Add external networks to HuMMuS object

General plan

1. Initialisation of a hummus object

2. Add a new multiplex

3. Add a new bipartite

# install python dependency
# reticulate::py_install("hummuspy", envname = "r-reticulate", pip=TRUE)
reticulate::use_condaenv("r-reticulate")
hummuspy <- reticulate::import("hummuspy")
library(HuMMuS)

##

1. Initialisation of HuMMuS object

HuMMuS R objects are instances developed on top of seurat objects. It means it’s created from a seurat object and the contained assays can be accessed the same way.

Additionally, it contains a motifs_db object, providing tf motifs informations, and a multilayer objects, that will be completed while going through this tutorial. It will mostly include : - list of multiplex networks (one per modality) - list of bipartites (one per connection between layers) hummus object schema

# Create an hummus object from seurat object
# Load the Chen dataset, which is a Seurat object containing scRNA-seq and scATAC-seq data
data("chen_dataset_subset")
chen_dataset_subset

## An object of class Seurat 
## 12000 features across 385 samples within 2 assays 
## Active assay: RNA (2000 features, 0 variable features)
##  2 layers present: counts, data
##  1 other assay present: peaks

hummus <- Initiate_Hummus_Object(chen_dataset_subset)


# wrapper of Signac::GetGRangesFromEnsDb, adapting output to UCSC format
genome_annotations <- get_genome_annotations(
  ensdb_annotations = EnsDb.Hsapiens.v86::EnsDb.Hsapiens.v86)
# Add annotations to peak assay
Signac::Annotation(hummus@assays$peaks) <- genome_annotations
rm(genome_annotations)

# Load TF motifs from JASPAR2020 and chromVARmotifs in hummus object
hummus@motifs_db <- get_tf2motifs() # by default human motifs

2. Add networks

Steps to build HuMMuS objects

2.1 Add a new multiplex through a network

# The networks should be imported as 3 columns data.frame: (source, target, weight)
rna_network_path = "chen_multilayer/multiplex/RNA/RNA_GENIE3.tsv"
rna_network <- read.table(rna_network_path, sep = "\t", header = FALSE)
head(rna_network)

##       V1      V2        V3
## 1  BACH1  COL4A6 0.7388727
## 2  DDIT3 CCDC173 0.7060954
## 3  DDIT3  CCT6P1 0.6697768
## 4  ATF6B  DNASE1 0.6598122
## 5 ARID3B   ACER3 0.6499825
## 6   ATF4  ATP8A1 0.6465523

hummus <- add_network(
    hummus, rna_network,
    multiplex_name = "RNA",
    network_name = "GENIE3",
    weighted = TRUE,
    verbose = 1)

##  Creating new multiplex :  RNA

2.2 Add a network to an existing multiplex

hummus <- add_network(
    hummus, rna_network,
    multiplex_name = "RNA",
    network_name = "GENIE3_other",
    weighted = TRUE)

3 Add external bipartites

Bipartites can be 2 or 3 columns data.frame, depending on if weighted or not. Bipartites not computed through HuMMuS have to be added directly to the object as below:

## Add TF peaks bipartite
atac_rna_path = "chen_multilayer/bipartite/atac_rna.tsv"
atac_rna <- read.table(atac_rna_path, sep = "\t", header = FALSE)
head(atac_rna)

##       V1                        V2
## 1   CMC1    chr3-28241304-28242015
## 2 ANGEL2  chr1-213015141-213016006
## 3  CNNM2 chr10-103053902-103054565
## 4  DNM1L   chr12-32678989-32680007
## 5   DHX9  chr1-182838974-182840143
## 6 B3GAT2    chr6-70955759-70956501

hummus@multilayer@bipartites[["atac_rna"]] <- new("bipartite",
                           "network" = atac_rna,
                           "multiplex_left" = "RNA",
                           "multiplex_right" = "peaks")

Display HuMMuS object

hummus

## Hummus object containing a multilayer object :
## Multilayer network containing  1  bipartite networks and  1  multiplex networks.
##  
## - Multiplex names:  RNA 
## - Bipartite names:  atac_rna 
## 
## 
## And a Seurat object :
## 
## 12000 features across samples within 2 assays 
## Active assay: RNA ( features, 0 variable features)
##  1 other assay present: peaks

hummus@multilayer@multiplex

## $RNA
## Multiplex of  2  networks with 2000 features.
##  Networks names:  GENIE3, GENIE3_other

Trimbour Rémi

2023-05-16