diff --git a/README.md b/README.md index 401a75c..01d3d76 100644 --- a/README.md +++ b/README.md @@ -13,17 +13,21 @@ Currently, qtWGCNA (v 1.0) is under its first version development. As showed in After WSL or server deployment, qtWGCNA required miniconda environment to install iterativeWGCNA by running commandline 'conda install -c conda-forge iterativewgcna' (https://anaconda.org/conda-forge/iterativewgcna). More information about how to install Miniconda, please click me. Finally, by uoload the gene expression file (file format info) with 'browse' button and set required parameters, we are able to run iterativeWGCNA at the back-end and wait for the processing to be done! -2. Interface Options +

2. Interface Options

Currently, qtWGCNA is now allowed to accept all arguments of valid for the WGCNA blockwiseModules R function (blockwiseModules help document). More information about the customized parameters, e.g. soft power or network type, please refer to WGCNA parameters. -3. Results and logs - A main text and log result of qtWGCNA was showed as below, which indicates how much connected of all the co-expressed genes in well-divided WGCNA modules. +

3. Results and logs

+A main text and log result of qtWGCNA was showed as below, which indicates how much connected of all the co-expressed genes in well-divided WGCNA modules. ![alt text](https://2021.igem.org/wiki/images/4/40/T--GDSYZX--software-fig3.png) ![alt text](https://2021.igem.org/wiki/images/5/54/T--GDSYZX--software-fig4.png) - A main figure result of qtWGCNA was showed as below, which indicates how many genes are jointly co-expressed. +A main figure result of qtWGCNA was showed as below, which indicates how many genes are jointly co-expressed. ![alt text](https://2021.igem.org/wiki/images/0/0f/T--GDSYZX--software-video-fig2.png) More information about the output files, please refer to iterativeWGCNA output files +

4. Source Code

+ The source code of qtWGCNA can be compiled by Qt creator. + ![alt text](https://2021.igem.org/wiki/images/e/e2/T--GDSYZX--qtcreator.png) + [1] iterativeWGCNA: iterative refinement to improve module detection from WGCNA co-expression networks Emily Greenfest-Allen, Jean-Philippe Cartailler, Mark A. Magnuson, Christian J. Stoeckert Jr. bioRxiv 234062; doi: https://doi.org/10.1101/234062.