library(CellNOptR) library(dplyr) data_file <- system.file("extdata/datasets", "MD-TCellPCB2007.csv", package = "artemis") model_file <- system.file("extdata/models", "PKN-TCellPCB2007.sif", package = "artemis") tcell_data <- CNOlist(data_file) tcell_model <- readSIF(model_file) preprocessing(tcell_data, tcell_model, expansion = FALSE) %>% plotModel(tcell_data)
The following is a model that blends a T-cell signaling prior knowledge network and a single cell AML study. The data is not real, but engineered such that it shows the overlap between the model and the data set. Not the pi3K -- Akt edge was added to the original sif representaion of the model so akt could be included.
data_file <- system.file("extdata/datasets", "TCell_fake.csv", package = "artemis") model_file <- system.file("extdata/models", "TCellConstructed.sif", package = "artemis") tcell_data <- CNOlist(data_file) tcell_model <- readSIF(model_file) preprocessing(tcell_data, tcell_model, expansion = FALSE) %>% plotModel(tcell_data)
Now working with the hepatocellular carcinoma cells discussed in Saez-Rodriguez, Lauffenburger, Klamt and Sorger et al's 2009 paper on logic modeling of signaling networks.
data_file <- system.file("extdata/datasets", "MD-ExtLiverPCB1.csv", package = "artemis") model_file <- system.file("extdata/models", "PKN-ExtLiverPCB.sif", package = "artemis") tcell_data <- CNOlist(data_file) tcell_model <- readSIF(model_file) preprocessing(tcell_data, tcell_model, expansion = FALSE) %>% plotModel(tcell_data)
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