Downstream analysis for integrated multi-omics data - Clustering models

Downstream analysis for integrated multi-omics data - Clustering models

integrative_results_clustering(
  multiassay,
  integration_model = "iCluster",
  dependent = "diagnosis",
  sense_check_variable = "PHF1",
  covariates = c("PHF1", "amyloid", "PHF1"),
  probability_threshold = 0.8,
  correlation_threshold = 0.4,
  log2FoldChange = 1,
  community_detection = "leading_eigen",
  disease_id = "MONDO_0004975",
  TF_fp,
  ctd
)

Arguments

multiassay

Multiassay experiment object generated by Omix

integration_model

Possible clustering integration methods are iCluster

dependent

Dependent variable for groups.

sense_check_variable

sense check

covariates

clinical covariates

probability_threshold

Posterior probability minimum threshold

correlation_threshold

Absolute correlation threshold to draw edges in the network

log2FoldChange

logFC cutoff

community_detection

community detection method

disease_id

OpenTargets disease id. Default to "MONDO_0004975" (Alzheimer's Disease) Different disease ontologies can be found on https://www.ebi.ac.uk/ols/ontologies

TF_fp

file path GMT file with Transcription Factors and target genes. Check https://maayanlab.cloud/chea3/

ctd

CellTypeData file. Generated by EWCE package

Value

List object of integrated results

See also

Other Multi-omic integration downstream analysis: OpenTarget_dataframe(), Transcription_Factor_enrichment_supervised(), Transcription_Factor_enrichment(), circos_TF(), clustering_DE_analysis(), extract_loadings(), extract_multiomic_signature(), extract_weigths(), integrative_results_sparse_supervised(), integrative_results_supervised(), integrative_results_unsupervised(), multiomics_modules(), multiomics_network_matrix(), multiomics_network(), plot_module_trajectory(), posterior_probability(), pseudotime_inference(), sign_signature()