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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "90%"
)
```
::: {style="margin-top: 5px;"}
<img src="man/figures/hex.jpg" align="right" width="150"/>
:::
## [`sceptre`]{style="font-size:60px;"}
An R package for single-cell CRISPR screen data analysis, emphasizing statistical rigor, computational efficiency, and ease of use.
<!-- badges: start -->
[](https://github.com/Katsevich-Lab/sceptre/actions)
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## Release of `sceptre` v0.9.2
We are excited to announce the release of `sceptre` v0.9.2, a substantial update to the package. This milestone includes the following developments:
- A reimagined user experience based on a modular, object-oriented workflow.
- Further improvements in speed and memory efficiency.
- A unified interface for low- and high-MOI analyses.
- A suite of plotting functions facilitating visualization of each step in the pipeline.
- Expanded support for gRNA assignment and quality control.
- Interoperability with output from 10X Cell Ranger and Parse Biosciences CRISPR Detect.
- An [e-book](https://timothy-barry.github.io/sceptre-book/) guiding users through the entire process of analyzing their data using `sceptre`.
`sceptre` v0.9.2 facilitates an entire analysis pipeline for single-cell CRISPR screens, starting from UMI count data obtained from tools like 10X Cell Ranger.
```{=html}
<!--
## About `sceptre`
Single-cell CRISPR screens (e.g., Perturb-seq, TAP-seq) combine CRISPR and single-cell sequencing to survey the effects of genetic perturbations on individual cells. Despite their promise, single-cell CRISPR screens present considerable statistical and computational challenges. `sceptre` is an R package for single-cell CRISPR screen data analysis, emphasizing statistical rigor, computational efficiency, and ease of use.
### Key features
- Import data from 10X CellRanger or a set of R matrices
- Assign gRNAs to cells using one of three principled methods
- Perform extensive quality control
- Run gRNA-to-gene differential expression analyses
- Verify false discovery rate control using negative control gRNAs
- Verify adequate power using positive control gRNAs
- Visualize each step in the pipeline by rendering an informative plot
### Compatible experimental designs
- low multiplicity-of-infection and high multiplicity-of-infection
- Gene-targeting and noncoding-regulatory-element-targeting
- CRISPRko, CRISPRi, CRISPRa, CRISPR base editing, and CRISPR prime editing
- Gene and protein expression readout
## Get started
The fastest way to get started with `sceptre` is to work through the [Get Started vignette](articles/sceptre.html) (30 minutes). Users also can read the `sceptre` [manual](https://timothy-barry.github.io/sceptre-book/).
-->
```
## Featured publications
- [Morris et al., 2023](https://www.science.org/doi/10.1126/science.adh7699). "Discovery of target genes and pathways...". *Science*.
- [Barry et al., 2021](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-021-02545-2). "SCEPTRE improves calibration and sensitivity...". *Genome Biology*.
## Bug reports, feature requests, and software questions
For bug reports, please open a [GitHub issue](https://github.com/Katsevich-Lab/sceptre/issues). For feature requests, please start a discussion under [feature requests](https://github.com/Katsevich-Lab/sceptre/discussions/categories/feature-requests). For questions about `sceptre` functionality, documentation, or how to apply it to your data, please start a discussion under [Q&A](https://github.com/Katsevich-Lab/sceptre/discussions/categories/q-a).