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  "Description": "Tensor Composition Analysis (TCA) allows the deconvolution\nof two-dimensional data (features by observations) coming from\na mixture of heterogeneous sources into a three-dimensional\nmatrix of signals (features by observations by sources). The\nTCA framework further allows to test the features in the data\nfor different statistical relations with an outcome of interest\nwhile modeling source-specific effects; particularly, it allows\nto look for statistical relations between source-specific\nsignals and an outcome. For example, TCA can deconvolve bulk\ntissue-level DNA methylation data (methylation sites by\nindividuals) into a three-dimensional tensor of\ncell-type-specific methylation levels for each individual (i.e.\nmethylation sites by individuals by cell types) and it allows\nto detect cell-type-specific statistical relations\n(associations) with phenotypes. For more details see Rahmani et\nal. (2019) <DOI:10.1038/s41467-019-11052-9>.",
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      "author": "Elior Rahmani",
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        "Tensor Composition Analysis: getting started",
        "Preparing the input",
        "Setting model assumptions and a statistical test for analysis",
        "Model directionality",
        "Statistical testing",
        "Example 1: detecting CD4-specific differential methylation with age",
        "Overview",
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