Title:	Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit
Version:	1.2.1
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 (≥ 1.15.0), checkmate, ggplot2, httr2, R6, xml2
Depends:	R (≥ 4.1.0)
LazyData:	true
Suggests:	litedown, knitr, patchwork, ggrepel, Seurat, limma, GEOquery, fgsea, edgeR, testthat (≥ 3.0.0),
biocViews:	limma, GEOquery, edgeR, fgsea
Language:	en-US
VignetteBuilder:	litedown
Config/testthat/edition:	3
NeedsCompilation:	no
Packaged:	2025-08-30 06:08:03 UTC; m2cw
Author:	Wei Cui [aut, cre, cph]
Maintainer:	Wei Cui <m2c.w@outlook.com>
Repository:	CRAN
Date/Publication:	2025-08-30 06:40:03 UTC

easybio: Comprehensive Single-Cell Annotation and Transcriptomic Analysis Toolkit

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.

Author(s)

Maintainer: Wei Cui m2c.w@outlook.com (ORCID) [copyright holder]

See Also

Useful links:

• https://github.com/person-c/easybio

• Report bugs at https://github.com/person-c/easybio/issues

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

Extract Unique Elements from a Column with Optional Filtering

Description

Retrieves the unique, non-missing values from a specified column of a data frame. An optional expression can be provided to filter the rows of the data frame before extracting the values.

Usage

available_ele(data, col_name, subset)

Arguments

Value

A vector containing the unique, non-NA values from the specified column after the optional filtering has been applied.

Examples

# Example 1: Get all unique species from the iris dataset
available_ele(iris, "Species")

# Example 2: Get unique species for flowers with Sepal.Length > 7
available_ele(iris, "Species", subset = Sepal.Length > 7)

# Example 3: Get unique carb values for cars with 6 cylinders
available_ele(mtcars, "carb", subset = cyl == 6)

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.

See Also

available_tissue_type, get_marker

Examples

# Get all tissue classes for Human
available_tissue_class("Human")

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.

See Also

available_tissue_class, get_marker

Examples

# Get all tissue types for Human
available_tissue_type("Human")

Verify and Explore Cell Type Annotations

Description

A post-analysis function that helps to verify and explore the automated cell type annotations generated by matchCellMarker2. It retrieves marker genes for the top-matching cell types of specified clusters, allowing for deeper inspection of the annotation results.

Usage

check_marker(marker, cl = c(), topcellN = 2, cis = FALSE)

Arguments

Details

The function provides two distinct modes for marker retrieval, controlled by the cis parameter. This allows the user to answer two different, important questions:

• cis = FALSE (Default): "Is the annotation correct?" This mode answers the question by fetching the canonical markers for the annotated cell type from the reference database (via get_marker). It automatically uses the same filtering criteria (species, tissue, etc.) that were used in the original matchCellMarker2 call, ensuring consistency.

• cis = TRUE: "Why was this annotation made?" This mode answers the question by extracting the local markers from the user's own data (i.e., the differentially expressed genes from the marker input) that led to the annotation. This helps understand the evidence behind the match.

Value

A named list. Each name in the list is a cell type, and each element is a character vector of its corresponding marker genes.

See Also

matchCellMarker2 to generate the input for this function. get_marker which is used internally when cis = FALSE. plotSeuratDot to visualize the results.

Examples

## Not run: 
library(easybio)
data(pbmc.markers)

# Step 1: Generate cell type annotations
matched_cells <- matchCellMarker2(pbmc.markers, n = 50, spc = "Human")

# Step 2: Verify the annotation for cluster 0.
# Let's check the top annotation (topcellN = 1).

# Question 1: "Is cluster 0 really a CD4-positive T cell?
# Let's see the canonical markers for it."
# Note: We don't need to pass 'spc' here; it's retrieved from matched_cells.
reference_markers <- check_marker(matched_cells, cl = 0, topcellN = 1)
print(reference_markers)
# Now you would typically use these markers in Seurat::DotPlot() or Seurat::FeaturePlot()

# Question 2: "Which of my genes made the algorithm think cluster 0
# is a CD4-positive T cell?"
local_markers <- check_marker(matched_cells, cl = 0, topcellN = 1, cis = TRUE)
print(local_markers)

## 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.

Create a Vector from an Index-to-Label Map

Description

Constructs a character vector by mapping labels to specified 0-based numeric indices. This is a utility function often used in single-cell analysis to assign cell type annotations to cluster IDs.

Usage

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

Arguments

Value

A character vector with the specified labels at the given positions. The vector is named with 0-based indices if setname is TRUE.

Examples

# --- Example 1: Using the default formula list format ---
# This is the recommended and default usage.
mapping_formula <- list(
  c(0, 1, 3) ~ "Neutrophil",
  c(2, 4, 8) ~ "Macrophage"
)
finsert(mapping_formula)

# --- Example 2: Using the expression format for backward compatibility ---
mapping_expr <- expression(
  c(0, 1, 3) == "Neutrophil",
  c(2, 4, 8) == "Macrophage"
)
finsert(mapping_expr, len = 10, na = "Unassigned")

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)
# Example with a typo in cell name
markers_typo <- get_marker(spc = "Human", cell = c("Macrophae", "Monocyte"))

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, col_names = c("name", "value"))

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).

Annotate Clusters by Matching Markers with the CellMarker2.0 Database

Description

This function takes cluster-specific markers, typically from Seurat::FindAllMarkers, and annotates each cluster with potential cell types by matching these markers against a reference database. It first filters and selects the top n marker genes for each cluster based on specified thresholds and then compares them to the reference database to find the most likely cell type annotations.

Usage

matchCellMarker2(
  marker,
  n,
  avg_log2FC_threshold = 0,
  p_val_adj_threshold = 0.05,
  spc,
  tissueClass = available_tissue_class(spc),
  tissueType = available_tissue_type(spc),
  ref = NULL
)

Arguments

Value

A data.table where each row represents a potential cell type match for a cluster. The table is keyed by cluster and includes columns for cluster, cell_name, uniqueN (number of unique matching markers), N (total matches), ordered_symbol (matching genes, ordered by frequency), and orderN (their frequencies).

The returned object also contains important attributes for downstream analysis:

See Also

check_marker, plotPossibleCell, available_tissue_class, available_tissue_type

Examples

## Not run: 
library(easybio)
data(pbmc.markers)

# Basic usage: Annotate clusters using the top 50 markers per cluster
matched_cells <- matchCellMarker2(pbmc.markers, n = 50, spc = "Human")
print(matched_cells)

# To see the top annotation for each cluster
top_matches <- matched_cells[, .SD[1], by = cluster]
print(top_matches)

# Advanced usage: Stricter filtering and focus on specific tissues
matched_cells_strict <- matchCellMarker2(
  pbmc.markers,
  n = 30,
  spc = "Human",
  avg_log2FC_threshold = 0.5,
  p_val_adj_threshold = 0.01,
  tissueType = c("Blood", "Bone marrow")
)
print(matched_cells_strict)

# --- Example with a custom reference ---
# Create a custom reference as a named list.
custom_ref_list <- list(
  "T-cell" = c("CD3D", "CD3E"),
  "B-cell" = c("CD79A", "MS4A1"),
  "Myeloid" = "LYZ"
)

# Convert the list to a long data.frame compatible with the 'ref' parameter.
custom_ref_df <- list2dt(custom_ref_list, col_names = c("cell_name", "marker"))

# Run annotation using the custom reference.
# When 'ref' is provided, the internal cellMarker2 database and its filters
# ('spc', 'tissueClass', 'tissueType') are ignored for matching.
matched_custom <- matchCellMarker2(
  pbmc.markers,
  n = 50,
  ref = custom_ref_df
)
print(matched_custom)

## 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 a Dot Plot to Visualize Marker Gene Expression

Description

This function generates a Seurat::DotPlot to visualize the expression of specified marker genes across different cell clusters or groups. It is designed to work with a list of features, such as the output from the check_marker function.

Usage

plotSeuratDot(features, srt, split = FALSE, ...)

Arguments

Value

A ggplot2 object representing the dot plot, which can be further customized.

See Also

check_marker to generate the features list.

Examples

## Not run: 
library(easybio)
library(Seurat)
data(pbmc.markers)

# In a real scenario, 'srt' would be your fully processed Seurat object.
# For this example, we create a minimal Seurat object.
# The expression matrix should contain the marker genes for the plot to be meaningful.
marker_genes <- unique(pbmc.markers$gene)
counts <- matrix(
  abs(rnorm(length(marker_genes) * 50, mean = 1, sd = 2)),
  nrow = length(marker_genes),
  ncol = 50
)
rownames(counts) <- marker_genes
colnames(counts) <- paste0("cell_", 1:50)

srt <- CreateSeuratObject(counts = counts)
srt$seurat_clusters <- sample(0:3, 50, replace = TRUE)
Idents(srt) <- "seurat_clusters"

# Step 1: Generate cell type annotations
matched_cells <- matchCellMarker2(pbmc.markers, n = 50, spc = "Human")

# Step 2: Get canonical markers for cluster 0's top annotation
reference_markers <- check_marker(matched_cells, cl = 0, topcellN = 1)

# Step 3: Plot the expression of these markers
if (!is.null(reference_markers) && length(reference_markers) > 0) {
  plotSeuratDot(features = reference_markers, srt = srt)
}

## End(Not run)

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)

Suggest Best Matches for a String from a Vector of Choices

Description

This function provides intelligent suggestions for a user's input string by finding the best matches from a given vector of choices. It follows a multi-layered approach:

1. Performs normalization (case-insensitivity, trimming whitespace).

2. Checks for an exact match first for maximum performance and accuracy.

3. If no exact match, it uses a combination of fuzzy string matching (Levenshtein distance via adist) to catch typos and partial/substring matching (grep) to handle incomplete input.

4. Ranks the potential matches and returns the best suggestion(s).

Usage

suggest_best_match(
  x,
  choices,
  n = 1,
  threshold = 2,
  ignore.case = TRUE,
  return_distance = FALSE
)

Arguments

Value

By default (return_distance = FALSE), returns a character vector of the best n suggestions. If no suitable match is found, returns NA. If return_distance = TRUE, returns a data.frame with columns suggestion and distance, or NULL if no match is found.

Examples

# --- Setup ---
cell_types <- c(
  "B cell", "T cell", "Macrophage", "Monocyte", "Neutrophil",
  "Natural Killer T-cell", "Dendritic cell"
)

# --- Usage ---
# 1. Exact match (after normalization)
suggest_best_match("t cell", cell_types)
#> [1] "T cell"

# 2. Typo correction (fuzzy match)
suggest_best_match("Macrophaeg", cell_types)
#> [1] "Macrophage"

# 3. Partial input (substring match)
suggest_best_match("Mono", cell_types)
#> [1] "Monocyte"

# 4. Requesting multiple suggestions
suggest_best_match("t", cell_types, n = 3)
#> [1] "T cell" "Neutrophil" "Natural Killer T-cell"

# 5. No good match found
suggest_best_match("Erythrocyte", cell_types)
#> [1] NA

# 6. Returning suggestions with their distance score
suggest_best_match("t ce", cell_types, n = 3, return_distance = TRUE)
#>              suggestion distance
#> 1                T cell        1
#> 2        Dendritic cell        2
#> 3 Natural Killer T-cell        2

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.