| Type: | Package |
| Title: | Single-Cell RNA Sequencing Pseudo-Time Based Gene Regulatory Network Inference |
| Version: | 0.3.5 |
| Author: | Gangcai Xie |
| Maintainer: | Gangcai Xie <gcxiester@gmail.com> |
| Description: | Inference and visualize gene regulatory network based on single-cell RNA sequencing pseudo-time information. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 7.2.3 |
| VignetteBuilder: | knitr, rmarkdown |
| Depends: | R (≥ 2.10), |
| Imports: | bigmemory, doParallel, dtw, foreach, ggplot2, ggraph, igraph, lmtest, proxy, tidygraph, visNetwork, future |
| Suggests: | knitr, webshot, rmarkdown, |
| NeedsCompilation: | no |
| Packaged: | 2023-01-16 14:36:55 UTC; gangcai |
| Repository: | CRAN |
| Date/Publication: | 2023-01-17 17:20:02 UTC |
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)