https://github.com/menchelab/infowalkr
package implementation of the MultiOme Paper
https://github.com/menchelab/infowalkr
Last synced: 12 months ago
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package implementation of the MultiOme Paper
- Host: GitHub
- URL: https://github.com/menchelab/infowalkr
- Owner: menchelab
- License: mit
- Created: 2025-06-24T11:08:28.000Z (12 months ago)
- Default Branch: main
- Last Pushed: 2025-06-24T12:13:37.000Z (12 months ago)
- Last Synced: 2025-06-24T12:33:23.714Z (12 months ago)
- Language: R
- Size: 5.78 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# InfoWalkR
package implementation of the MultiOme Paper
```R
library(InfoWalkR, quietly = TRUE)
```
## Data Import
```R
#read in data
path = ''
graph_list = read_graphs_from_folder(path)
graph_list
```
All edgelists are structured like this:
```
# Graph Type: undirected
A B
A1BG ABCC4
A1BG ALB
A1BG APLP2
A1BG BRPF3
```
The Graph type flag is needed to determine whether the graph is directed or not.
When a third column is present that specifies edge weight, the graph is read as a weighted graph.
As long as the node type is the same in the multiplex, different edge types are allowed across different layers.
```R
#test gene set
path_gene_set = ''
# Read the TSV file
data <- read.delim(path_gene_set, sep = "\t", header = TRUE)
# Extract the gene names column as a vector
# in the example, th e
gene_set <- unlist(strsplit(data$all_genes, ";"))
gene_set
```
## Layer Selection
```R
# perform layer selection based on lcc that seed genes form
#alpha sets the threshold for significance
lcc_results = lcc_based_network_selection(graph_list, gene_set,
trial = 100, randomization = 'random',
alpha = 0.05)
#pull results
lcc_results$results
layer_weights = lcc_results$results
sig_layers = lcc_results$significant_networks
```
The lcc_results object contains:
1. the dataframe containing only the significant layers and the associated z_score --> this is for the layer weights (weighted_layer_df)
2. the graph_list containing only the significant layers
## Random Walk
| Parameter | Description |
|-----------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| rank_aggregate | If `TRUE`, aggregates RWR ranking results across all layers and returns summary statistics. If `FALSE`, this step is skipped. |
| rank_individual | If `TRUE`, ranks RWR results within each layer and returns results for all layers individually. If `FALSE`, this step is skipped. |
| remove_seeds | If `TRUE`, removes seed genes from ranking. If `FALSE`, seed genes are retained in the ranking process. |
If both rank_aggregata = FALSE and rank_individual = FALSE, the function returns the raw results for each individual layer
```R
#perform random walk with restart on multiplex network
#the default is equal weights on the seed gene set
infowalk_results = weighted_multiplex_propagation(layer_weights_df = layer_weights,
graph_list = sig_layers,
seed_set = gene_set,
rank_aggregate = TRUE,
rank_individual = FALSE,
remove_seeds = FALSE)
head(infowalk_results)
```
#### Description of summary statistics
| Output | Description |
|------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| mean_arithmetic | The arithmetic mean of visiting probabilities of a given node (gene-wise average across all layers). |
| mean_geometric | Geometric mean of visiting probabilities of a given node (gene-wise geometric mean across all layers). |
| rank_all_geometric | Expresses the visiting probabilities of a node across all layers (aggregated by geometric mean) as a rank. The calculation ranks the visiting probabilities of a node across all layers and then aggregates the ranks by geometric mean. |
| rank_each_geometric | Expresses the visiting probabilities of a node across all layers as a rank, but the calculation first ranks the visiting probabilities for the nodes within each layer and then aggregates the result by geometric mean. |
| rank_mean_arithmetic | Expresses the aggregated visiting probabilities for each node across all layers average as rank. |
| rank_mean_geometric | Expresses the aggregated visiting probabilities for each node across all layers Geometric Avg as rank. |
#### Results for individual layers
```R
infowalk_results = weighted_multiplex_propagation(layer_weights_df = layer_weights,
graph_list = sig_layers,
seed_set = gene_set,
rank_aggregate = FALSE,
rank_individual = TRUE,
remove_seeds = FALSE)
head(infowalk_results)
```
## Visualizations
```R
#plot degree distribution
plot_degree_dist(graph = sig_layers[[1]])
```
```R
#plot z-score ranking of layers
plot_z_score_ranking(results_df = lcc_results$results, alpha= 0.00001)
```
```R
#plot histograms of lcc sizes
plot_lcc_overview(results_df = lcc_results$results)
```
#### pre-calculate the supra-adjacency matrix and then perform weighted multiplex propagation
The calculation of the normalized supra-adjacency matrix takes the most time, therefore pre-computing the supra_adjacecncy matrix might be preferable.
```R
supra_adjacency_matrix = construct_supraadjacency_matrix(layer_weights, sig_layers)
infowalk_results = weighted_multiplex_propagation(supra_adjacency_matrix = supra_adjacency_matrix,
layer_weights_df = layer_weights,
graph_list = sig_layers,
seed_set = gene_set,
rank_aggregate = TRUE,
rank_individual = FALSE,
remove_seeds = FALSE)
```