kdps
Remove related subjects based on kinship matrix as well as subject phenotype
Science Score: 26.0%
This score indicates how likely this project is to be science-related based on various indicators:
-
○CITATION.cff file
-
✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
○DOI references
-
○Academic publication links
-
○Academic email domains
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (11.8%) to scientific vocabulary
Last synced: 9 months ago
·
JSON representation
Repository
Remove related subjects based on kinship matrix as well as subject phenotype
Basic Info
Statistics
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
- Releases: 1
Created about 3 years ago
· Last pushed 11 months ago
Metadata Files
Readme
License
README.Rmd
--- output: github_document --- # KDPS**KDPS** (Kinship Decouple and Phenotype Selection) is an R package designed to resolve cryptic relatedness in genetic studies using a **phenotype-aware** approach. It retains subjects with relevant traits while pruning related individuals based on kinship or identity-by-descent (IBD) scores. ## Features - Prioritizes individuals with key phenotypes (categorical or numeric) - Supports phenotype ranking and composite scoring - Customizable pruning using `fuzziness` parameter - Efficient on biobank-scale datasets like UK Biobank - Compatible with both kinship and phenotype file formats ## Installation You can install the development version of KDPS from GitHub with: ```{r,eval=FALSE} # install.packages("devtools") devtools::install_github("UCSD-Salem-Lab/kdps") ``` ## Tutorial You can view the tutorial of the KDPS function with: ```{r,eval=FALSE} vignette("kdps-intro", package = "kdps") ``` ## Example ```{r,eval=FALSE} library(kdps) phenotype_file = system.file("extdata", "simple_pheno.txt", package = "kdps") kinship_file = system.file("extdata", "simple_kinship.txt", package = "kdps") kdps_results = kdps( phenotype_file = phenotype_file, kinship_file = kinship_file, fuzziness = 0, phenotype_name = "pheno2", prioritize_high = FALSE, prioritize_low = FALSE, phenotype_rank = c("DISEASED1", "DISEASED2", "HEALTHY"), fid_name = "FID", iid_name = "IID", fid1_name = "FID1", iid1_name = "IID1", fid2_name = "FID2", iid2_name = "IID2", kinship_name = "KINSHIP", kinship_threshold = 0.0442, phenotypic_naive = FALSE ) head(kdps_results) ``` ## Documentation - [KDPS Documentations](https://ucsd-salem-lab.github.io/kdps/) - [Getting Started with KDPS](https://ucsd-salem-lab.github.io/kdps/articles/kdps-intro.html) - Function reference: `?kdps` ## Citation If you use KDPS in your research, please cite: > Wanjun Gu, Jiachen Xi, Steven Cao, Rany M. Salem. *Kinship Decouple and Phenotype Selection (KDPS)*. Manuscript in preparation ## License This package is released under the MIT License. See `LICENSE` file for details.
Owner
- Name: UCSD Salem Lab
- Login: UCSD-Salem-Lab
- Kind: organization
- Email: rsalem@ucsd.edu
- Location: United States of America
- Repositories: 1
- Profile: https://github.com/UCSD-Salem-Lab
GitHub Events
Total
- Push event: 14
Last Year
- Push event: 14
Packages
- Total packages: 1
-
Total downloads:
- cran 248 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 1
- Total maintainers: 1
cran.r-project.org: kdps
Kinship Decouple and Phenotype Selection (KDPS)
- Homepage: https://github.com/UCSD-Salem-Lab/kdps
- Documentation: http://cran.r-project.org/web/packages/kdps/kdps.pdf
- License: MIT + file LICENSE
-
Latest release: 1.0.0
published 11 months ago
Rankings
Dependent packages count: 25.8%
Dependent repos count: 31.7%
Average: 47.7%
Downloads: 85.6%
Maintainers (1)
Last synced:
10 months ago
Dependencies
.github/workflows/R-CMD-check.yaml
actions
- actions/checkout v4 composite
- r-lib/actions/check-r-package v2 composite
- r-lib/actions/setup-pandoc v2 composite
- r-lib/actions/setup-r v2 composite
- r-lib/actions/setup-r-dependencies v2 composite
DESCRIPTION
cran
- data.table * imports
- dplyr * imports
- progress * imports
- tibble * imports
- tidyr * imports
- testthat >= 3.0.0 suggests
**KDPS** (Kinship Decouple and Phenotype Selection) is an R package designed to resolve cryptic relatedness in genetic studies using a **phenotype-aware** approach. It retains subjects with relevant traits while pruning related individuals based on kinship or identity-by-descent (IBD) scores.
## Features
- Prioritizes individuals with key phenotypes (categorical or numeric)
- Supports phenotype ranking and composite scoring
- Customizable pruning using `fuzziness` parameter
- Efficient on biobank-scale datasets like UK Biobank
- Compatible with both kinship and phenotype file formats
## Installation
You can install the development version of KDPS from GitHub with:
```{r,eval=FALSE}
# install.packages("devtools")
devtools::install_github("UCSD-Salem-Lab/kdps")
```
## Tutorial
You can view the tutorial of the KDPS function with:
```{r,eval=FALSE}
vignette("kdps-intro", package = "kdps")
```
## Example
```{r,eval=FALSE}
library(kdps)
phenotype_file = system.file("extdata", "simple_pheno.txt", package = "kdps")
kinship_file = system.file("extdata", "simple_kinship.txt", package = "kdps")
kdps_results = kdps(
phenotype_file = phenotype_file,
kinship_file = kinship_file,
fuzziness = 0,
phenotype_name = "pheno2",
prioritize_high = FALSE,
prioritize_low = FALSE,
phenotype_rank = c("DISEASED1", "DISEASED2", "HEALTHY"),
fid_name = "FID",
iid_name = "IID",
fid1_name = "FID1",
iid1_name = "IID1",
fid2_name = "FID2",
iid2_name = "IID2",
kinship_name = "KINSHIP",
kinship_threshold = 0.0442,
phenotypic_naive = FALSE
)
head(kdps_results)
```
## Documentation
- [KDPS Documentations](https://ucsd-salem-lab.github.io/kdps/)
- [Getting Started with KDPS](https://ucsd-salem-lab.github.io/kdps/articles/kdps-intro.html)
- Function reference: `?kdps`
## Citation
If you use KDPS in your research, please cite:
> Wanjun Gu, Jiachen Xi, Steven Cao, Rany M. Salem. *Kinship Decouple and Phenotype Selection (KDPS)*. Manuscript in preparation
## License
This package is released under the MIT License. See `LICENSE` file for details.