Seurat3 integration
Last updated
Last updated
Seurat v3[1] introduced new methods for the integration of multiple single-cell datasets, no matter whether they were collected from different individuals, experimental conditions, technologies, etc. Seurat 3 integration method aims to use a subset of the data as reference for the integrate analysis. The method integrates all other data with the reference subset. The subset can be one sample or a subgroup of samples defined by the factor attribute.
Seurat3 integration in Flow can be invoked in Batch removal section if a Normalized counts data node is selected (Figure 1).
To run Seurat3 integration,
Click a Normalized counts data node
Click the Batch removal section in the toolbox
Click Seurat3 Integration
You will be promoted to pick some attribute(s) for analysis. The first Seurat3 integration dialog is a drop-down list that includes the factors for data integration. To set up the model, you need to choose which attribute should be considered. For example, in the case of one dataset that has different cell types from multiple technologies(Tech), different technology may have divergent impacts on different cell types. Hence, the attribute Tech should be considered to be the batch factor_._ The attribute celltype represents different cell types in this dataset (Figure 2).
To integrate data with default settings,
Select Tech from the dropdown list
Click Finish
The output of Seurat3 integration is a new data node - Integrated counts (Figure 1). We can then use this new integrated matrix for downstream analysis and visualization (Figure 3).
Users can click Configure to change the default settings In Advanced options (Figure 4).
Use reference to find anchors: when this box is checked, the first group of the selected attribute is used as reference to find anchors. To use a different group as reference, change the order of subgroups of the attribute in the attribute management page on Data tab. When the box is unchecked, anchors will be identified by comparing all pairs of subgroups, this option is very computationally intensive.
Perform L2 normalization: Perform L2 normalization on the CCA cell embeddings after dimensional reduction.
Pick anchors: How many neighbors (k) to use when picking anchors.
Filter anchors: How many neighbors (k) to use when filtering anchors.
Score anchors: How many neighbors (k) to use when scoring anchors.
Nearest neighbor finding methods: Method for nearest neighbor finding. Options include: rann, annoy.
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Stuart T, Butler A, Hoffman P, et al. Comprehensive integration of single-cell data. Cell, 2019. DOI:https://doi.org/10.1016/j.cell.2019.05.031 \
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