https://github.com/cbg-ethz/wwdpcr
Science Score: 26.0%
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Low similarity (10.2%) to scientific vocabulary
Last synced: 10 months ago
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Basic Info
- Host: GitHub
- Owner: cbg-ethz
- License: gpl-3.0
- Language: R
- Default Branch: master
- Size: 982 KB
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- Stars: 0
- Watchers: 3
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Created over 4 years ago
· Last pushed over 4 years ago
Metadata Files
Readme
Changelog
License
README.Rmd
---
output: github_document
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# WWdPCR
WWdPCR is a package to analyse dPCR duplex assays. The main use cases are: computing confidence intervals for the variant proprotion in a dPCR duplex assay, fitting a logistic growth model to the variant proportion with time series duplex assay dPCR data (also including background prevalence) to estimate growth rates. The logistic growth functions (that include background prevalence) can also be applied to binomial data.
If you use WWdPCR, please cite Caduff et al. 2021 [(https://doi.org/10.1101/2021.08.22.21262024)](https://doi.org/10.1101/2021.08.22.21262024).
## Installation
WWdPCR is not yet on CRAN.
You can install the development version from [GitHub](https://github.com/) with:
``` r
# install.packages("devtools")
devtools::install_github("cbg-ethz/WWdPCR")
```
## Examples
To see examples on how to use WWdPCR, compile the vignettes on installing:
``` r
# install.packages("devtools")
# install.packages("rmarkdown")
devtools::install_github("cbg-ethz/WWdPCR", build_vignettes=TRUE)
```
And then run the vignettes:
``` r
vignette("fitting_dPCR_data")
vignette("fitting_binomial_data")
vignette("example_confints")
```
Owner
- Name: Computational Biology Group (CBG)
- Login: cbg-ethz
- Kind: organization
- Location: Basel, Switzerland
- Website: https://www.bsse.ethz.ch/cbg
- Twitter: cbg_ethz
- Repositories: 91
- Profile: https://github.com/cbg-ethz
Beerenwinkel Lab at ETH Zurich
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