunks, such as when performing analysis on a single computer with limited memory.

Usage

split_matrix(matrix, chunk_size, column = TRUE)

Arguments

Value

A list of smaller matrices, each with chunk_size columns or rows.

Examples

library(easybio)
split_matrix(mtcars, chunk_size = 2)
split_matrix(mtcars, chunk_size = 5, column = FALSE)

Custom ggplot2 Theme for Academic Publications

Description

theme_publication creates a custom ggplot2 theme designed for academic publications, ensuring clarity, readability, and a professional appearance. It is based on theme_classic() and includes additional refinements to axis lines, text, and other plot elements to meet the standards of high-quality academic figures.

Usage

theme_publication(base_size = 12, base_family = "sans")

Arguments

Value

A ggplot2 theme object that can be applied to ggplot2 plots.

ggplot2 theme.

Examples

library(ggplot2)
p <- ggplot(mtcars, aes(mpg, wt)) +
  geom_point() +
  theme_publication()
print(p)

Optimize Resolution and Gene Number Parameters for Cell Type Annotation

Description

This function tunes the resolution parameter in Seurat::FindClusters() and the number of top differential genes (N) to obtain different cell type annotation results. The function generates UMAP plots for each parameter combination, allowing for a comparison of how different settings affect the clustering and annotation.

Usage

tuneParameters(srt, resolution = numeric(), N = integer(), spc)

Arguments

Value

A list of ggplot2 objects, each representing a UMAP plot generated with a different combination of resolution and N parameters.

Map UniProt IDs to Other Identifiers

Description

This function maps UniProt IDs to other identifiers using UniProt's ID mapping service. It sends a request to the UniProt API to perform the mapping and retrieves the results in a tabular format.

Usage

uniprot_id_map(...)

Arguments

Value

A data.table containing the mapped identifiers.

Examples

## Not run: 
uniprot_id_map(
  ids = "P21802,P12345",
  from = "UniProtKB_AC-ID",
  to = "UniRef90"
)

## End(Not run)

Perform Operations in a Specified Directory and Return to the Original Directory

Description

This function allows you to perform operations in a specified directory and then return to the original directory. It is useful when you need to work with files or directories that are located in a specific location, but you want to return to the original working directory after the operation is complete.

Usage

workIn(dir, expr)

Arguments

Value

The result of evaluating the expression within the specified directory.