CellNet is a network biology-based computational platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations. CellNet is based on the reconstruction of cell type specific gene regulatory networks (GRNs), which we performed using publicly available microarray data on 21 cell and tissue types from both human and mouse samples. Code to run CellNet locally is available for download on this site. You can also analyze your data through our our servers, where you can also explore predicted transcriptional targets of over 1,200 mouse or human transcriptional regulators.
CellNet was written by Patrick Cahan with input from Hu Li, Samantha Morris, and Edroaldo Lummertz da Rocha.
It is provided under the OSI-approved Artistic License (version 2.0).
The first version of CellNet is now available. This software a beta release, meaning that it is a work in progress, and new features will be added in the near future. To report a bugs or to discuss this software (but not the web application, please), go to the CellNet user group.
You can install them packages from within an R session by typing:
> install.packages(c("ggplot2", "gplots", "randomForest", "igraph"))
You will also need to install the affy package from Bioconductor. You can install it from within an R session by typing:
> source("http://bioconductor.org/biocLite.R") > biocLite("affy")
You will also need to install the following Bioconductor annotation packages. Please note that you must download these particular versions in order for CellNet to run properly:
We recommend that you install these packages to directory where they will not be overwritten. For example, we install these in the directory ~/sample_packages/install_dir. After you have downloaded these files, you can install them by typing at the shell:
R CMD INSTALL -l ~/sample_packages/install_dir package name
To install CellNet, download the compressed package, then at the shell type:
R CMD INSTALL -l ~/sample_packages/install_dir cellnetr.0.1.tar.gz
Finally, you need to download CellNet Objects, one per platform, which contain the training data, classifiers, and gene regulatory networks
# Set your library path so that it points to the correct platform and annotation libraries: .libPaths("~/sample_packages/install_dir") # load cellnet library("cellnetr") # load appropriate gene annotation sources # mouse library("org.Mm.eg.db") # if human # library("org.Hs.eg.db") # load platform annotation # mouse4302 library("mouse4302cdf"); library("mouse4302.db"); # mogene10 # library("mogene10sttranscriptcluster.db"); # library("mogene10stv1cdf"); # hgu133plus2 # library("hgu133plus2.db"); # library("hgu133plus2cdf"); # hugene10 # library("hugene10sttranscriptcluster.db"); # library("hugene10stv1cdf"); # set up path the CellNet objects containing the classifiers, GRNs, etc # Edit this so that it points to the path where you saved the CellNet objects path_CN_obj<-"~/CellNet/CN_Objects/"; # change to reflect your platform myPlatform<-"mouse4302" # name of the column in the sample data table that indicates experimental groups or replicates # set to "", or "sample_name" if there are no replicates cName<-"description1" # target tissue or cell type. targetCT<-"hspc"
# Load sample table and fix the data file names # replace 'sampleTab.csv' with your sample table name. See [Step 2 here](http://dev.cellnet.hms.harvard.edu/run/) for a description of the sample table format and an example table. stQuery<-expr_readSampTab("sampleTab.csv"); stQuery<-geo_fixNames(stQuery); # load and normalize expression data expQuery<-Norm_cleanPropRaw(stQuery, "mouse4302"); # load right CellNet object cnObjName<-switch(myPlatform, mouse4302 = paste(path_CN_obj, "cnProc_mouse4302_062414.R",sep=''), mogene10stv1 = paste(path_CN_obj, "cnProc_mogene_062414.R",sep=''), hgu133plus2 = paste(path_CN_obj, "cnProc_Hg1332_062414.R",sep=''), hugene10stv1 = paste(path_CN_obj, "cnProc_Hugene_062414.R",sep='')); cnProc<-utils_loadObject(cnObjName); # Run CellNet tmpAns<-cn_apply(expQuery, stQuery, cnProc, dLevelQuery=cName); # Score transcription factors tfScores<-cn_nis_all(tmpAns, cnProc, targetCT);
# Classification heatmap cn_hmClass(tmpAns);
# Gene regulatory network status of starting cell type (esc) GRN cn_barplot_grnSing(tmpAns, cnProc, "esc", c("esc"), bOrder=NULL, norm=T);
# Gene regulatory network status of target cell type (hspc) GRN cn_barplot_grnSing(tmpAns, cnProc, "hspc", c("hspc"), bOrder=NULL, norm=T);
# Network influence score of HSPC GRN transcriptional regulators. # factor heatmap cn_plotnis(tfScores, limit=15);
# Gene expression in the training data set mp_rainbowPlot(cnProc[['expTrain']],cnProc[['stTrain']],"Meis1", dLevel="description1")
- Adding a cell or tissue type
- Adding a new platform
- Adding a new species
- Reconstructing GRNs de novo
- Identifying cell and tissue specific sub-networks