Update README.md

README.md

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 <details>
 <summary>DRAGON</summary>
-<b>DRAGON</b> (Determining Regulatory Associations using Graphical models on Omics Networks) <a href="https://arxiv.org/abs/2104.01690">Shutta et al.</a> is a method for estimating multiomic Gaussian graphical models (GGMs, also known as partial correlation networks) that incorporate two different omics data types. DRAGON builds off of the popular covariance shrinkage method of Ledoit and Wolf with an optimization approach that explicitly accounts for the differences in two separate omics "layers" in the shrinkage estimator. The resulting sparse covariance matrix is then inverted to obtain a precision matrix estimate and a corresponding GGM.  Although GGMs assume normally distributed data, DRAGON can be used on any type of continuous data by transforming data to approximate normality prior to network estimation. Currently, DRAGON can be applied to estimate networks with two different types of omics data. Investigators interested in applying DRAGON to more than two types of omics data can consider estimating pairwise networks and "chaining" them together.
+<b>DRAGON</b> (Determining Regulatory Associations using Graphical models on Omics Networks) <a href="https://arxiv.org/abs/2104.01690">Shutta, Weighill et al.</a> is a method for estimating multiomic Gaussian graphical models (GGMs, also known as partial correlation networks) that incorporate two different omics data types. DRAGON builds off of the popular covariance shrinkage method of Ledoit and Wolf with an optimization approach that explicitly accounts for the differences in two separate omics "layers" in the shrinkage estimator. The resulting sparse covariance matrix is then inverted to obtain a precision matrix estimate and a corresponding GGM.  Although GGMs assume normally distributed data, DRAGON can be used on any type of continuous data by transforming data to approximate normality prior to network estimation. Currently, DRAGON can be applied to estimate networks with two different types of omics data. Investigators interested in applying DRAGON to more than two types of omics data can consider estimating pairwise networks and "chaining" them together.
 </details>
 
 <details>
 <summary>TIGER</summary>
 <b>TIGER</b> (Transcription Inference using Gene Expression and Regulatory Data) <a href="https://www.biorxiv.org/content/10.1101/2022.12.12.520141v1">Chen et al.</a> is a Bayesian matrix factorization framework that combines prior TF binding knowledge, such as from the DoRothEA database, with gene expression data from experiments. It estimates individual-level TF activities (TFA) and context-specific gene regulatory networks (GRN). Unlike other methods, TIGER can flexibly model activation and inhibition events, prioritize essential edges, shrink irrelevant edges towards zero using a sparse Bayesian prior, and simultaneously estimate TF activity levels and the underlying regulatory network. It is important to note that TIGER works most appropriately with large sample size datasets like TCGA to include a wide range of TFs due to its lower rank constraint.
 </details>
 
+<details>
+<summary>COBRA</summary>
+<b>COBRA</b> (Co-expression Batch Reduction Adjustment) Micheletti, Schlauch et al. is method for correction of high-order batch effects such as those that persist in co-expression networks. Batch effects and other covariates are known to induce spurious associations in co-expression networks and confound differential gene expression analyses. These effects are corrected for using various methods prior to downstream analyses such as the inference of co-expression networks and computing differences between them. In differential co-expression analysis, the pairwise joint distribution of genes is considered rather than independently analyzing the distribution of expression levels for each individual gene. Computing co-expression matrices after standard batch correction on gene expression data is not sufficient to account for the possibility of batch-induced changes in the correlation between genes as existing batch correction methods act solely on the marginal distribution of each gene. Consequently, uncorrected, artifactual differential co-expression can skew the correlation structure such that network-based methods that use gene co-expression can produce false, nonbiological associations even using data corrected using standard batch correction. Co-expression Batch Reduction Adjustment (COBRA) addresses this question by computing a batch-corrected gene co-expression matrix based on estimating a conditional covariance matrix. COBRA estimates a reduced set of parameters that express the co-expression matrix as a function of the sample covariates and can be used to control for continuous and categorical covariates. The method is computationally fast and makes use of the inherently modular structure of genomic data to estimate accurate gene regulatory associations and enable functional analysis for high-dimensional genomic data.
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 * Source protein-protein interaction network from [STRINGdb](https://string-db.org/) based on a list of protein of interest.
 
 * Plot one PANDA network in [Cytoscape](https://cytoscape.org/).