align_embedding: Gene Network Reconstruction and Alignment

In scPOEM: Single-Cell Meta-Path Based Omic Embedding

Gene Network Reconstruction and Alignment

Description

Reconstruct gene networks via epsilon-NN and compare conditions using manifold alignment implemented in scTenifoldNet.

Usage

align_embedding(
  gene_data1,
  gene_node1,
  E1,
  gene_data2,
  gene_node2,
  E2,
  dirpath = tempdir(),
  save_file = TRUE,
  d = 100
)

Arguments

gene_data1	The information for genes in state1, must have a col names "gene_name".
gene_node1	Gene ids that are associated with other peaks or genes in state1.
E1	Embedding representations of peaks and genes in state1.
gene_data2	The information for genes in state2, must have a col names "gene_name".
gene_node2	Gene ids that are associated with other peaks or genes in state2.
E2	Embedding representations of peaks and genes in state2.
dirpath	The folder path to read or write file
save_file	Logical, whether to save the output to a file.
d	The dimension of latent space.

Value

A list containing the following elements:

E_g2

Low-dimensional embedding representations of genes under the two conditions.

common_genes

Genes shared between both conditions and used in the analysis.

diffRegulation

A list of differential regulatory information for each gene.

Examples

library(scPOEM)
library(monocle)
dirpath <- "./example_data"
# Download compare mode example data
data(example_data_compare)
data_S1 <- example_data_compare$S1
data_S2 <- example_data_compare$S2
gg_net1 <- GGN(data_S1$Y, file.path(dirpath, "compare/S1"), save_file=FALSE)
pp_net1 <- PPN(data_S1$X, data_S1$peak_data, data_S1$cell_data,
               data_S1$genome, file.path(dirpath, "compare/S1"), save_file=FALSE)

net_Lasso1 <- PGN_Lasso(data_S1$X, data_S1$Y,
                        data_S1$gene_data, data_S1$neibor_peak,
                        file.path(dirpath, "compare/S1"), save_file=FALSE)
net_RF1 <- PGN_RF(data_S1$X, data_S1$Y, data_S1$gene_data,
                  data_S1$neibor_peak, file.path(dirpath, "compare/S1"), save_file=FALSE)
net_XGB1 <- PGN_XGBoost(data_S1$X, data_S1$Y,
                        data_S1$gene_data, data_S1$neibor_peak,
                        file.path(dirpath, "compare/S1"), save_file=FALSE)
pg_net_list1 <- list(net_Lasso1, net_RF1, net_XGB1)
E_result_S1 <- pg_embedding(gg_net1, pp_net1, pg_net_list1,
               file.path(dirpath, "compare/S1"), save_file=FALSE)

gg_net2 <- GGN(data_S2$Y, file.path(dirpath, "compare/S2"), save_file=FALSE)
pp_net2 <- PPN(data_S2$X, data_S2$peak_data,
               data_S2$cell_data, data_S2$genome,
               file.path(dirpath, "compare/S2"), save_file=FALSE)
net_Lasso2 <- PGN_Lasso(data_S2$X, data_S2$Y,
                        data_S2$gene_data, data_S2$neibor_peak,
                        file.path(dirpath, "compare/S2"), save_file=FALSE)
net_RF2 <- PGN_RF(data_S2$X, data_S2$Y, data_S2$gene_data,
                  data_S2$neibor_peak, file.path(dirpath, "compare/S2"), save_file=FALSE)
net_XGB2 <- PGN_XGBoost(data_S2$X, data_S2$Y,
                        data_S2$gene_data, data_S2$neibor_peak,
                        file.path(dirpath, "compare/S2"), save_file=FALSE)
pg_net_list2 <- list(net_Lasso2, net_RF2, net_XGB2)
E_result_S2 <- pg_embedding(gg_net2, pp_net2, pg_net_list2,
               file.path(dirpath, "compare/S2"), save_file=FALSE)

compare_result <- align_embedding(data_S1$gene_data,
                                  E_result_S1$gene_node,
                                  E_result_S1$E,
                                  data_S2$gene_data,
                                  E_result_S2$gene_node,
                                  E_result_S2$E,
                                  file.path(dirpath, "compare/compare"),
                                  save_file=FALSE)

scPOEM documentation built on Aug. 28, 2025, 9:09 a.m.