Downstream analysis for integrated multi-omics data - Sparse Supervised models

Downstream analysis for integrated multi-omics data - Sparse Supervised models

integrative_results_sparse_supervised(
  multiassay,
  integration_model = "sMBPLS",
  dependent = "PHF1",
  component = 1,
  sense_check_variable = "PHF1",
  covariates = c("PHF1", "amyloid", "AT8"),
  correlation_threshold = 0.4,
  community_detection = "leading_eigen",
  disease_id = "MONDO_0004975",
  TF_fp,
  ctd
)

Arguments

multiassay

Multiassay experiment object generated by Omix

integration_model

Possible unsupervised integration methods are DIABLO, sMBPLS

dependent

Dependent variable for groups.

component

Component to extract the multi-omics singature from

sense_check_variable

sense check

covariates

cov

correlation_threshold

Absolute correlation threshold to draw edges in the network

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_clustering(), integrative_results_supervised(), integrative_results_unsupervised(), multiomics_modules(), multiomics_network_matrix(), multiomics_network(), plot_module_trajectory(), posterior_probability(), pseudotime_inference(), sign_signature()