diff --git a/README.md b/README.md index 5cceb56..caf39d7 100755 --- a/README.md +++ b/README.md @@ -1,73 +1,74 @@ -# NetBID 2.0 -NetBID (**Net**work-based **B**ayesian **I**nference of **D**rivers) is a data-driven system biology pipeline and toolkit for finding drivers from transcriptomics, proteomics and phosphoproteomics data, where the drivers can be either transcription factors (**TF**) or signaling factors (**SIG**). - -NetBID 2.0 is an upgraded version of [NetBID 1.0](https://github.com/jyyulab/NetBID/releases/tag/1.0.0) that has been published in [Nature]((https://www.nature.com/articles/s41586-018-0177-0)) in 2018. NetBID 2.0 inherites all the main functions from NetBID 1.0, and provides many more functions and pipelines to perform advanced end-to-end analyses. - -# Installation - -Require ```R >= 3.6.0```. Other dependencies can be found in table [https://jyyulab.github.io/NetBID/docs/pre_request](https://jyyulab.github.io/NetBID/docs/pre_request). - -Installation instructions are in [Installation section](https://jyyulab.github.io/NetBID/) of the documentation. - - -# Documentation & Guided Analyses - -Instructions, documentation, and tutorials can be found at: - -+ [https://jyyulab.github.io/NetBID/](https://jyyulab.github.io/NetBID/) - -A PDF manual [NetBID_manual.pdf](https://github.com/jyyulab/NetBID/blob/master/NetBID_manual.pdf) can be found in the repository. - - -# Docker -We publish our lastest docker builds on DockerHub. You can pull the docker image by running the following command: - -```$ docker pull jyyulab/netbid2:2.0.1``` - -The docker image has build-in RStudio server that can be launched by - -```$ docker run -it netbid2:2.0.1 rserver --server-daemonize 0``` - -For interactive command line analysis, use the following command: - -```$ docker run -it netbid2:2.0.1``` - -# Demos -Demo scripts can be found in [demo_scripts](https://github.com/jyyulab/NetBID/tree/master/demo_scripts) directory. - -### Demo script for network generation -Summary of steps in pipeline_network_demo1.R: - -+ Step1: load in gene expression datasets for network construction (exp-load) -+ Step2: normalization for the exp dataset (exp-QC) -+ Step3: check sample cluster info, optional (exp-cluster) -+ Step4: prepare [SJARACNE](https://github.com/jyyulab/SJARACNe) (sjaracne-prep) - -### Demo script for network-based analysis -Summary of steps in pipeline_analysis_demo1.R: - -+ Step1: load in gene expression datasets for analysis (exp-load,exp-cluster,exp-QC) -+ Step2: activity calculation (act-prep,act-get) -+ Step3: get DE/DA (act-DA) -+ Step4: generate master table (ms-tab) - -### Demo script for the following analyses, mainly focus on visualization -Questions that the analyses in analysis_and_plot_demo1.R help to answer: - -+ Part I: More details about the top drivers - 1. How to get the top drivers with significant differential activity (DA) in the comparison between G4 vs. other subtypes? - 2. How to interpret the significance of top DA drivers? - 3. What is the expression/activity pattern of these top DA drivers across sample subtypes? - 4. What are the biological functions of these top DA drivers? - 5. What are the biological functions of the target genes of these top DA drivers? - -+ Part II: More details about the selected driver - 1. How to interpret the significance of the selected driver? - 2. How to visualize the network structure of the selected driver? - 3. What is the expression/activity of this selected driver across subtypes of sample? - 4. What are the functions of the target genes of this selected driver? - -+ Part III: Other analyses - 1. What are the activities of the curated gene sets across all samples? - 2. How to find drivers share significantly overlapped target genes? - +# NetBID 2.0 +NetBID (**Net**work-based **B**ayesian **I**nference of **D**rivers) is a data-driven system biology pipeline and toolkit for finding drivers from transcriptomics, proteomics and phosphoproteomics data, where the drivers can be either transcription factors (**TF**) or signaling factors (**SIG**). + +NetBID 2.0 is an upgraded version of [NetBID 1.0](https://github.com/jyyulab/NetBID/releases/tag/1.0.0) that has been published in [Nature]((https://www.nature.com/articles/s41586-018-0177-0)) in 2018. NetBID 2.0 inherites all the main functions from NetBID 1.0, and provides many more functions and pipelines to perform advanced end-to-end analyses. + +# Installation + +Require ```R >= 3.6.0```. Other dependencies can be found in table [https://jyyulab.github.io/NetBID/docs/pre_request](https://jyyulab.github.io/NetBID/docs/pre_request). + +Installation instructions are in [Installation section](https://jyyulab.github.io/NetBID/) of the documentation. + + +# Documentation & Guided Analyses + +Instructions, documentation, and tutorials can be found at: + ++ [https://jyyulab.github.io/NetBID/](https://jyyulab.github.io/NetBID/) + +A PDF manual [NetBID_manual.pdf](https://github.com/jyyulab/NetBID/blob/master/NetBID_manual.pdf) can be found in the repository. + + +# Docker +We publish our lastest docker builds on DockerHub. You can pull the docker image by running the following command: + +```$ docker pull adamdingliang/netbid2:2.0.1``` + +The docker image has build-in RStudio server that can be launched by + +```$ docker run -it netbid2:2.0.1 rserver --server-daemonize 0``` + +For interactive command line analysis, use the following command: + +```$ docker run -it netbid2:2.0.1``` + +# Demos +Demo scripts can be found in [demo_scripts](https://github.com/jyyulab/NetBID/tree/master/demo_scripts) directory. + +### Demo script for network generation +Summary of steps in pipeline_network_demo1.R: + ++ Step1: load in gene expression datasets for network construction (exp-load) ++ Step2: normalization for the exp dataset (exp-QC) ++ Step3: check sample cluster info, optional (exp-cluster) ++ Step4: prepare [SJARACNE](https://github.com/jyyulab/SJARACNe) (sjaracne-prep) + +### Demo script for network-based analysis +Summary of steps in pipeline_analysis_demo1.R: + ++ Step1: load in gene expression datasets for analysis (exp-load,exp-cluster,exp-QC) ++ Step2: activity calculation (act-prep,act-get) ++ Step3: get DE/DA (act-DA) ++ Step4: generate master table (ms-tab) + +### Demo script for the following analyses, mainly focus on visualization +Questions that the analyses in analysis_and_plot_demo1.R help to answer: + ++ Part I: More details about the top drivers + 1. How to get the top drivers with significant differential activity (DA) in the comparison between G4 vs. other subtypes? + 2. How to interpret the significance of top DA drivers? + 3. What is the expression/activity pattern of these top DA drivers across sample subtypes? + 4. What are the biological functions of these top DA drivers? + 5. What are the biological functions of the target genes of these top DA drivers? + ++ Part II: More details about the selected driver + 1. How to interpret the significance of the selected driver? + 2. How to visualize the network structure of the selected driver? + 3. What is the expression/activity of this selected driver across subtypes of sample? + 4. What are the functions of the target genes of this selected driver? + ++ Part III: Other analyses + 1. What are the activities of the curated gene sets across all samples? + 2. How to find drivers share significantly overlapped target genes? + 3. How to draw oncoPrint figures for samples with different mutation information? +