data-drivern Network-based Bayesian Inference of Drivers, Version II
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Create two "lazy mode" functions, NetBID.lazyMode.DriverVisualization(),NetBID.lazyMode.DriverEstimation()
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Add warning message for all functions
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Let color code set by user-defined, with two options: use_color,pre_define passed to get.class.color(). The modified functions are: draw.2D(), draw.2D.interactive(), draw.2D.text(), draw.3D(), draw.2D.ellipse(), draw.eset.QC(), draw.pca.keans(), draw.umap.kmeans(), draw.heatmap(), draw.categoryValue(),
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Add package information in calling functions to avoid possible conflict of function names
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Re-write the cal.Activity(), add in Matrix Cross Products, which will accelerate calculation time but it is memory consuming. memory_constrain option could be set.
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Modify the draw.eset.QC(), add correlation plot. Modify draw.network.QC(), add html_info_limit.
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Add draw.2D.interactive(), and add option "2D.interactive" to draw.pca.kmeans(), draw.umap.kmeans()
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Add functions to judge abnormal values.
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modify option name from network --> target_list in generate.masterTable()
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add option geneSymbol_column for SJAracne.prepare()
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rebuild on R 3.6.0
library(devtools)
library(BiocManager)
# set repos, for R version 3.6.0, Bioconductor version 3.9
local({
r <- getOption("repos")
r["CRAN"] <- "https://cran.rstudio.com/"
r["BioCsoft"] <- "https://bioconductor.org/packages/3.9/bioc"
r["BioCann"] <- "https://bioconductor.org/packages/3.9/data/annotation"
r["BioCexp"] <- "https://bioconductor.org/packages/3.9/data/experiment"
options(repos = r)
})
devtools::install_github("jyyulab/NetBID-dev",ref='master',dependencies='Depends')
or download the release version from https://github.com/jyyulab/NetBID-dev/releases/download/0.1.2/NetBID2_0.1.2.tar.gz
pull the repos from github and install locally:
devtools::install(pkg='.',dependencies=TRUE) ## Install the package with dependencies.
devtools::install_deps(pkg = ".", dependencies = TRUE) ## Install package dependencies if needed.
download the directory to your workspace and then run:
devtools::install_local('NetBID2_0.1.2.tar.gz') ##
manual: NetBID2_0.1.2.pdf
tutorial: https://jyyulab.github.io/NetBID-dev/
in demo_scripts/ directory
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 (sjaracne-prep)
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)
analysis_and_plot_demo1.R
Part I: More details about the top drivers
QI.1: How to get the top drivers with significant differential activity (DA) in the comparison between G4 vs. other subtypes ?
QI.2: How to interpret the significance of top DA drivers ?
QI.3: What is the expression/activity pattern of these top DA drivers across sample subtypes?
QI.4: What are the biological functions of these top DA drivers ?
QI.5: What are the biological functions of the target genes of these top DA drivers ?
Part II: More details about the selected driver
QII.1: How to interpret the significance of the selected driver ?
QII.2: How to visualize the network structure of the selected driver ?
QII.3: What is the expression/activity of this selected driver across subtypes of sample ?
QII.4: What are the functions of the target genes of this selected driver ?
Part III: Other analyses NetBID2 can do
QIII.1: What are the activities of the curated gene sets across all samples ?
QIII.2: How to find drivers share significantly overlapped target genes ?
Q&A: How to modify the figure size created by draw. functions ?