Chromatin Accessibility and Expression Analysis from Matrix

The workflow Chromatin Accessibility and Expression Analysis from Matrix takes a pair of an Expression Matrix (Image expression_matrix_track_16_n_p) / (Image expr_matrix_spliced_unspliced_16_n_p) and a Peak Count Matrix (Image peak_count_matrix_16_n_p) as input to jointly analyze scRNA-Seq and scATAC-Seq data originating from the same sample or samples.

The expression matrix is analyzed as described in Expression Analysis from Matrix. Clustering and dimensionality reduction are performed using both expression and peak matrices.

The workflow allows for a combined analysis to produce:

The workflow can be found here:

        Template Workflows | Single Cell Workflows (Image sc_workflow_folder_open_16_n_p) | From Imported Data (Image sc_wf_from_imported_folder_open_16_n_p) | Chromatin Accessibility and Expression Analysis from Matrix (Image atac_matrix_expression_16_n_p)

If you are connected to a CLC Server via the CLC Workbench, you will be asked where you would like to run the analysis. We recommend that you run the analysis on a CLC Server when possible.

Choose either one or more Expression Matrix (Image expression_matrix_track_16_n_p) / (Image expr_matrix_spliced_unspliced_16_n_p) and Peak Count Matrix (Image peak_count_matrix_16_n_p) or Select files for import and select the formats that are compatible with the selected inputs. Read more about import options in Importing data.

The workflow offers a number of options. Note that not all parameters can be configured. Open parameters indicate places where customization may be necessary for different samples, but default settings are suitable in most cases.

The workflow can be run using Single Cell hg38 (Ensembl) or Single Cell Mouse (Ensembl) reference data sets (see Reference data management).

Note: Reference data elements cannot be configured during workflow execution. If other elements than those provided in the default reference data sets are needed, a custom reference data set can be used, see  https://resources.qiagenbioinformatics.com/manuals/clcgenomicsworkbench/current/index.php?manual=Custom_Sets.html. When creating custom reference data sets, the chosen gene track needs to match the gene annotations used for training the provided Cell Type Classifier (Image cell_type_classifier_16_n_p) (see Features used for training and prediction).



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