Package 'inferCSN'

Title: Inferring Cell-Specific Gene Regulatory Network
Description: Inferring cell-type specific gene regulatory network using sparse regression model from single-cell transcriptomic data.
Authors: Meng Xu [aut, cre] (ORCID: <https://orcid.org/0000-0002-8300-1054>)
Maintainer: Meng Xu <[email protected]>
License: MIT + file LICENSE
Version: 1.2.3
Built: 2026-05-24 15:12:22 UTC
Source: https://github.com/mengxu98/infercsn

Help Index

Inferring Cell-Specific Gene Regulatory Network

Description

Inferring cell-type specific gene regulatory network using sparse regression model from single-cell transcriptomic data.

Author(s)

Meng Xu (Maintainer), [email protected]

Source

https://mengxu98.github.io/inferCSN/

See Also

Useful links:

Calculate Accuracy

Description

Calculates accuracy metric

Usage

calculate_accuracy(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_accuracy(
  network_table,
  example_ground_truth
)

Calculate AUC Metrics

Description

Calculates AUROC and AUPRC metrics with optional visualization

Usage

calculate_auc(
  network_table,
  ground_truth,
  return_plot = FALSE,
  line_color = "#1563cc",
  line_width = 1,
  tag_levels = "A"
)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network
return_plot

Logical value indicating whether to generate plots
line_color

Color for plot lines
line_width

Width for plot lines
tag_levels

Tag levels for plot annotations

Value

A list containing metrics and optional plots

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_auc(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)

Calculate AUPRC Metric

Description

Calculates AUPRC metric with optional visualization

Usage

calculate_auprc(
  network_table,
  ground_truth,
  return_plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network
return_plot

Logical value indicating whether to generate plot
line_color

Color for plot lines
line_width

Width for plot lines

Value

A list containing metric and optional plot

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_auprc(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)

Calculate AUROC Metric

Description

Calculates AUROC metric with optional visualization

Usage

calculate_auroc(
  network_table,
  ground_truth,
  return_plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network
return_plot

Logical value indicating whether to generate plot
line_color

Color for plot lines
line_width

Width for plot lines

Value

A list containing metric and optional plot

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_auroc(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)

Calculate F1 Score

Description

Calculates F1 score

Usage

calculate_f1(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_f1(
  network_table,
  example_ground_truth
)

Rank TFs and genes in network

Description

Rank TFs and genes in network

Usage

calculate_gene_rank(
  network_table,
  regulators = NULL,
  targets = NULL,
  directed = FALSE
)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.
directed

Whether the network is directed.

Value

A table of gene rank.

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)
head(calculate_gene_rank(network_table))
head(calculate_gene_rank(network_table, regulators = "g1"))
head(calculate_gene_rank(network_table, targets = "g1"))

Calculate Jaccard Index

Description

Calculates Jaccard Index (JI) metric

Usage

calculate_ji(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_ji(
  network_table,
  example_ground_truth
)

Calculate Network Prediction Performance Metrics

Description

Calculates comprehensive performance metrics for evaluating predicted network structures, including classification performance, precision-recall metrics, and network topology metrics.

Usage

calculate_metrics(
  network_table,
  ground_truth,
  metric_type = c("all", "auc", "auroc", "auprc", "precision", "recall", "f1",
    "accuracy", "si", "ji"),
  return_plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

network_table

A data frame.
ground_truth

A data frame of ground truth network with the same format as network_table.
metric_type

The type of metric to return. Default is "all". This can take any of the following choices:

  • all Returns all available metrics with Performance Metrics plot.

  • auc Returns both AUROC and AUPRC with their plots.

  • auroc Area Under ROC Curve with plot.

  • auprc Area Under Precision-Recall Curve with plot.

  • precision Proportion of correct predictions among positive predictions.

  • recall Proportion of actual positives correctly identified.

  • f1 Harmonic mean of precision and recall.

  • accuracy Overall classification accuracy.

  • si Set Intersection, counting correctly predicted edges.

  • ji Jaccard Index, measuring overlap between predicted and true networks.

return_plot

Whether to generate visualization plots. Default is FALSE.
line_color

Color for plot lines. Default is ⁠#1563cc⁠.
line_width

Width for plot lines. Default is 1.

Value

A list containing:

  • metrics A data frame with requested metrics.

  • plot A plot object if return_plot = TRUE (optional).

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_metrics(
  network_table,
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  network_table,
  example_ground_truth,
  metric_type = "auroc"
)

Calculate Precision Metric

Description

Calculates precision metric

Usage

calculate_precision(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_precision(
  network_table,
  example_ground_truth
)

Calculate Recall Metric

Description

Calculates recall metric

Usage

calculate_recall(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_recall(
  network_table,
  example_ground_truth
)

Calculate Set Intersection

Description

Calculates Set Intersection (SI) metric

Usage

calculate_si(network_table, ground_truth)

Arguments

network_table

A data frame of predicted network structure
ground_truth

A data frame of ground truth network

Value

A list containing the metric

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
calculate_si(
  network_table,
  example_ground_truth
)

Example ground truth data

Description

The data used for calculate the evaluating indicator.

Example matrix data

Description

The matrix used for reconstruct gene regulatory network.

Example meta data

Description

The data contains cells and pseudotime information.

Filter and sort matrix

Description

Filter and sort matrix

Usage

filter_sort_matrix(network_matrix, regulators = NULL, targets = NULL)

Arguments

network_matrix

The matrix of network weight.
regulators

Regulators list.
targets

Targets list.

Value

Filtered and sorted matrix

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)
colnames(network_table) <- c("row", "col", "value")
network_matrix <- thisutils::table_to_matrix(network_table)
filter_sort_matrix(network_matrix)[1:6, 1:6]

filter_sort_matrix(
  network_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)

Sparse regression model

Description

Sparse regression model

Usage

fit_srm(
  x,
  y,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  regulators_num = ncol(x),
  n_folds = 5,
  verbose = TRUE,
  ...
)

Arguments

x

The matrix of regulators.
y

The vector of target.
cross_validation

Whether to use cross-validation. Default is FALSE.
seed

The random seed for cross-validation. Default is 1.
penalty

The type of regularization. This can take either one of the following choices: "L0", "L0L1", and "L0L2". For high-dimensional and sparse data, "L0L2" is more effective. Default is "L0".
regulators_num

The number of regulators for target.
n_folds

The number of folds for cross-validation. Default is 5.
verbose

Whether to print progress messages. Default is TRUE.
...

Parameters for other methods.

Value

A list of the sparse regression model. The list has the three components: model, metrics, and coefficients.

Examples

data(example_matrix)
fit_srm(
  x = example_matrix[, -1],
  y = example_matrix[, 1]
)

inferring cell-type specific gene regulatory network

Description

inferring cell-type specific gene regulatory network

Usage

inferCSN(
  object,
  penalty = c("L0", "L0L1", "L0L2"),
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

## S4 method for signature 'matrix'
inferCSN(
  object,
  penalty = c("L0", "L0L1", "L0L2"),
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

## S4 method for signature 'sparseMatrix'
inferCSN(
  object,
  penalty = c("L0", "L0L1", "L0L2"),
  cross_validation = FALSE,
  seed = 1,
  n_folds = 5,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  r_squared_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

object

The input data for inferring network.
penalty

The type of regularization. This can take either one of the following choices: "L0", "L0L1", and "L0L2". For high-dimensional and sparse data, "L0L2" is more effective. Default is "L0".
cross_validation

Whether to use cross-validation. Default is FALSE.
seed

The random seed for cross-validation. Default is 1.
n_folds

The number of folds for cross-validation. Default is 5.
subsampling_method

The method to use for subsampling. Options are "sample", "pseudobulk" or "meta_cells".
subsampling_ratio

The percent of all samples used for fit_srm. Default is 1.
r_squared_threshold

Threshold of R² coefficient. Default is 0.
regulators

The regulator genes for which to infer the regulatory network.
targets

The target genes for which to infer the regulatory network.
cores

The number of cores to use for parallelization with foreach::foreach. Default is 1.
verbose

Whether to print progress messages. Default is TRUE.
...

Parameters for other methods.

Value

A data table of regulator-target regulatory relationships, which has three columns: regulator, target, and weight.

Examples

data(example_matrix)
network_table <- inferCSN(
  example_matrix
)

head(network_table)

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g0")
)

Detection of metacells from single-cell gene expression matrix

Description

This function detects metacells from a single-cell gene expression matrix using dimensionality reduction and clustering techniques.

Usage

meta_cells(
  matrix,
  genes_use = NULL,
  genes_exclude = NULL,
  var_genes_num = min(1000, nrow(matrix)),
  gamma = 10,
  knn_k = 5,
  do_scale = TRUE,
  pc_num = 25,
  fast_pca = FALSE,
  do_approx = FALSE,
  approx_num = 20000,
  directed = FALSE,
  use_nn2 = TRUE,
  seed = 1,
  cluster_method = "walktrap",
  block_size = 10000,
  weights = NULL,
  do_median_norm = FALSE,
  ...
)

Arguments

matrix

A gene expression matrix where rows represent genes and columns represent cells.
genes_use

A character vector specifying genes to be used for PCA dimensionality reduction. Default is NULL.
genes_exclude

A character vector specifying genes to be excluded from PCA computation. Default is NULL.
var_genes_num

Number of most variable genes to select when genes_use is not provided. Default is min(1000, nrow(matrix)).
gamma

Default is 10. Coarse-graining parameter defining the target ratio of input cells to output metacells (e.g., gamma=10 yields approximately n/10 metacells for n input cells).
knn_k

Default is 5. Number of nearest neighbors for constructing the cell-cell similarity network.
do_scale

Whether to standardize (center and scale) gene expression values before PCA. Default is TRUE.
pc_num

Default is 25. Number of principal components to retain for downstream analysis.
fast_pca

Default is TRUE. Whether to use the faster irlba algorithm instead of standard PCA. Recommended for large datasets.
do_approx

Default is FALSE. Whether to use approximate nearest neighbor search for datasets with >50000 cells to improve computational efficiency.
approx_num

Default is 20000. Number of cells to randomly sample for approximate nearest neighbor computation when do_approx = TRUE.
directed

Default is FALSE. Whether to construct a directed or undirected nearest neighbor graph.
use_nn2

Default is TRUE. Whether to use the faster RANN::nn2 algorithm for nearest neighbor search (only applicable with Euclidean distance).
seed

Default is 1. Random seed for reproducibility when subsampling cells in approximate mode.
cluster_method

Default is walktrap. Algorithm for community detection in the cell similarity network. Options: walktrap (recommended) or louvain (gamma parameter ignored).
block_size

Default is 10000. Number of cells to process in each batch when mapping cells to metacells in approximate mode. Adjust based on available memory.
weights

Default is NULL. Numeric vector of cell-specific weights for weighted averaging when computing metacell expression profiles. Length must match number of cells.
do_median_norm

Default is FALSE. Whether to perform median-based normalization of the final metacell expression matrix.
...

Additional arguments passed to internal functions.

Value

A matrix where rows represent metacells and columns represent genes.

References

SuperCell, SCORPION

Examples

data(example_matrix)
meta_cells_matrix <- meta_cells(
  example_matrix
)
dim(meta_cells_matrix)
meta_cells_matrix[1:6, 1:6]

Format network table

Description

Format network table

Usage

network_format(
  network_table,
  regulators = NULL,
  targets = NULL,
  abs_weight = TRUE
)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.
abs_weight

Logical value, default is TRUE, whether to perform absolute value on weights, and when set abs_weight to TRUE, the output of weight table will create a new column named Interaction.

Value

Formated network table

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)

network_format(
  network_table,
  regulators = "g1"
)

network_format(
  network_table,
  regulators = "g1",
  abs_weight = FALSE
)

network_format(
  network_table,
  targets = "g3"
)

network_format(
  network_table,
  regulators = c("g1", "g3"),
  targets = c("g3", "g5")
)

Plot coefficients

Description

Plot coefficients

Usage

plot_coefficient(
  data,
  style = "continuous",
  positive_color = "#3d67a2",
  negative_color = "#c82926",
  neutral_color = "#cccccc",
  bar_width = 0.7,
  text_size = 3,
  show_values = TRUE
)

Arguments

data

Input data.
style

Plotting style: "binary", "gradient", or "continuous".
positive_color

Color for positive weights. Default is "#3d67a2".
negative_color

Color for negative weights. Default is "#c82926".
neutral_color

Color for weights near zero (used in "continuous" style). Default is "#cccccc".
bar_width

Width of the bars. Default is 0.7.
text_size

Size of the text for weight values. Default is 3.
show_values

Whether to show weight values on bars. Default is TRUE.

Value

A ggplot object

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix, targets = "g1")
plot_coefficient(network_table)
plot_coefficient(network_table, style = "binary")

Plot coefficients for multiple targets

Description

Plot coefficients for multiple targets

Usage

plot_coefficients(data, targets = NULL, nrow = NULL, ...)

Arguments

data

Input data.
targets

Targets to plot. Default is 'NULL'.
nrow

Number of rows for the plot. Default is 'NULL'.
...

Other arguments passed to [plot_coefficient].

Value

A list of ggplot objects

Examples

data(example_matrix)
network_table <- inferCSN(
  example_matrix,
  targets = c("g1", "g2", "g3")
)
plot_coefficients(network_table, show_values = FALSE)
plot_coefficients(network_table, targets = "g1")

Plot contrast networks

Description

Plot contrast networks

Usage

plot_contrast_networks(
  network_table,
  degree_value = 0,
  weight_value = 0,
  legend_position = "bottom"
)

Arguments

network_table

The weight data table of network.
degree_value

Degree value to filter nodes. Default is 0.
weight_value

Weight value to filter edges. Default is 0.
legend_position

The position of legend. Default is "bottom".

Value

A ggplot2 object.

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)
plot_contrast_networks(network_table[1:50, ])

Plot dynamic networks

Description

Plot dynamic networks

Usage

plot_dynamic_networks(
  network_table,
  celltypes_order,
  ntop = 10,
  title = NULL,
  theme_type = "theme_void",
  plot_type = "ggplot",
  layout = "fruchtermanreingold",
  nrow = 2,
  figure_save = FALSE,
  figure_name = NULL,
  figure_width = 6,
  figure_height = 6,
  seed = 1
)

Arguments

network_table

The weight data table of network.
celltypes_order

The order of cell types.
ntop

The number of top genes to plot. Default is 10.
title

The title of figure. Default is NULL.
theme_type

The theme of figure. Could be "theme_void", "theme_blank", or "theme_facet". Default is "theme_void".
plot_type

The type of figure. Could be "ggplot", "animate", or "ggplotly". Default is "ggplot".
layout

The layout of figure. Could be "fruchtermanreingold" or "kamadakawai". Default is "fruchtermanreingold".
nrow

The number of rows of figure. Default is 2.
figure_save

Whether to save the figure file. Default is FALSE.
figure_name

The name of figure file. Default is NULL.
figure_width

The width of figure. Default is 6.
figure_height

The height of figure. Default is 6.
seed

The seed random use to plot network. Default is 1.

Value

A dynamic figure object.

Examples

data(example_matrix)
network <- inferCSN(example_matrix)[1:100, ]
network$celltype <- c(
  rep("cluster1", 20),
  rep("cluster2", 20),
  rep("cluster3", 20),
  rep("cluster5", 20),
  rep("cluster6", 20)
)

celltypes_order <- c(
  "cluster5", "cluster3",
  "cluster2", "cluster1",
  "cluster6"
)

plot_dynamic_networks(
  network,
  celltypes_order = celltypes_order
)

plot_dynamic_networks(
  network,
  celltypes_order = celltypes_order[1:3]
)

plot_dynamic_networks(
  network,
  celltypes_order = celltypes_order,
  plot_type = "ggplotly"
)

Network Edge Comparison Visualization

Description

Generates visualizations comparing edges of two networks.

Usage

plot_edges_comparison(
  network_table,
  ground_truth,
  color_pattern = list(predicted = "gray", ground_truth = "#bb141a", overlap = "#1966ad",
    total = "#6C757D")
)

Arguments

network_table

A data frame of predicted network structure.
ground_truth

A data frame of ground truth network.
color_pattern

A list of colors for different categories.

Value

A patchwork plot object containing network edge comparison and distribution plots

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)
plot_edges_comparison(
  network_table,
  example_ground_truth
)

Plot embedding

Description

Plot embedding

Usage

plot_embedding(
  matrix,
  labels = NULL,
  method = "pca",
  colors = RColorBrewer::brewer.pal(length(unique(labels)), "Set1"),
  seed = 1,
  point_size = 1,
  cores = 1
)

Arguments

matrix

Input matrix.
labels

Input labels.
method

Method to use for dimensionality reduction.
colors

Colors to use for the plot.
seed

Seed for the random number generator.
point_size

Size of the points.
cores

Set the number of threads when setting for uwot::umap and Rtsne::Rtsne.

Value

An embedding plot

Examples

data(example_matrix)
samples_use <- 1:200
plot_data <- example_matrix[samples_use, ]
labels <- sample(
  c("A", "B", "C", "D", "E"),
  nrow(plot_data),
  replace = TRUE
)

plot_embedding(
  plot_data,
  labels,
  method = "pca",
  point_size = 2
)

plot_embedding(
  plot_data,
  labels,
  method = "tsne",
  point_size = 2
)

Plot histogram

Description

Plot histogram

Usage

plot_histogram(
  data,
  binwidth = 0.01,
  show_border = FALSE,
  border_color = "black",
  alpha = 1,
  theme = "viridis",
  theme_begin = 0,
  theme_end = 0.5,
  theme_direction = -1,
  legend_position = "right"
)

Arguments

data

A numeric vector.
binwidth

Width of the bins.
show_border

Logical value, whether to show border of the bins.
border_color

Color of the border.
alpha

Alpha value of the bins.
theme

Theme of the bins.
theme_begin

Begin value of the theme.
theme_end

End value of the theme.
theme_direction

Direction of the theme.
legend_position

The position of legend.

Value

A ggplot object

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)
plot_histogram(network_table[, 3])

Plot network heatmap

Description

Plot network heatmap

Usage

plot_network_heatmap(
  network_table,
  regulators = NULL,
  targets = NULL,
  switch_matrix = TRUE,
  show_names = FALSE,
  heatmap_size_lock = TRUE,
  heatmap_size = 5,
  heatmap_height = NULL,
  heatmap_width = NULL,
  heatmap_title = NULL,
  heatmap_color = c("#1966ad", "white", "#bb141a"),
  border_color = "gray",
  rect_color = NA,
  anno_width = 1,
  anno_height = 1,
  row_anno_type = c("boxplot", "barplot", "histogram", "density", "lines", "points",
    "horizon"),
  column_anno_type = c("boxplot", "barplot", "histogram", "density", "lines", "points"),
  legend_name = "Weight",
  row_title = "Regulators"
)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.
switch_matrix

Whether to weight data table to matrix. Default is TRUE.
show_names

Whether to show names of row and column. Default is FALSE.
heatmap_size_lock

Lock the size of heatmap.
heatmap_size

The size of heatmap. Default is 5.
heatmap_height

The height of heatmap.
heatmap_width

The width of heatmap.
heatmap_title

The title of heatmap.
heatmap_color

Colors of heatmap.
border_color

Default is "gray". Color of heatmap border.
rect_color

Default is NA. Color of heatmap rect.
anno_width

Width of annotation.
anno_height

Height of annotation.
row_anno_type

Default is "boxplot", could add a annotation plot to row. choose one of "boxplot", "barplot", "histogram", "density", "lines", "points", and "horizon".
column_anno_type

Default is "boxplot", could add a annotation plot to column. choose one of "boxplot", "barplot", "histogram", "density", "lines", and "points".
legend_name

The name of legend.
row_title

The title of row.

Value

A heatmap

Examples

data(example_matrix)
data("example_ground_truth")
network_table <- inferCSN(example_matrix)

p1 <- plot_network_heatmap(
  example_ground_truth[, 1:3],
  heatmap_title = "Ground truth",
  legend_name = "Ground truth"
)
p2 <- plot_network_heatmap(
  network_table,
  heatmap_title = "inferCSN",
  legend_name = "inferCSN"
)
ComplexHeatmap::draw(p1 + p2)

plot_network_heatmap(
  network_table,
  show_names = TRUE,
  rect_color = "gray90",
  row_anno_type = "density",
  column_anno_type = "barplot"
)

plot_network_heatmap(
  network_table,
  regulators = c("g1", "g3", "g5"),
  targets = c("g3", "g6", "g9"),
  show_names = TRUE
)

Plot expression data in a scatter plot

Description

Plot expression data in a scatter plot

Usage

plot_scatter(
  data,
  smoothing_method = "lm",
  group_colors = RColorBrewer::brewer.pal(9, "Set1"),
  title_color = "black",
  title = NULL,
  col_title = NULL,
  row_title = NULL,
  legend_title = NULL,
  legend_position = "bottom",
  margins = "both",
  marginal_type = NULL,
  margins_size = 10,
  compute_correlation = TRUE,
  compute_correlation_method = "pearson",
  keep_aspect_ratio = TRUE,
  facet = FALSE,
  se = FALSE,
  pointdensity = TRUE
)

Arguments

data

Input data.
smoothing_method

Method for smoothing curve, lm or loess.
group_colors

Colors for different groups.
title_color

Color for the title.
title

Main title for the plot.
col_title

Title for the x-axis.
row_title

Title for the y-axis.
legend_title

Title for the legend.
legend_position

The position of legend.
margins

The position of marginal figure ("both", "x", "y").
marginal_type

The type of marginal figure (density, histogram, boxplot, violin, densigram).
margins_size

The size of marginal figure, note the bigger size the smaller figure.
compute_correlation

Whether to compute and print correlation on the figure.
compute_correlation_method

Method to compute correlation (pearson or spearman).
keep_aspect_ratio

Logical value, whether to set aspect ratio to 1:1.
facet

Faceting variable. If setting TRUE, all settings about margins will be inalidation.
se

Display confidence interval around smooth.
pointdensity

Plot point density when only provide 1 cluster.

Value

ggplot object

Examples

data(example_matrix)
test_data <- data.frame(
  example_matrix[1:200, c(1, 7)],
  c = c(
    rep("c1", 40),
    rep("c2", 40),
    rep("c3", 40),
    rep("c4", 40),
    rep("c5", 40)
  )
)

p1 <- plot_scatter(
  test_data
)
p2 <- plot_scatter(
  test_data,
  marginal_type = "boxplot"
)
p1 + p2

p3 <- plot_scatter(
  test_data,
  facet = TRUE
)
p3

p4 <- plot_scatter(
  test_data[, 1:2],
  marginal_type = "histogram"
)
p4

Plot dynamic networks

Description

Plot dynamic networks

Usage

plot_static_networks(
  network_table,
  regulators = NULL,
  targets = NULL,
  legend_position = "right"
)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.
legend_position

The position of legend.

Value

A ggplot2 object

Examples

data(example_matrix)
network_table <- inferCSN(example_matrix)
plot_static_networks(
  network_table,
  regulators = "g1"
)
plot_static_networks(
  network_table,
  targets = "g1"
)
plot_static_networks(
  network_table,
  regulators = "g1",
  targets = "g2"
)

Construct network for single target gene

Description

Construct network for single target gene

Usage

single_network(
  matrix,
  regulators,
  target,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  r_squared_threshold = 0,
  n_folds = 5,
  verbose = TRUE,
  ...
)

Arguments

matrix

An expression matrix.
regulators

The regulator genes for which to infer the regulatory network.
target

The target gene.
cross_validation

Whether to use cross-validation. Default is FALSE.
seed

The random seed for cross-validation. Default is 1.
penalty

The type of regularization. This can take either one of the following choices: "L0", "L0L1", and "L0L2". For high-dimensional and sparse data, "L0L2" is more effective. Default is "L0".
r_squared_threshold

Threshold of R² coefficient. Default is 0.
n_folds

The number of folds for cross-validation. Default is 5.
verbose

Whether to print progress messages. Default is TRUE.
...

Parameters for other methods.

Value

A data frame of the single target gene network. The data frame has three columns: regulator, target, and weight.

Examples

data(example_matrix)
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1"
  )
)
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1",
    cross_validation = TRUE
  )
)

single_network(
  example_matrix,
  regulators = c("g1", "g2", "g3"),
  target = "g1"
)
single_network(
  example_matrix,
  regulators = c("g1", "g2"),
  target = "g1"
)

Subsampling function

Description

This function subsamples a matrix using either random sampling or meta cells method.

Usage

subsampling(
  matrix,
  subsampling_method = c("sample", "meta_cells", "pseudobulk"),
  subsampling_ratio = 1,
  seed = 1,
  verbose = TRUE,
  ...
)

Arguments

matrix

The input matrix to be subsampled.
subsampling_method

The method to use for subsampling. Options are "sample", "pseudobulk" or "meta_cells".
subsampling_ratio

The percent of all samples used for fit_srm. Default is 1.
seed

The random seed for cross-validation. Default is 1.
verbose

Whether to print progress messages. Default is TRUE.
...

Parameters for other methods.

Value

The subsampled matrix.

Examples

data(example_matrix)
data("example_ground_truth")
subsample_matrix <- subsampling(
  example_matrix,
  subsampling_ratio = 0.5
)
subsample_matrix_2 <- subsampling(
  example_matrix,
  subsampling_method = "meta_cells",
  subsampling_ratio = 0.5,
  fast_pca = FALSE
)
subsample_matrix_3 <- subsampling(
  example_matrix,
  subsampling_method = "pseudobulk",
  subsampling_ratio = 0.5
)

calculate_metrics(
  inferCSN(example_matrix),
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix),
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix_2),
  example_ground_truth,
  return_plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix_3),
  example_ground_truth,
  return_plot = TRUE
)