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Fig. 2 | Genome Biology

Fig. 2

From: MUON: multimodal omics analysis framework

Fig. 2

Example multi-omics analysis workflows implemented using MUON. a Construction and processing of individual modalities of a multi-omics scRNA-seq and scATAC-seq dataset. Processing steps for individual omics from left to right. Rectangles denote count matrices following each processing step, which are stored in a shared MUON data container. MUON provides processing functionalities for a wide range of single-omics, including RNA-seq, ATAC-seq, CITE-seq. Existing workflows and methods can be utilised, including those implemented in scanpy. Respective analysis steps are described below each step. b Alternative workflows for integrating multiple omics for latent space inference and clustering. MUON enables combining alternative analysis steps to define tailored multi-omics data integrations. Shown are canonical workflows from left to right: dimensionality reduction, definition of cell neighbourhood graphs, followed by either nonlinear estimation of cell embeddings or clustering. Letters W and Z denote matrices with feature weights (loadings) and factors (components), respectively. Triangles represent cell-cell distance matrices, with shading corresponding to cell similarity. Green colour signifies steps that combine information from multiple modalities; steps based on individual modalities only are marked with blue (RNA) or red (ATAC) respectively. The outputs of the respective workflows (right) are from top to bottom: UMAP space (i) and cell labels (ii) based on RNA or alternatively based on ATAC modality (iii, iv), cell labels based on two cell neighbour graphs from individual modalities (v), UMAP space and cell labels based on WNN output (vi, vii), UMAP space and cell labels based on MOFA output (viii, ix)

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