https://github.com/carmonalab/scooter
Science Score: 26.0%
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○CITATION.cff file
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✓codemeta.json file
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○.zenodo.json file
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✓DOI references
Found 2 DOI reference(s) in README -
○Academic publication links
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○Institutional organization owner
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○Scientific vocabulary similarity
Low similarity (7.8%) to scientific vocabulary
Repository
Basic Info
- Host: GitHub
- Owner: carmonalab
- License: gpl-3.0
- Language: R
- Default Branch: master
- Size: 761 KB
Statistics
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 7
- Releases: 0
Metadata Files
README.md
scooter
Exploratory single-cell data analysis at the sample level
scxplor is designed to help you explore your single-cell RNA-seq data in a simple and time-efficient way. It summarizes your cell type annotations by providing tools for compositional data analysis as well as tools for gene expression on the sample and cell type level. - Supervised analysis: visualize your samples with box plots comparing groups or PCA colored by group - Unsupervised analysis: cluster your samples based on their similarity
Why unsupervised analysis? - Quality Control: Identify outliers, biases, and potential technical artifacts. - Dimensionality Reduction: Condense 1000s of dimensions into a few highly interpretable features. - Detection of Biological Variability: reveal important insights into population heterogeneity, developmental trajectories, and responses to stimuli or disease.
Installation
``` r
install.packages("remotes")
remotes::install_github("carmonalab/scooter") ```
Summarize your scRNA-seq data
A list of annotated Seurat objects can be summarized into a list of scoot objects using the scoot function. Compositional cell type distribution and aggregated transcriptomic profile (pseudobulk) are returned for each sample.
``` r obj.list <- SplitObject(obj, split.by = "Sample")
scootobjectlist <- scoot(obj.list)
scootsummary <- mergescootobjects(scootobject_list) ```
scoot object content
The scoot object summarize the cell type annotation and contain the following slots:
- Seurat object metadata (dataframe):
metadata - Cell type composition for each layer of cell type prediction:
composition. Including:- Cell counts
- Frequency
- CLR (Centred log ratio)-transformed counts (useful for downstream analyses such as PCA/Logratio analysis )
- Aggregated profile of predicted cell types:
aggregated_profile. Including:
Owner
- Name: Cancer Systems Immunology Lab
- Login: carmonalab
- Kind: organization
- Location: Lausanne, Switzerland
- Website: https://agora-cancer.ch/laboratory/carmona-lab
- Twitter: carmonation
- Repositories: 16
- Profile: https://github.com/carmonalab
At Ludwig Cancer Research Lausanne and Department of Oncology, University of Lausanne & Swiss Institute of Bioinformatics
GitHub Events
Total
- Watch event: 3
- Push event: 3
Last Year
- Watch event: 3
- Push event: 3
Dependencies
- R >= 4.3.1 depends
- BiocGenerics >= 0.48.1 imports
- BiocParallel >= 1.34.2 imports
- DESeq2 >= 1.40.2 imports
- Hotelling >= 1.0 imports
- MatrixGenerics >= 1.12.3 imports
- ProjecTILs >= 3.3.1 imports
- RColorBrewer >= 1.1 imports
- Seurat >= 5.0.0 imports
- SeuratObject >= 5.0.0 imports
- SignatuR >= 0.1.2 imports
- SummarizedExperiment >= 1.30.2 imports
- biomaRt >= 2.56.1 imports
- caret >= 6.0 imports
- cowplot >= 1.1.1 imports
- data.table >= 1.14.8 imports
- dplyr >= 1.1.4 imports
- factoextra >= 1.0.7 imports
- ggdendro >= 0.1.23 imports
- ggplot2 >= 3.4.4 imports
- ggplotify >= 0.1.2 imports
- ggpubr >= 0.6.0 imports
- ggraph >= 2.1.0 imports
- gridExtra >= 2.3 imports
- igraph >= 2.0.2 imports
- metap >= 1.9 imports
- methods >= 4.3.1 imports
- multtest >= 2.58.0 imports
- parallelly >= 1.36.0 imports
- patchwork >= 1.2.0 imports
- pheatmap >= 1.0.12 imports
- purrr >= 1.0.2 imports
- rrapply >= 1.2.6 imports
- scGate >= 1.6.1 imports
- scran >= 1.30.2 imports
- stringr >= 1.5.1 imports
- tibble >= 3.2.1 imports
- tidyr >= 1.3.1 imports
- knitr * suggests
- rmarkdown * suggests
- testthat >= 3.0.0 suggests