:: DESCRIPTION
deNOPA is a novel ATAC-seq analysis toolkit, to predict nucleosome positions.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Xu B, Li X, Gao X, Jia Y, Liu J, Li F, Zhang Z.
DeNOPA: decoding nucleosome positions sensitively with sparse ATAC-seq data.
Brief Bioinform. 2021 Dec 7:bbab469. doi: 10.1093/bib/bbab469. Epub ahead of print. PMID: 34875002.
:: DESCRIPTION
sparse.inv.cov is an R package designed for fitting large sparse covariance selection models.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Harri Kiiveri, Frank de Hoog
Fitting very large sparse Gaussian graphical models
Computational Statistics & Data Analysis Volume 56, Issue 9, September 2012, Pages 2626–2636
:: DESCRIPTION
glmgraph is an R package for variable selection and predictive modeling of structured genomic data
::DEVELOPER
Li Chen <li.chen at emory.edu>
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
glmgraph: An R Package for Variable Selection and Predictive Modeling of Structured Genomic Data.
Chen L, Liu H, Kocher JA, Li H, Chen J.
Bioinformatics. 2015 Aug 26. pii: btv497.
:: DESCRIPTION
metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation:
Nat Methods. 2013 Dec;10(12):1200-2. doi: 10.1038/nmeth.2658. Epub 2013 Sep 29.
Differential abundance analysis for microbial marker-gene surveys.
Paulson JN1, Stine OC, Bravo HC, Pop M.
:: DESCRIPTION
SparseDC is an algorithm, which identifies cell types, traces their changes across conditions and identifies genes which are marker genes for these changes.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Nucleic Acids Res, 46 (3), e14 2018 Feb 16
A Sparse Differential Clustering Algorithm for Tracing Cell Type Changes via Single-Cell RNA-sequencing Data
Martin Barron , Siyuan Zhang , Jun Li
:: DESCRIPTION
GFAsparse implements the Bayesian Group Factor Analysis with element wise prior inducing sparsity.
::DEVELOPER
Probabilistic Machine Learning
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Identification of structural features in chemicals associated with cancer drug response: a systematic data-driven analysis.
Khan SA, Virtanen S, Kallioniemi OP, Wennerberg K, Poso A, Kaski S.
Bioinformatics. 2014 Sep 1;30(17):i497-i504. doi: 10.1093/bioinformatics/btu456.
:: DESCRIPTION
SPONGE provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input.
::DEVELOPER
:: SCREENSHOTS
N/A
:: REQUIREMENTS
:: DOWNLOAD
:: MORE INFORMATION
Citation
Bioinformatics. 2019 Jul 15;35(14):i596-i604. doi: 10.1093/bioinformatics/btz314.
Large-scale inference of competing endogenous RNA networks with sparse partial correlation.
List M, Dehghani Amirabad A, Kostka D, Schulz MH
:: DESCRIPTION
SCCA (Sparse Canonical Correlation Analysis) examines the relationships between two types of variables and provides sparse solutions that include only small subsets of variables of each type by maximizing the correlation between the subsets of variables of different types while performing variable selection. We also present an extension of SCCA – adaptive SCCA. We evaluate their properties using simulated data and illustrate practical use by applying both methods to the study of natural variation in human gene expression.
::DEVELOPER
: SCREENSHOTS
N/A
:: REQUIREMENTS
:: MORE INFORMATION
Citation
Daniela M Witten and Robert J. Tibshirani
Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
Stat Appl Genet Mol Biol. 2009 January 1; 8(1): Article 28.
