Load and view data | Preprocessing | Coembedding using coFAST | Downstream analysis | Clustering and assess the cluster significance

Fit DR-SC using real data DLPFC | Prepare Seurat object for DR-SC | Data preprocessing | Fit DR-SC model using 500 highly variable features | Visualization | Fit DR-SC model using 480 spatially variable features | Ridge plots | Violin plot | Feature plot | Dot plots | Heatmap plot | Fit DR-SC model using 480 spatially variable features and using MBIC　to determine clusters | Session information

Install the DR.SC | Setup on Linux or MacOS system

Generate the simulated data | Fit DR-SC using simulated data | Data preprocessing | Fit DR-SC based on highly variable genes(HVGs) | Fit DR-SC based on spatially variable genes(SVGs) | DR-SC can enhance visualization | DR-SC can automatically determine the number of clusters | DR-SC can help differentially expression analysis | Ridge plots | Violin plot | Feature plot | Dot plots | Heatmap plot | Session information

Generate the simulated data | Compare with other methods | Comparison of performance

Generate the simulated data | Compare with other methods | Comparison of performance | Choose number of factors

Generate the simulated data | Compare with other methods | Visualize the comparison of performance | Select the parameters

Data preparation | Obtain data matrices and tunning parameters | Determine the structure dimension | Fitting SpaCOAP | Compare SpaCOAP and other methods | Summarize the metrics

Generate the simulated data | Compare with other methods | Visualize the comparison of performance | Select the parameters

Generate the simulated data | Compare with other methods | Visualize the comparison of performance | Select the parameters

Load and view data | Preprocessing | Coembedding using non-centered factor model | Downstream analysis

View the DLPFC data | Preprocessing | Fit FAST for this data | Evaluate the adjusted McFadden's pseudo R-square | Clustering analysis based on FAST embedding | Visualization | Differential epxression analysis

Load and view data | Preprocessing | Coembedding using FAST | Downstream analysis

View the DLPFC data | Create a PRECASTObject object | Prepare the PRECASTObject with preprocessing step. | Fit FAST for this data | Add the model setting | Fit FAST | Evaluate the adjusted McFadden's pseudo R-square | Embedding alignment and spatial clustering using iSC-MEB | Remove unwanted variations in the log-normalized expression matrices | Visualization | Combined differential epxression analysis

View the simulated data | Create a PRECASTObject object | Prepare the PRECASTObject with preprocessing step. | Fit FAST using simulated data | Add the model setting | Fit FAST | Evaluate the adjusted McFadden's pseudo R-square | Embedding alignment and clustering using Harmony and Louvain | Remove unwanted variations in the log-normalized expression matrices | Visualization | Combined differential epxression analysis

Load real data | Fit GFM model | Compare with LFM in downstream analysis

Fit GFM model using simulated data | GFM can handle data with homogeneous normal variables | GFM outperforms LFM in analyzing data with heterogeous normal variables | GFM outperforms LFM in analyzing data with Count(Poisson) variables | GFM outperforms LFM in analyzing data with the mixed-types of count and categorical variables | Session information

Install the GFM

Load GFM package | Load rrpack and PCAmixdata packages for other methods | Introduction to the data generation mechanisms | Brief description of other methods | OverGFM can handle overdispersed mixed-type data | Other methods poorly handle overdispersed mixed-type data | Visualization | Session information