Package 'inferCSN' reference manual

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:	2024-11-23 06:34:01 UTC
Source:	CRAN

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

Convert dgCMatrix into a dense matrix

Description

Convert dgCMatrix into a dense matrix

Usage

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

dims_i <- 2000
dims_j <- 2000
sparse_matrix <- Matrix::sparseMatrix(
  i = sample(1:dims_i, 500),
  j = sample(1:dims_j, 500),
  x = rnorm(500),
  dims = c(dims_i, dims_j),
  dimnames = list(
    paste0("a", rep(1:dims_i)),
    paste0("b", rep(1:dims_j))
  )
)

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_1 <- inferCSN(
  as_matrix(example_matrix, sparse = TRUE)
)
network_table_2 <- inferCSN(
  as(example_matrix, "sparseMatrix")
)

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

## End(Not run)
dims_i <- 2000
dims_j <- 2000
sparse_matrix <- Matrix::sparseMatrix(
  i = sample(1:dims_i, 500),
  j = sample(1:dims_j, 500),
  x = rnorm(500),
  dims = c(dims_i, dims_j),
  dimnames = list(
    paste0("a", rep(1:dims_i)),
    paste0("b", rep(1:dims_j))
  )
)

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_1 <- inferCSN(
  as_matrix(example_matrix, sparse = TRUE)
)
network_table_2 <- inferCSN(
  as(example_matrix, "sparseMatrix")
)

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

## End(Not run)

Calculate accuracy value

Description

Calculate accuracy value

Usage

calculate_acc(network_table, ground_truth)
calculate_acc(network_table, ground_truth)

Arguments

`network_table`	The weight data table of network
`ground_truth`	Ground truth for calculate AUC

Value

ACC value

Examples

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

Calculate AUPRC and AUROC values

Description

Calculate AUPRC and AUROC values

Usage

calculate_auc(
  network_table,
  ground_truth,
  plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)
calculate_auc(
  network_table,
  ground_truth,
  plot = FALSE,
  line_color = "#1563cc",
  line_width = 1
)

Arguments

`network_table`	The weight data table of network
`ground_truth`	Ground truth for calculate AUC
`plot`	If true, draw and print figure of AUC
`line_color`	The color of line in the figure
`line_width`	The width of line in the figure

Value

AUC values and figure

Examples

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

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
)
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"))
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"))

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, ...)
## 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. 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.
`...`	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)
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")
)
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,
  ...
)
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,
  ...
)

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. 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.
`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.
`...`	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))
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,
  percent_samples = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = NULL,
  cores = 1,
  verbose = TRUE,
  ...
)

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

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

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

## S4 method for signature 'sparseMatrix'
inferCSN(
  object,
  penalty = "L0",
  algorithm = "CD",
  cross_validation = FALSE,
  seed = 1,
  n_folds = 10,
  percent_samples = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = 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,
  percent_samples = 1,
  r_threshold = 0,
  regulators = NULL,
  targets = NULL,
  regulators_num = 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`.
`percent_samples`	The percent of all samples used for `sparse_regression`, default is `1`.
`r_threshold`	Threshold of $R^2$ 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.
`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. 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")
network_table_1 <- inferCSN(
  example_matrix
)
head(network_table_1)

network_table_2 <- inferCSN(
  example_matrix,
  cores = 2
)

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")
)
inferCSN(
  example_matrix,
  regulators = c("g1", "g0"),
  targets = c("g2", "g3")
)
inferCSN(
  example_matrix,
  regulators = c("g1"),
  targets = c("g2")
)
## 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)
data("example_matrix")
network_table_1 <- inferCSN(
  example_matrix
)
head(network_table_1)

network_table_2 <- inferCSN(
  example_matrix,
  cores = 2
)

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")
)
inferCSN(
  example_matrix,
  regulators = c("g1", "g0"),
  targets = c("g2", "g3")
)
inferCSN(
  example_matrix,
  regulators = c("g1"),
  targets = c("g2")
)
## 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)
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")
log_message("Hello, ", "world!")
suppressMessages(log_message("Hello, ", "world!"))
log_message("Hello, world!", verbose = FALSE)
log_message("Hello, world!", verbose = TRUE, message_type = "warning")

Format network table

Description

Format network table

Usage

network_format(
  network_table,
  regulators = NULL,
  targets = NULL,
  abs_weight = TRUE
)
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 = c("g1")
)

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

network_format(
  network_table,
  targets = c("g3")
)

network_format(
  network_table,
  regulators = c("g1", "g3"),
  targets = c("g3", "g5")
)
data("example_matrix")
network_table <- inferCSN(example_matrix)

network_format(
  network_table,
  regulators = c("g1")
)

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

network_format(
  network_table,
  targets = c("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
)
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_auc(
  network_table,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  network_table_sifted,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  network_table_sifted_entropy,
  example_ground_truth,
  plot = TRUE
)

## End(Not run)
## 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_auc(
  network_table,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  network_table_sifted,
  example_ground_truth,
  plot = TRUE
)
calculate_auc(
  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)
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.

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)
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)

Plot contrast networks

Description

Plot contrast networks

Usage

plot_contrast_networks(
  network_table,
  degree_value = 0,
  weight_value = 0,
  legend_position = "bottom"
)
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, ])
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
)
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)
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
)
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
)
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 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"
)
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
)
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
)
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
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"
)
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 = network_table[1, 1]
)
plot_static_networks(
  network_table,
  targets = network_table[1, 1]
)
plot_static_networks(
  network_table,
  regulators = network_table[1, 1],
  targets = network_table[1, 2]
)
data("example_matrix")
network_table <- inferCSN(example_matrix)
plot_static_networks(
  network_table,
  regulators = network_table[1, 1]
)
plot_static_networks(
  network_table,
  targets = network_table[1, 1]
)
plot_static_networks(
  network_table,
  regulators = network_table[1, 1],
  targets = network_table[1, 2]
)

Plot weight distribution

Description

Plot weight distribution

Usage

plot_weight_distribution(
  network_table,
  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"
)
plot_weight_distribution(
  network_table,
  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

`network_table`	The weight data table of network.
`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

ggplot object

Examples

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

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, ...)
## 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, ...)
## 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

$R^2$ (coefficient of determination)

Description

$R^2$ (coefficient of determination)

Usage

r_square(y_true, y_pred)
r_square(y_true, y_pred)

Arguments

`y_true`	A numeric vector with ground truth values.
`y_pred`	A numeric vector with predicted values.

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,
  percent_samples = 1,
  r_threshold = 0,
  verbose = FALSE,
  ...
)
single_network(
  matrix,
  regulators,
  target,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = (ncol(matrix) - 1),
  n_folds = 10,
  percent_samples = 1,
  r_threshold = 0,
  verbose = FALSE,
  ...
)

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. 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.
`n_folds`	The number of folds for cross-validation, default is `10`.
`percent_samples`	The percent of all samples used for `sparse_regression`, default is `1`.
`r_threshold`	Threshold of $R^2$ 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 = "g1",
  target = "g2"
)
data("example_matrix")
head(
  single_network(
    example_matrix,
    regulators = colnames(example_matrix),
    target = "g1"
  )
)

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

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,
  percent_samples = 1,
  r_threshold = 0,
  computation_method = "cor",
  verbose = TRUE,
  ...
)
sparse_regression(
  x,
  y,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = ncol(x),
  n_folds = 10,
  percent_samples = 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. 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.
`n_folds`	The number of folds for cross-validation, default is `10`.
`percent_samples`	The percent of all samples used for `sparse_regression`, default is `1`.
`r_threshold`	Threshold of $R^2$ 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(
  example_matrix[, -1],
  example_matrix[, 1]
)
data("example_matrix")
sparse_regression(
  example_matrix[, -1],
  example_matrix[, 1]
)

Switch network table to matrix

Description

Switch network table to matrix

Usage

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

`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.

Package 'inferCSN'

Help Index

inferCSN: inferring Cell-Specific gene regulatory Network

Description

Author(s)

Source

See Also

Convert dgCMatrix into a dense matrix

Description

Usage

Arguments

Examples

Calculate accuracy value

Description

Usage

Arguments

Value

Examples

Calculate AUPRC and AUROC values

Description

Usage

Arguments

Value

Examples

Rank TFs and genes in network

Description

Usage

Arguments

Value

Examples

Check sparsity of matrix

Description

Usage

Arguments

Value

Extracts a specific solution in the regularization path

Description

Usage

Arguments

Value

Example ground truth data

Description

Example matrix data

Description

Example meta data

Description

Filter and sort matrix

Description

Usage

Arguments

Value

Examples

Fit a sparse regression model

Description

Usage

Arguments

Value

References

Examples

inferring Cell-Specific gene regulatory Network

Description

Usage

Arguments

Value

Examples

Print diagnostic message

Description

Usage

Arguments

Examples

Format network table

Description

Usage

Arguments

Value

Examples

Sifting network

Description

Usage

Arguments

Prints a summary of `fit_sparse_regression`

$R^2$ (coefficient of determination)