MIA is a one-stop platform for multi-omics integrative analysis, committing to clustering and identifying feature genes by supervised and unsupervised learning algorithms. Based on input data from single or multiple omics, it is capable for disease subtyping/classification, feature gene identification, differential analysis between normal and disease samples, tissue/cell clustering, etc.

Clustering & Feature Gene Identification

Input omics data
  • Input at least one omics dataset. Data file type: .txt; Data separator: Tab;
    Rows:
    genes/features; Columns: samples.

    e.g., Transcriptome

    For two or three omics types: genes/samples should be arranged consistently
    in different datasets.

    e.g., CNV
    e.g., DNA methylation

    Cluster or Input labels

    To cluster unlabeled samples, please specify the cluster number (2~10) or select “auto”, which means that an optimal cluster number will be determined automatically.

    For labeled samples, please input label file. Data file type: .txt; Data separator: Tab; First column: sample; Second column: label.

    * Get results: