This is the public repository of the Sydney Precision Data Science Centre at the University of Sydney. Here, you can access R / Python packages developed by members of Sydney Precision Data Science covering a broad range of topics ranging from generating predictive biomarkers to single cell data analysis.
Additionally, open analyses and data from published papers, workshops, workflows, and useful scripts are released here.
| Name | Description | Single Cell | Precision Medicine | Spatial | Multiomics | On | |
|---|---|---|---|---|---|---|---|
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CiteFuse (paper) | A suite of tools for pre-processing, modality integration, clustering, differential RNA and ADT expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of CITE-seq data | ✔️ | ✔️ | BioC | ||
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ClassifyR (paper) | A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing | ✔️ | BioC | |||
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CPOP (paper) | A statistical machine learning framework for wider implementation of precision medicine | ✔️ | ||||
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DCARS (paper) | Differential correlation across ranked samples | |||||
| directPA (paper) | Pathway analysis in experiments with multiple perturbation designs | ✔️ | CRAN | ||||
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FuseSOM (paper) | A correlation based Multiview Self Organizing Map for the characterisation of cell types in highly multiplexed in situ imaging cytometry assays | ✔️ | ✔️ | BioC | ||
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hRUV (paper) | Normalisation of multiple batches of metabolomics data in a hierarchical strategy with use of samples replicates in large-scale studies | ✔️ | ||||
| Hydra (paper) | Interpretable deep generative ensemble learning for single-cell omics with Hydra | ✔️ | ✔️ | PyPI | |||
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lisaClust (paper) | Clustering of local indicators of spatial association | ✔️ | ✔️ | BioC | ||
| MoleculeExperiment (paper) | Provide functionality for the representation and summarisation of imaging-based spatial transcriptomics data | ✔️ | ✔️ | BioC | |||
| NEMoE (paper) | A nutrition-aware regularised mixture of experts model | ✔️ | |||||
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scClassify (paper) | Single cell classification via cell-type hierarchies based on ensemble learning and sample size estimation. | ✔️ | ||||
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scDC (paper) | Perform differential composition analysis on scRNA-seq data | ✔️ | ||||
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scdney (paper) | A collection of single cell analysis R packages | ✔️ | ||||
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scFeatures (paper) | Generates multi-view representations of single-cell and spatial data through the construction of a total of 17 feature types | ✔️ | ✔️ | BioC | ||
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scHOT (paper) | Single cell higher order testing | ✔️ | ✔️ | BioC | ||
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scMerge (paper) | Statistical approach for removing unwanted variation from multiple single-cell datasets | ✔️ | BioC | |||
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scReClassify (paper) | Post hoc cell type classification of single-cell RNA-sequencing data. | ✔️ | BioC | |||
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SimBench (paper) | Benchmark simulation methods based on two key aspects of accuracy of data properties estimation and ability to retain biological signals | |||||
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spicyR (paper) | Spatial analysis of in situ cytometry data. | ✔️ | ✔️ | BioC | ||
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StabMap (paper) | Mosaic single cell data integration using non-overlapping features | ✔️ | ✔️ | |||
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treekoR (paper) | Utilise the hierarchical nature of single cell cytometry data, to find robust and interpretable associations between cell subsets and patient clinical end points | ✔️ | ✔️ | BioC |
