Package 'inferCSN'

Title: Inferring Cell-Specific Gene Regulatory Network
Description: An R package for inferring cell-type specific gene regulatory network from single-cell RNA data.
Authors: Meng Xu [aut, cre]
Maintainer: Meng Xu <[email protected]>
License: MIT + file LICENSE
Version: 1.0.8
Built: 2024-11-18 06:26:46 UTC
Source: https://github.com/mengxu98/infercsn

Help Index

inferCSN: inferring Cell-Specific gene regulatory Network

Description

An R package for inferring Cell-Specific gene regulatory Network from single-cell RNA data

Author(s)

Meng xu (Maintainer), [email protected]

Source

https://github.com/mengxu98/inferCSN

See Also

Useful links:

Value selection operator

Description

This operator returns the left side if it's not NULL, otherwise it returns the right side.

Usage

a %s% b

Arguments

a

The left side value to check
b

The right side value to use if a is NULL

Examples

NULL %s% 10
5 %s% 10

Convert sparse matrix into dense matrix

Description

Convert sparse matrix into dense matrix

Usage

as_matrix(x, parallel = FALSE, sparse = FALSE)

Arguments

x

A matrix.
parallel

Logical value, default is FALSE. Setting to parallelize the computation with setThreadOptions.
sparse

Logical value, default is FALSE, whether to output a sparse matrix.

Examples

sparse_matrix <- simulate_sparse_matrix(
  2000,
  2000,
  density = 0.01
)

system.time(as.matrix(sparse_matrix))
system.time(as_matrix(sparse_matrix))
system.time(as_matrix(sparse_matrix, parallel = TRUE))

identical(
  as.matrix(sparse_matrix),
  as_matrix(sparse_matrix)
)

identical(
  as.matrix(sparse_matrix),
  as_matrix(sparse_matrix, parallel = TRUE)
)

identical(
  sparse_matrix,
  as_matrix(as.matrix(sparse_matrix), sparse = TRUE)
)

## Not run: 
network_table_0 <- inferCSN(example_matrix)

network_table_1 <- inferCSN(
  as_matrix(example_matrix, sparse = TRUE)
)
network_table_2 <- inferCSN(
  as(example_matrix, "sparseMatrix")
)

plot_scatter(
  data.frame(
    network_table_0$weight,
    network_table_1$weight
  ),
  legend_position = "none"
)

plot_scatter(
  data.frame(
    network_table_1$weight,
    network_table_2$weight
  ),
  legend_position = "none"
)

## End(Not run)

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 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,
  type = "all",
  plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

network_table

A data frame of predicted network structure containing:

  • regulator - Source nodes of the network edges

  • target - Target nodes of the network edges

  • weight - Edge weights representing prediction confidence

ground_truth

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

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

  • all - Returns all available metrics

  • auroc - Area Under ROC Curve, evaluating overall classification ability

  • auprc - Area Under Precision-Recall Curve, focusing on positive class prediction

  • precision - Proportion of correct predictions among positive predictions

  • recall - Proportion of actual positives correctly identified

  • f1 - Harmonic mean of precision and recall

  • si - Set Intersection, counting correctly predicted edges

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

plot

Logical value, default is FALSE, whether to generate visualization plots. If TRUE, generates:

  • ROC curve plot for AUROC evaluation

  • Precision-Recall curve plot for AUPRC evaluation

  • Confusion matrix heatmap for classification results

  • Network comparison plot showing edge overlap

  • Metrics summary bar plot

line_color

The color for plot lines, default is ⁠#1563cc⁠.
line_width

The width for plot lines, default is 1.

Value

  • If type="all": A data frame with all metrics

  • If specific type: A single numeric value with the requested metric

  • If plot=TRUE: Displays relevant visualization plots

Examples

data("example_matrix")
data("example_ground_truth")
inferCSN(example_matrix) |>
  calculate_metrics(example_ground_truth, plot = TRUE)

Check sparsity of matrix

Description

Check sparsity of matrix

Usage

check_sparsity(x)

Arguments

x

A matrix.

Value

Sparsity of matrix

Extracts a specific solution in the regularization path

Description

Extracts a specific solution in the regularization path

Usage

## S3 method for class 'srm'
coef(object, lambda = NULL, gamma = NULL, regulators_num = NULL, ...)

## S3 method for class 'srm_cv'
coef(object, lambda = NULL, gamma = NULL, ...)

Arguments

object

The output of fit_sparse_regression.
lambda

The value of lambda at which to extract the solution.
gamma

The value of gamma at which to extract the solution.
regulators_num

The number of non-zore coefficients, this value will affect the final performance. The maximum support size at which to terminate the regularization path.
...

Other parameters

Value

Return the specific solution

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)
network_matrix <- 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")
)

Fit a sparse regression model

Description

Computes the regularization path for the specified loss function and penalty function.

Usage

fit_sparse_regression(
  x,
  y,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = ncol(x),
  cross_validation = FALSE,
  n_folds = 10,
  seed = 1,
  loss = "SquaredError",
  nLambda = 100,
  nGamma = 5,
  gammaMax = 10,
  gammaMin = 1e-04,
  partialSort = TRUE,
  maxIters = 200,
  rtol = 1e-06,
  atol = 1e-09,
  activeSet = TRUE,
  activeSetNum = 3,
  maxSwaps = 100,
  scaleDownFactor = 0.8,
  screenSize = 1000,
  autoLambda = NULL,
  lambdaGrid = list(),
  excludeFirstK = 0,
  intercept = TRUE,
  lows = -Inf,
  highs = Inf,
  verbose = TRUE,
  ...
)

Arguments

x

The matrix of regulators.
y

The vector of target.
penalty

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

The type of algorithm used to minimize the objective function, default is CD. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
regulators_num

The number of non-zore coefficients, this value will affect the final performance. The maximum support size at which to terminate the regularization path.
cross_validation

Logical value, default is FALSE, whether to use cross-validation.
n_folds

The number of folds for cross-validation, default is 10.
seed

The random seed for cross-validation, default is 1.
loss

The loss function.
nLambda

The number of Lambda values to select.
nGamma

The number of Gamma values to select.
gammaMax

The maximum value of Gamma when using the L0L2 penalty. For the L0L1 penalty this is automatically selected.
gammaMin

The minimum value of Gamma when using the L0L2 penalty. For the L0L1 penalty, the minimum value of gamma in the grid is set to gammaMin * gammaMax. Note that this should be a strictly positive quantity.
partialSort

If TRUE, partial sorting will be used for sorting the coordinates to do greedy cycling. Otherwise, full sorting is used.
maxIters

The maximum number of iterations (full cycles) for CD per grid point.
rtol

The relative tolerance which decides when to terminate optimization, based on the relative change in the objective between iterations.
atol

The absolute tolerance which decides when to terminate optimization, based on the absolute L2 norm of the residuals.
activeSet

If TRUE, performs active set updates.
activeSetNum

The number of consecutive times a support should appear before declaring support stabilization.
maxSwaps

The maximum number of swaps used by CDPSI for each grid point.
scaleDownFactor

This parameter decides how close the selected Lambda values are.
screenSize

The number of coordinates to cycle over when performing initial correlation screening.
autoLambda

Ignored parameter. Kept for backwards compatibility.
lambdaGrid

A grid of Lambda values to use in computing the regularization path.
excludeFirstK

This parameter takes non-negative integers.
intercept

If FALSE, no intercept term is included in the model.
lows

Lower bounds for coefficients.
highs

Upper bounds for coefficients.
verbose

Logical value, default is TRUE, whether to print progress messages.
...

Parameters for other methods.

Value

An S3 object describing the regularization path

References

Hazimeh, Hussein et al. “L0Learn: A Scalable Package for Sparse Learning using L0 Regularization.” J. Mach. Learn. Res. 24 (2022): 205:1-205:8.

Hazimeh, Hussein and Rahul Mazumder. “Fast Best Subset Selection: Coordinate Descent and Local Combinatorial Optimization Algorithms.” Oper. Res. 68 (2018): 1517-1537.

https://github.com/hazimehh/L0Learn/blob/master/R/fit.R

Examples

data("example_matrix")
fit <- fit_sparse_regression(
  example_matrix[, -1],
  example_matrix[, 1]
)
head(coef(fit))

inferring Cell-Specific gene regulatory Network

Description

inferring Cell-Specific gene regulatory Network

Usage

inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling_method = "sample",
  subsampling_ratio = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

## S4 method for signature 'matrix'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling_method = "sample",
  subsampling_ratio = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

## S4 method for signature 'sparseMatrix'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling_method = "sample",
  subsampling_ratio = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

## S4 method for signature 'data.frame'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  subsampling_method = "sample",
  subsampling_ratio = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

Arguments

object

The input data for inferCSN.
penalty

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

The type of algorithm used to minimize the objective function, default is CD. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
cross_validation

Logical value, default is FALSE, whether to use cross-validation.
seed

The random seed for cross-validation, default is 1.
n_folds

The number of folds for cross-validation, default is 10.
subsampling_method

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

The percent of all samples used for sparse_regression, default is 1.
r_threshold

Threshold of R2R^2 or correlation 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. Recommend setting this to a small fraction of min(n,p) (e.g. 0.05 * min(n,p)) as L0 regularization typically selects a small portion of non-zeros.
cores

The number of cores to use for parallelization with foreach, default is 1.
verbose

Logical value, default is TRUE, whether to print progress messages.
...

Parameters for other methods.

Value

A data table of regulator-target regulatory relationships

Examples

data("example_matrix")
data("example_ground_truth")
network_table_1 <- inferCSN(
  example_matrix
)

network_table_2 <- inferCSN(
  example_matrix,
  cores = 2
)

head(network_table_1)

identical(
  network_table_1,
  network_table_2
)

inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)
inferCSN(
  example_matrix,
  regulators = c("g1", "g2"),
  targets = c("g3", "g0")
)

## Not run: 
network_table_07 <- inferCSN(
  example_matrix,
  r_threshold = 0.7
)
calculate_metrics(
  network_table_1,
  example_ground_truth,
  plot = TRUE
)
calculate_metrics(
  network_table_07,
  example_ground_truth,
  plot = TRUE
)

## End(Not run)
## Not run: 
data("example_matrix")
network_table <- inferCSN(example_matrix)
head(network_table)

network_table_sparse_1 <- inferCSN(
  as(example_matrix, "sparseMatrix")
)
head(network_table_sparse_1)

network_table_sparse_2 <- inferCSN(
  as(example_matrix, "sparseMatrix"),
  cores = 2
)
identical(
  network_table,
  network_table_sparse_1
)

identical(
  network_table_sparse_1,
  network_table_sparse_2
)

plot_scatter(
  data.frame(
    network_table$weight,
    network_table_sparse_1$weight
  ),
  legend_position = "none"
)

plot_weight_distribution(
  network_table
) + plot_weight_distribution(
  network_table_sparse_1
)

## End(Not run)

Print diagnostic message

Description

Print diagnostic message

Usage

log_message(..., verbose = TRUE, message_type = "info", cli_model = TRUE)

Arguments

...

Text to print.
verbose

Logical value, default is TRUE. Whether to print the message.
message_type

Type of message, default is info. Could be choose one of info, warning, and error.
cli_model

Logical value, default is TRUE. Whether to use the cli package to print the message. Add because the message is printed by message, the message could be suppressed by suppressMessages.

Examples

log_message("Hello, ", "world!")
suppressMessages(log_message("Hello, ", "world!"))
log_message("Hello, world!", verbose = FALSE)
log_message("Hello, world!", verbose = TRUE, message_type = "warning")

Switch matrix to network table

Description

Switch matrix to network table

Usage

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

Arguments

network_matrix

The matrix of network weight.
regulators

Regulators list.
targets

Targets list.

Value

Network table

Examples

data("example_matrix")
network_table <- inferCSN(example_matrix)
network_matrix <- table_to_matrix(network_table)
network_table_new <- matrix_to_table(network_matrix)
head(network_table)
head(network_table_new)
identical(
  network_table,
  network_table_new
)

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

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

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

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

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

https://github.com/GfellerLab/SuperCell https://github.com/kuijjerlab/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")
)

Sifting network

Description

Sifting network

Usage

network_sift(
  network_table,
  matrix = NULL,
  meta_data = NULL,
  pseudotime_column = NULL,
  method = c("entropy", "max"),
  entropy_method = c("Shannon", "Renyi"),
  effective_entropy = FALSE,
  shuffles = 100,
  entropy_nboot = 300,
  lag_value = 1,
  entropy_p_value = 0.05,
  cores = 1,
  verbose = TRUE
)

Arguments

network_table

The weight data table of network.
matrix

The expression matrix.
meta_data

The meta data for cells or samples.
pseudotime_column

The column of pseudotime.
method

The method used for filter edges. Could be choose entropy or max.
entropy_method

If setting method to entropy, could be choose Shannon or Renyi to compute entropy.
effective_entropy

Default is FALSE. Logical value, using effective entropy to filter weights or not.
shuffles

Default is 100. The number of shuffles used to calculate the effective transfer entropy.
entropy_nboot

Default is 300. The number of bootstrap replications for each direction of the estimated transfer entropy.
lag_value

Default is 1. Markov order of gene expression values, i.e. the number of lagged values affecting the current value of gene expression values.
entropy_p_value

P value used to filter edges by entropy.
cores

The number of cores to use for parallelization with foreach, default is 1.
verbose

Logical value, default is TRUE, whether to print progress messages.

Value

Sifted network table

Examples

## Not run: 
data("example_matrix")
data("example_meta_data")
data("example_ground_truth")

network_table <- inferCSN(example_matrix)
network_table_sifted <- network_sift(network_table)
network_table_sifted_entropy <- network_sift(
  network_table,
  matrix = example_matrix,
  meta_data = example_meta_data,
  pseudotime_column = "pseudotime",
  lag_value = 2,
  shuffles = 0,
  entropy_nboot = 0
)

plot_network_heatmap(
  example_ground_truth[, 1:3],
  heatmap_title = "Ground truth",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table,
  heatmap_title = "Raw",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table_sifted,
  heatmap_title = "Filtered",
  show_names = TRUE,
  rect_color = "gray70"
)
plot_network_heatmap(
  network_table_sifted_entropy,
  heatmap_title = "Filtered by entropy",
  show_names = TRUE,
  rect_color = "gray70"
)

calculate_metrics(
  network_table,
  example_ground_truth,
  plot = TRUE
)
calculate_metrics(
  network_table_sifted,
  example_ground_truth,
  plot = TRUE
)
calculate_metrics(
  network_table_sifted_entropy,
  example_ground_truth,
  plot = TRUE
)

## End(Not run)

Normalize numeric vector

Description

Normalize numeric vector

Usage

normalization(x, method = "max_min", na_rm = TRUE, ...)

Arguments

x

Input numeric vector.
method

Method used for normalization.
na_rm

Whether to remove NA values, and if setting TRUE, using 0 instead.
...

Parameters for other methods.

Value

Normalized numeric vector

Examples

nums <- c(runif(2), NA, -runif(2))
nums
normalization(nums, method = "max_min")
normalization(nums, method = "maximum")
normalization(nums, method = "sum")
normalization(nums, method = "softmax")
normalization(nums, method = "z_score")
normalization(nums, method = "mad")
normalization(nums, method = "unit_vector")
normalization(nums, method = "unit_vector", na_rm = FALSE)

Parallelize a function

Description

Parallelize a function

Usage

parallelize_fun(x, fun, cores = 1, export_fun = NULL, verbose = TRUE)

Arguments

x

A vector or list to apply over.
fun

The function to be applied to each element.
cores

The number of cores to use for parallelization with foreach, default is 1.
export_fun

The functions to export the function to workers.
verbose

Logical value, default is TRUE, whether to print progress messages.

Value

A list of computed results

Correlation and covariance calculation for sparse matrix

Description

Correlation and covariance calculation for sparse matrix

Usage

pearson_correlation(x, y = NULL)

Arguments

x

Sparse matrix or character vector.
y

Sparse matrix or character vector.

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.
negative_color

Color for negative weights.
neutral_color

Color for weights near zero (used in "continuous" style).
bar_width

Width of the bars.
text_size

Size of the text for weight values.
show_values

Logical, whether to show weight values on bars.

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.
nrow

Number of rows for the plot.
...

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.
weight_value

Weight value to filter edges.
legend_position

The position of legend.

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.
title

The title of figure.
theme_type

Default is theme_void, the theme of figure, could be theme_void, theme_blank or theme_facet.
plot_type

Default is "ggplot", the type of figure, could be ggplot, animate or ggplotly.
layout

Default is "fruchtermanreingold", the layout of figure, could be fruchtermanreingold or kamadakawai.
nrow

The number of rows of figure.
figure_save

Default is FALSE, Logical value, whether to save the figure file.
figure_name

The name of figure file.
figure_width

The width of figure.
figure_height

The height of figure.
seed

Default is 1, the seed random use to plot network.

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"
)

## Not run: 
# If setting `plot_type = "animate"` to plot and save `gif` figure,
# please install `gifski` package first.
plot_dynamic_networks(
  network,
  celltypes_order = celltypes_order,
  plot_type = "animate"
)

## End(Not run)

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 method to umap and 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 = NULL,
  column_anno_type = NULL,
  legend_name = "Weight",
  row_title = "Regulators"
)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.
switch_matrix

Logical value, default is TRUE, whether to weight data table to matrix.
show_names

Logical value, default is FALSE, whether to show names of row and column.
heatmap_size_lock

Lock the size of heatmap.
heatmap_size

Default is 5. The size of heatmap.
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 NULL, could add a annotation plot to row, choose one of boxplot, barplot, histogram, density, lines, points, and horizon.
column_anno_type

Default is NULL, 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)

p3 <- plot_network_heatmap(
  network_table,
  heatmap_title = "inferCSN",
  legend_name = "Weight1",
  heatmap_color = c("#20a485", "#410054", "#fee81f")
)
p4 <- plot_network_heatmap(
  network_table,
  heatmap_title = "inferCSN",
  legend_name = "Weight2",
  heatmap_color = c("#20a485", "white", "#fee81f")
)
ComplexHeatmap::draw(p3 + p4)

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", "g2"),
  show_names = TRUE
)

plot_network_heatmap(
  network_table,
  targets = c("g1", "g2"),
  row_anno_type = "boxplot",
  column_anno_type = "histogram",
  show_names = TRUE
)

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"
)

Predicts response for a given sample

Description

Predicts response for a given sample

Usage

## S3 method for class 'srm'
predict(object, newx, lambda = NULL, gamma = NULL, ...)

## S3 method for class 'srm_cv'
predict(object, newx, lambda = NULL, gamma = NULL, ...)

Arguments

object

The output of fit_sparse_regression.
newx

A matrix on which predictions are made. The matrix should have p columns
lambda

The value of lambda to use for prediction. A summary of the lambdas in the regularization path can be obtained using print.srm.
gamma

The value of gamma to use for prediction. A summary of the gammas in the regularization path can be obtained using print.srm.
...

Other parameters

Details

If both lambda and gamma are not supplied, then a matrix of predictions for all the solutions in the regularization path is returned. If lambda is supplied but gamma is not, the smallest value of gamma is used. In case of logistic regression, probability values are returned.

Value

Return predict value

Prints a summary of fit_sparse_regression

Description

Prints a summary of fit_sparse_regression

Usage

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

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

Arguments

x

The output of fit_sparse_regression.
...

Other parameters

Value

Return information of fit_sparse_regression

R2R^2 (coefficient of determination)

Description

R2R^2 (coefficient of determination)

Usage

r_square(y_true, y_pred)

Arguments

y_true

A numeric vector with ground truth values.
y_pred

A numeric vector with predicted values.

Generate a simulated sparse matrix for single-cell data testing

Description

Generate a simulated sparse matrix for single-cell data testing

Usage

simulate_sparse_matrix(
  nrow,
  ncol,
  density = 0.1,
  distribution_fun = function(n) stats::rpois(n, lambda = 0.5) + 1,
  seed = 1
)

Arguments

nrow

Number of rows (genes) in the matrix.
ncol

Number of columns (cells) in the matrix.
density

Density of non-zero elements (default: 0.1, representing 90 sparsity).
distribution_fun

Function to generate non-zero values.
seed

Random seed for reproducibility.

Value

A sparse matrix of class "dgCMatrix"

Examples

simulate_sparse_matrix(2000, 500) |>
  check_sparsity()

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",
  algorithm = "CD",
  regulators_num = (ncol(matrix) - 1),
  n_folds = 10,
  subsampling_ratio = 1,
  r_threshold = 0,
  verbose = TRUE,
  ...
)

Arguments

matrix

An expression matrix.
regulators

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

The target gene.
cross_validation

Logical value, default is FALSE, whether to use cross-validation.
seed

The random seed for cross-validation, default is 1.
penalty

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

The type of algorithm used to minimize the objective function, default is CD. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
regulators_num

The number of non-zore coefficients, this value will affect the final performance. The maximum support size at which to terminate the regularization path.
n_folds

The number of folds for cross-validation, default is 10.
subsampling_ratio

The percent of all samples used for sparse_regression, default is 1.
r_threshold

Threshold of R2R^2 or correlation coefficient, default is 0.
verbose

Logical value, default is TRUE, whether to print progress messages.
...

Parameters for other methods.

Value

The weight data table of sub-network

Examples

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

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

Safe correlation function which returns a sparse matrix without missing values

Description

Safe correlation function which returns a sparse matrix without missing values

Usage

sparse_cor(
  x,
  y = NULL,
  method = "pearson",
  allow_neg = TRUE,
  remove_na = TRUE,
  remove_inf = TRUE,
  ...
)

Arguments

x

Sparse matrix or character vector.
y

Sparse matrix or character vector.
method

Method to use for calculating the correlation coefficient.
allow_neg

Logical. Whether to allow negative values or set them to 0.
remove_na

Logical. Whether to replace NA values with 0.
remove_inf

Logical. Whether to replace infinite values with 1.
...

Other arguments passed to cor function.

Value

A correlation matrix.

Examples

m1 <- simulate_sparse_matrix(
  1000, 1000,
  density = 0.01
)
m2 <- simulate_sparse_matrix(
  1000, 500,
  density = 0.01
)

all.equal(
  as.matrix(sparse_cor(m1)),
  cor(as_matrix(m1))
)
all.equal(
  as.matrix(sparse_cor(m1, m2)),
  cor(as_matrix(m1), as_matrix(m2))
)

system.time(
  sparse_cor(m1)
)
system.time(
  cor(as_matrix(m1))
)
system.time(
  sparse_cor(m1, m2)
)
system.time(
  cor(as_matrix(m1), as_matrix(m2))
)

# add missing values
m1[sample(1:500, 10)] <- NA
m2[sample(1:500, 10)] <- NA

sparse_cor(m1, m2)[1:5, 1:5]

Sparse regression model

Description

Sparse regression model

Usage

sparse_regression(
  x,
  y,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = ncol(x),
  n_folds = 10,
  subsampling_ratio = 1,
  r_threshold = 0,
  computation_method = "cor",
  verbose = TRUE,
  ...
)

Arguments

x

The matrix of regulators.
y

The vector of target.
cross_validation

Logical value, default is FALSE, whether to use cross-validation.
seed

The random seed for cross-validation, default is 1.
penalty

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

The type of algorithm used to minimize the objective function, default is CD. Currently CD and CDPSI are supported. The CDPSI algorithm may yield better results, but it also increases running time.
regulators_num

The number of non-zore coefficients, this value will affect the final performance. The maximum support size at which to terminate the regularization path.
n_folds

The number of folds for cross-validation, default is 10.
subsampling_ratio

The percent of all samples used for sparse_regression, default is 1.
r_threshold

Threshold of R2R^2 or correlation coefficient, default is 0.
computation_method

The method used to compute 'r“.
verbose

Logical value, default is TRUE, whether to print progress messages.
...

Parameters for other methods.

Value

Coefficients

Examples

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

Split indices.

Description

An optimised version of split for the special case of splitting row indices into groups.

Usage

split_indices(group, n = 0L)

Arguments

group

Integer indices
n

The largest integer (may not appear in index). This is hint: if the largest value of group is bigger than n, the output will silently expand.

Value

A list of vectors of indices.

References

https://github.com/hadley/plyr/blob/d57f9377eb5d56107ba3136775f2f0f005f33aa3/src/split-numeric.cpp#L20

Examples

split_indices(sample(10, 100, rep = TRUE))
split_indices(sample(10, 100, rep = TRUE), 10)

Subsampling function

Description

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

Usage

subsampling(
  matrix,
  subsampling_method = "sample",
  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" or "meta_cells".
subsampling_ratio

The percent of all samples used for sparse_regression, default is 1.
seed

The random seed for cross-validation, default is 1.
verbose

Logical value, default is TRUE, whether to print progress messages.
...

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,
  plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix),
  example_ground_truth,
  plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix_2),
  example_ground_truth,
  plot = TRUE
)
calculate_metrics(
  inferCSN(subsample_matrix_3),
  example_ground_truth,
  plot = TRUE
)

Switch network table to matrix

Description

Switch network table to matrix

Usage

table_to_matrix(network_table, regulators = NULL, targets = NULL)

Arguments

network_table

The weight data table of network.
regulators

Regulators list.
targets

Targets list.

Value

Weight matrix

Examples

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

table_to_matrix(network_table)[1:6, 1:6]

table_to_matrix(
  network_table,
  regulators = c("g1", "g2"),
  targets = c("g3", "g4")
)

Weight sift

Description

Remove edges with smaller weights in the reverse direction.

Usage

weight_sift(table)

Arguments

table

A data frame with three columns: "regulator", "target", and "weight".

Examples

data("example_matrix")
network_table <- inferCSN(example_matrix)
weight_sift(network_table) |> head()