Package 'pGRN'

Pseudotime based Expression Data Transformation

Description

Based on single-cell pseudotime information, get the sliding window average expression, and then standard normlize the expression for each gene

Usage

data_transform(data, pseudotime, slide_window_size = 100, slide_step_size = 50)

Arguments

data	expression matrix data
pseudotime	list of pseudotime
slide_window_size	sliding window size
slide_step_size	sliding window step size

Value

Transformed new matrix

Examples

data <- matrix(1,100,1000)
ptime <- seq(1:1000)
data_transform(data,
               ptime,
               slide_window_size=100,
               slide_step_size=50)

---

Bidirectional DTW Distance

Description

Get bidirectional DTW distance.

Usage

get_dtw_dist_bidirectional(x, y)

Arguments

x	list of x input
y	list of y input

Value

numeric

Examples

get_dtw_dist_bidirectional(c(1:1000),c(1:1000))

---

DTW distance matrix for all genes

Description

Get DTW distance matrix for all genes using pseudotime based sliding window transfromation, parallel computing allowed.

Usage

get_dtw_dist_mat(
  data,
  ptime,
  slide_window_size = 50,
  slide_step_size = 25,
  cores = 2
)

Arguments

data	gene expression matrix (Gene * Cells)
ptime	pseudotime matched with the column cells of the gene expression matrix
slide_window_size	sliding window size
slide_step_size	sliding window step size
cores	number of cores for parallel computing

Value

bidirectional DTW distance matrix

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)

---

Get the list of sub-networks

Description

Get sub-networks based on given adjacency data.frame input

Usage

get_networks(
  data,
  centrality_degree_mod = "out",
  components_mod = "weak",
  network_min_genes = 10
)

Arguments

data	adjacency data.frame
centrality_degree_mod	mode of centrality degree for popularity calculation
components_mod	mode of sub-network extraction methods
network_min_genes	minimal number of gene elements required for extracted sub-networks

Value

list of tabl_graph objects

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)
adj_df <- matrix2adj(dtw_dist_matrix)
get_networks(adj_df,network_min_genes=5)

---

Convert distance matrix to adjacency dataframe

Description

Convert distance matrix to adjacency dataframe for network construction.

Usage

matrix2adj(data, quantile_cutoff = 5)

Arguments

data	distance matrix
quantile_cutoff	an integer value (1-99) for quantile cutoff

Value

adjacency dataframe (with columns "from, to, distance,direction, similarity")

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)
adj_df <- matrix2adj(dtw_dist_matrix)

---

Get module level networks

Description

Given a distance matrix, calculate gene modules based on hierarchical clustering method and then get module level networks

Usage

module_networks(
  data,
  k = 10,
  quantile_cutoff = 10,
  centrality_degree_mod = "out",
  components_mod = "weak",
  network_min_genes = 10
)

Arguments

data	distance matrix
k	number of gene clusters for module inference
quantile_cutoff	distance cutoff based on quantile(1-99) for edge identification
centrality_degree_mod	"in" or "out" for nodes popularity calculation
components_mod	"weak" or "strong" for sub-network components inference
network_min_genes	minial number of genes required for a network

Value

a list networks for each module

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)
nets <- module_networks(dtw_dist_matrix,k=1,quantile_cutoff=50)
plot_network(nets[["module1"]])

---

pGRN: creates gene regulatory network based on single cell pseudotime information

Description

Given single cell matrix and pseudotime, construct gene regulatory network (GRN)

Usage

pGRN(
  expression_matrix,
  pseudotime_list,
  method = "DTW",
  slide_window_size = 20,
  slide_step_size = 10,
  centrality_degree_mod = "out",
  components_mod = "weak",
  network_min_genes = 10,
  quantile_cutoff = 5,
  order = 1,
  cores = 1
)

Arguments

expression_matrix	expression matrix data
pseudotime_list	list of pseudotime
method	method for GRN construction: DTW, granger
slide_window_size	sliding window size
slide_step_size	sliding window step size
centrality_degree_mod	(for DTW method) mode of centrality degree for popularity calculation
components_mod	(for DTW method) mode of sub-network extraction methods (weak or strong)
network_min_genes	minimal number of gene elements required for extracted sub-networks
quantile_cutoff	an integer value (1-99) for quantile cutoff
order	(for granger method) integer specifying the order of lags to include in the auxiliary regression
cores	number of cores for parallel computing

Value

a list of tabl_graph objects

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime

# try DTW method
nets <- pGRN(expression_matrix,
             pseudotime_list, 
             method= "DTW",
             quantile_cutoff=50,
             cores=1)
plot_network(nets[[1]])

# plot the network interactively
plot_network_i(nets[[1]])

---

pGRN example data

Description

A list with expression dataframe and pseudotime dataframe

Usage

pGRNDB

Format

pGRNDB

A list with items expression and ptime

expression

data frame of single cell expression

ptime

pseudotime of the single cells

...

Source

pGRN

---

Plot stationary network

Description

Plot stationary network through ggraph

Usage

plot_network(graph, ...)

Arguments

graph	a tbl_graph object
...	other parameters for ggraph

Value

ggraph

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)
nets <- module_networks(dtw_dist_matrix,k=1,quantile_cutoff=50)
plot_network(nets[["module1"]])

---

Plot interactive network

Description

Plot interactive network based on igraph layout input

Usage

plot_network_i(graph, save_file = NULL)

Arguments

graph	igraph layout object
save_file	file name of the saved file, not save if NULL

Value

visNetwork htmlwidget

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
dtw_dist_matrix <- get_dtw_dist_mat(expression_matrix,
                                    pseudotime_list,
                                    cores=1)
nets <- module_networks(dtw_dist_matrix,k=1,quantile_cutoff=50)
plot_network_i(nets[["module1"]])

---

Get network adjacency dataframe based on DTW method

Description

Use DTW to calcuate gene-gene distance based on their expression and pseudotime

Usage

run_dtw(
  expression_matrix,
  pseudotime_list,
  slide_window_size = 50,
  slide_step_size = 25,
  quantile_cutoff = 5,
  cores = 1
)

Arguments

expression_matrix	expression matrix data
pseudotime_list	list of pseudotime
slide_window_size	sliding window size
slide_step_size	sliding window step size
quantile_cutoff	an integer value (1-99) for quantile cutoff
cores	number of cores for parallel computing

Value

adjacency dataframe (with columns "from, to, distance,direction, similarity")

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
adj_df <- run_dtw(expression_matrix,
                  pseudotime_list,
                  quantile_cutoff=50,
                  cores=1)

---

Use Granger-causality Test to get gene-gene regulatory relationship

Description

Based on single-cell gene expression matrix and pseudotime, calculate Granger-causality Test based gene-gene regulatory relationship

Usage

run_granger_test(
  data,
  ptime,
  slide_window_size = 20,
  slide_step_size = 10,
  pvalue_cutoff = 0.01,
  order = 1,
  ...
)

Arguments

data	gene expression matrix (Gene * Cells)
ptime	pseudotime matched with the column cells of the gene expression matrix
slide_window_size	sliding window size
slide_step_size	sliding window step size
pvalue_cutoff	cutoff for the pvalue from transfer entropy test
order	integer specifying the order of lags to include in the auxiliary regression
...	other parameters for grangertest function in lmtest

Value

adjacency data frame

Examples

example_data <- pGRNDB
expression_matrix <- example_data[["expression"]]
pseudotime_list <- example_data[["ptime"]]$PseudoTime
gt_adj_df <- run_granger_test(expression_matrix, pseudotime_list)

---

Sliding Window Average

Description

Get sliding windows average values for given vector/list

Usage

slideWindows(data, window = 2, step = 1)

Arguments

data	list of expression
window	sliding window size
step	sliding window step size

Value

list/vector of sliding windows with average expression value

Examples

slideWindows(c(1:1000),window=200,step=100)
slideWindows(c(1:1000),window=100,step=50)

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