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Pull request overview
This PR updates references to scPRINT by adding scPRINT-2 alongside the original scPRINT tool across multiple sections of the README. It consolidates duplicate entries, adds new ones where scPRINT capabilities are relevant, and includes a comprehensive benchmarking reference.
Changes:
- Updated tool naming from "scPRINT" to "scPRINT/scPRINT-2" across multiple sections
- Added new scPRINT/scPRINT-2 entries in sections where previously absent (normalization, multi-assay integration, rare cell detection, cellular interactions, spatial transcriptomics)
- Consolidated duplicate scPRINT entries by removing standalone versions
- Added benchmark reference comparing foundation models
- Minor formatting cleanup (removed empty lines, added missing entries like Dino and Sanity)
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README.md
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| - [BPSC](https://github.com/nghiavtr/BPSC) - [R] - Beta-Poisson model for single-cell RNA-seq data analyses | ||
| - [dsb](https://github.com/niaid/dsb) - [R or Python] - a method for normalizing and denoising protein data from antibody derived tags (ADT). Compatible with CITE-seq, ASAP-seq, TEA-seq, ICICLE-seq, MissionBio etc. Removes ambient and cell to cell technical noise from ADTs see vignettes on [CRAN](https://CRAN.R-project.org/package=dsb). Manuscript open access: [Normalizing and denoising protein expression data from droplet-sed single cell profiling. *Nature Communications* (2022)](https://www.nature.com/articles/s41467-022-29356-8) | ||
| - [MAST](https://github.com/RGLab/MAST) - [R] - Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data | ||
| - [Dino](https://github.com/JBrownBiostat/Dino) - [R] - normalizes single-cell RNA-seq data by constructing a flexible negative-binomial mixture model of gene expression and sampling from the posterior distribution of expected expression conditional on observed sequencing depth. [Normalization by distributional resampling of high throughput single-cell RNA-sequencing data. *Bioinformatics* (2021)](https://doi.org/10.1093/bioinformatics/btab450) |
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The entry for Dino has an extra space before the opening bracket. It should start with |- not |- (two spaces).
| - [MAST](https://github.com/RGLab/MAST) - [R] - Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data | ||
| - [Dino](https://github.com/JBrownBiostat/Dino) - [R] - normalizes single-cell RNA-seq data by constructing a flexible negative-binomial mixture model of gene expression and sampling from the posterior distribution of expected expression conditional on observed sequencing depth. [Normalization by distributional resampling of high throughput single-cell RNA-sequencing data. *Bioinformatics* (2021)](https://doi.org/10.1093/bioinformatics/btab450) | ||
| - [MAST](https://github.com/RGLab/MAST) - [R] - Model-based Analysis of Single-cell Transcriptomics (MAST) fits a two-part, generalized linear models that are specially adapted for bimodal and/or zero-inflated single cell gene expression data. | ||
| - [Sanity](https://github.com/jmbreda/Sanity) - [C] - (SAmpling-Noise-corrected Inference of Transcription ActivitY) is a Bayesian procedure that infers the log expression levels (log transcription quotients) of genes by filtering out Poisson noise from UMI count matrices. It estimates expression values and error bars directly without tunable parameters. [Bayesian inference of gene expression states from single-cell RNA-seq data. *Nature Biotechnology* (2021](https://doi.org/10.1038/s41587-021-00875-x) |
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The closing parenthesis is missing in the paper reference. It should be 'Nature Biotechnology (2021)' not 'Nature Biotechnology (2021'.
README.md
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| - [Connectome](https://github.com/msraredon/Connectome) - [R] - Software package that facilitates calculation and visualization of cell-cell signaling network topologies in single-cell RNA-seq data. Supports analysis of ligand-receptor interactions, differential connectomics between tissue systems, and interactive exploration of cellular communication patterns. [Paper](https://www.nature.com/articles/s41598-022-07959-x) | ||
| - [GEARS](https://github.com/snap-stanford/GEARS) - [Python] - Graph-enhanced gene activation and repression simulator that predicts transcriptional responses to both single and multigene perturbations. Integrates deep learning with knowledge graphs of gene-gene relationships to predict outcomes of novel gene perturbations not seen experimentally. Shows high precision in predicting genetic interaction subtypes. [Paper](https://www.nature.com/articles/s41587-023-01905-6) | ||
| - [LIANA](https://github.com/saezlab/liana/) - [R, python] - LIANA enables the use of any combination of ligand-receptor methods and resources, and their consensus. [Paper](https://www.nature.com/articles/s41467-022-30755-0) | ||
| - [scPRINT/scPRINT-2](https://github.com/cantinilab/scPRINT) - [Python] - next generation single-cell foundation models which supports dozens of zeroshot abilities across species, tissues, diseases and modalities. It can be fine-tuned and outperform other foundation and task-specific models in multiple tasks. |
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The entry appears to be placed in the 'Cellular interactions/communication' section but describes general capabilities rather than specific cell-cell communication functionality. This placement may be misleading - consider moving to a more general section or rephrasing to focus on communication-specific capabilities.
Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com>
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