This is an R package for summarizing and visualizing association analysis results of whole-genome/whole-exome sequencing (WGS/WES) studies generated by STAARpipeline.
STAARpipelineSummary is an R package for summarizing and visualizing association analysis results generated by STAARpipeline.
STAARpipeline and STAARpipelineSummary are implemented as a collection of apps. Please see the apps staarpipeline, staarpipelinesummary_varset and staarpipelinesummary_indvar that run on the UK Biobank Research Analysis Platform for more details.
R (recommended version >= 3.5.1)
For optimal computational performance, it is recommended to use an R version configured with the Intel Math Kernel Library (or other fast BLAS/LAPACK libraries). See the instructions on building R with Intel MKL.
STAARpipelineSummary imports R packages Rcpp, STAAR, MultiSTAAR, STAARpipeline, SCANG, dplyr, SeqArray, SeqVarTools, GenomicFeatures, TxDb.Hsapiens.UCSC.hg38.knownGene, GMMAT, GENESIS, Matrix, lattice. These dependencies should be installed before installing STAARpipelineSummary.
library(devtools)
devtools::install_github("xihaoli/STAARpipelineSummary",ref="main")
A docker image for STAARpipelineSummary, including R (version 3.6.1) built with Intel MKL and all STAAR-related packages (STAAR, MultiSTAAR, SCANG, STAARpipeline, STAARpipelineSummary) pre-installed, is located in the Docker Hub. The docker image can be pulled using
docker pull zilinli/staarpipeline:0.9.7
Please see the STAARpipelineSummary user manual for detailed usage of STAARpipelineSummary package. Please see the STAARpipeline tutorial for a detailed example of summarizing and visualizing association analysis results using STAARpipelineSummary.
The whole-genome functional annotation data assembled from a variety of sources and the precomputed annotation principal components are available at the Functional Annotation of Variant - Online Resource (FAVOR) site and FAVOR Essential Database.
The current version is 0.9.7.1 (October 16, 2024).
If you use STAARpipeline and STAARpipelineSummary for your work, please cite:
Zilin Li*, Xihao Li*, Hufeng Zhou, Sheila M. Gaynor, Margaret Sunitha Selvaraj, Theodore Arapoglou, Corbin Quick, Yaowu Liu, Han Chen, Ryan Sun, Rounak Dey, Donna K. Arnett, Paul L. Auer, Lawrence F. Bielak, Joshua C. Bis, Thomas W. Blackwell, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jennifer A. Brody, Brian E. Cade, Matthew P. Conomos, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Paul S. de Vries, Ravindranath Duggirala, Nora Franceschini, Barry I. Freedman, Harald H. H. Göring, Xiuqing Guo, Rita R. Kalyani, Charles Kooperberg, Brian G. Kral, Leslie A. Lange, Bridget M. Lin, Ani Manichaikul, Alisa K. Manning, Lisa W. Martin, Rasika A. Mathias, James B. Meigs, Braxton D. Mitchell, May E. Montasser, Alanna C. Morrison, Take Naseri, Jeffrey R. O’Connell, Nicholette D. Palmer, Patricia A. Peyser, Bruce M. Psaty, Laura M. Raffield, Susan Redline, Alexander P. Reiner, Muagututi’a Sefuiva Reupena, Kenneth M. Rice, Stephen S. Rich, Jennifer A. Smith, Kent D. Taylor, Margaret A. Taub, Ramachandran S. Vasan, Daniel E. Weeks, James G. Wilson, Lisa R. Yanek, Wei Zhao, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Jerome I. Rotter, Cristen J. Willer, Pradeep Natarajan, Gina M. Peloso, & Xihong Lin. (2022). A framework for detecting noncoding rare variant associations of large-scale whole-genome sequencing studies. Nature Methods, 19(12), 1599-1611. PMID: 36303018. PMCID: PMC10008172. DOI: 10.1038/s41592-022-01640-x.
Xihao Li*, Zilin Li*, Hufeng Zhou, Sheila M. Gaynor, Yaowu Liu, Han Chen, Ryan Sun, Rounak Dey, Donna K. Arnett, Stella Aslibekyan, Christie M. Ballantyne, Lawrence F. Bielak, John Blangero, Eric Boerwinkle, Donald W. Bowden, Jai G. Broome, Matthew P. Conomos, Adolfo Correa, L. Adrienne Cupples, Joanne E. Curran, Barry I. Freedman, Xiuqing Guo, George Hindy, Marguerite R. Irvin, Sharon L. R. Kardia, Sekar Kathiresan, Alyna T. Khan, Charles L. Kooperberg, Cathy C. Laurie, X. Shirley Liu, Michael C. Mahaney, Ani W. Manichaikul, Lisa W. Martin, Rasika A. Mathias, Stephen T. McGarvey, Braxton D. Mitchell, May E. Montasser, Jill E. Moore, Alanna C. Morrison, Jeffrey R. O'Connell, Nicholette D. Palmer, Akhil Pampana, Juan M. Peralta, Patricia A. Peyser, Bruce M. Psaty, Susan Redline, Kenneth M. Rice, Stephen S. Rich, Jennifer A. Smith, Hemant K. Tiwari, Michael Y. Tsai, Ramachandran S. Vasan, Fei Fei Wang, Daniel E. Weeks, Zhiping Weng, James G. Wilson, Lisa R. Yanek, NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium, TOPMed Lipids Working Group, Benjamin M. Neale, Shamil R. Sunyaev, Gonçalo R. Abecasis, Jerome I. Rotter, Cristen J. Willer, Gina M. Peloso, Pradeep Natarajan, & Xihong Lin. (2020). Dynamic incorporation of multiple in silico functional annotations empowers rare variant association analysis of large whole-genome sequencing studies at scale. Nature Genetics, 52(9), 969-983. PMID: 32839606. PMCID: PMC7483769. DOI: 10.1038/s41588-020-0676-4.
This software is licensed under GPLv3.
