Plot SCEG-HiC predicted enhancer-gene links

Visualizes the predicted enhancer-gene links from SCEG-HiC for a given gene by plotting them in a genome region view.

Usage

connections_Plot(
  SCEG_HiC_Result,
  species,
  genome,
  focus_gene,
  cutoff = NULL,
  upstream = 250000,
  downstream = 250000,
  gene_anno = NULL
)

Arguments

SCEG_HiC_Result

A data.frame containing the output from Run_SCEG_HiC().

species

Character string specifying the species name. Supported values are "Homo sapiens" or "Mus musculus".

genome

Character string specifying the genome assembly. Supported values are "hg38", "hg19", "mm10", or "mm9".

focus_gene

A character vector of gene symbols to focus on.

cutoff

Threshold for selecting gene-peak pairs. Default is NULL. If aggregate = TRUE, we recommend setting cutoff = 0.01. If aggregate = FALSE, we recommend cutoff = 0.001.

upstream

Numeric specifying the number of base pairs upstream of each TSS to define enhancers. Default is 250,000 bp (250 kb).

downstream

Numeric specifying the number of base pairs downstream of each TSS to define enhancers. Default is 250,000 bp (250 kb).

gene_anno

A data.frame or NULL. If provided, should be compatible with the GeneRegionTrack-class. Column names must not contain NA.

Value

A genome browser-style plot displaying the focused gene and predicted enhancer-gene connections.

Examples

#' data(multiomic_small)
SCEGdata <- process_data(multiomic_small, k_neigh = 5, max_overlap = 0.5)
#> Generating aggregated data
#> Aggregating cluster 0
#> Sample cells randomly.
#> There are 11 samples
#> Aggregating cluster 1
#> Sample cells randomly.
#> There are 11 samples
fpath <- system.file("extdata", package = "SCEGHiC")
gene <- c("TRABD2A", "GNLY", "MFSD6", "CTLA4", "LCLAT1", "NCK2", "GALM", "TMSB10", "ID2", "CXCR4")
weight <- calculateHiCWeights(SCEGdata, species = "Homo sapiens", genome = "hg38", focus_gene = gene, averHicPath = fpath)
#> Processing chromosome chr2...
#> Found 10 TSS loci on chr2.
#> Calculating Hi-C weights for gene TRABD2A...
#> Calculating Hi-C weights for gene GNLY...
#> Calculating Hi-C weights for gene MFSD6...
#> Calculating Hi-C weights for gene CXCR4...
#> Calculating Hi-C weights for gene CTLA4...
#> Calculating Hi-C weights for gene LCLAT1...
#> Calculating Hi-C weights for gene NCK2...
#> Calculating Hi-C weights for gene ID2...
#> Calculating Hi-C weights for gene GALM...
#> Calculating Hi-C weights for gene TMSB10...
#> Finished calculating Hi-C weights for all genes.
results_SCEGHiC <- Run_SCEG_HiC(SCEGdata, weight, focus_gene = gene)
#> Total predicted genes: 10
#> Running model for gene: TRABD2A
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.43"
#> Running model for gene: GNLY
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.19"
#> Running model for gene: MFSD6
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.22"
#> Running model for gene: CXCR4
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.14"
#> Running model for gene: CTLA4
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.17"
#> Running model for gene: LCLAT1
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.41"
#> Running model for gene: NCK2
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.25"
#> Running model for gene: ID2
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.13"
#> Running model for gene: GALM
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.11"
#> Running model for gene: TMSB10
#> [1] "The optimal penalty parameter (rho) selected by BIC is: 0.44"
connections_Plot(results_SCEGHiC, species = "Homo sapiens", genome = "hg38", focus_gene = "CTLA4", cutoff = 0.01, gene_anno = NULL)