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Example usage of the Molecular Expression Assay Loader application.
- 1.If launching the app from outside the Platform, first download and set up dx-toolkit. If running the app from the Platform (Cloud Workspace, JupyterLab, etc.) dx-toolkit is already installed and ready for use.
- 2.From CLI, launch the app using
dx run. For example:
dx run app-molecular-expression-assay-loader \
--name="My Molecular Expression Assay Loader Job" \
-i source_expression_data=example_matrix.csv \
-i assay_title="My Expression Project" \
-i database="my_expression_project" \
-i reference="GRCh38.p13" \
-i feature_type="mRNA" \
-i feature_id_type="transcript_ENST" \
-i feature_value_type="tpm" \
-i dataset_name="My Expression Project Dataset"
//Note that there does not need to be a space between -i and the variable name
// "-ivariable" and "-i variable" are both acceptable
A Dataset created by the Molecular Expression Assay Loader App may be used in the Cohort Browser just like any other Dataset, however only the phenotypic data will be available for cohort browsing and cohort criteria selection. Either double click on the Dataset (a Record entity on the Platform) or right click on More Actions and select Explore Data.
The Dataset created by the Molecular Expression Assay Loader app can be linked to an existing Dataset that contains either phenotypic or phenotypic and assay data to create a combined new Dataset for use. This can be done by adding this Dataset as an input to the Assay Dataset Merger app. This allows for the creation of a rich clinico-omic Dataset where both the molecular information and clinical data related to your study are linked together to accelerate analysis.
A Spark-enabled JupyterLab instance may be used to parse assay metadata and access molecular expression data, as referenced in a Molecular Expression Assay Loader Dataset. See the tutorial notebook in OpenBio.