Below you will find pages that utilize the taxonomy term “sif”
Blog
Network Visualization Using Cytoscape
Cytoscape is a nice tool to visualize network for better understanding and delivery. I used it for in silico data network visualization and the result was really pretty. Now, I have networks constructed using experimental data from HPN-DREAM Challenge.
In this post, I want to demonstrate how to visualize a network with scores. I’m using Cytoscape 2.8 on Ubuntu 12.
First, the network will be read from a SIF file which is default format of Cytoscape for networks.
Blog
Determining Edges More Progress on Network Inference
Lately, I have been writing an R script to infer network using in silico data. Last version of the script was reading MIDAS file and plotting expression profiles. I have modified it and now it reads MIDAS file, does some analyses and prints causal relations to a file. This file is a SIF file as required.
This dataset is generated with 20 antibodies but only 3 of them are perturbed. Also, for one, stimulus is missing.
Blog
Playing around with CellNOptR Tool and MIDAS File
With CellNOptR, we will try to construct network models for the challenge. For this, the tool needs two inputs. First one is a special data object called CNOlist that stores vectors and matrices of data. Second one is a .SIF file that contains prior knowledge network which can be obtained from pathway database and analysis tools.
CNOlist contains following fields: namesSignals, namesCues, namesStimuli and namesInhibitors, which are vectors storing the names of measurements.
Blog
Progress on Network Inference Sub-Challenge
This sub-challenge has several requirements:
Directed and causal edges on the models (32 models - 4 cell lines × 8 stimuli) Edges should be scored (normalizing to range between 0 and 1) that will show confidence Nodes will be phosphoproteins from the data Prior knowledge network (that can be constructed using pathway databases) is allowed to be used (actually this is a must for some network inference tools) First thing was to look for existing tools.
Blog
Network Inference DREAM Breast Cancer Challenge
The inference of causal edges are described as the change on a node seen after the intervention of another node. If the curves obtained over time overlap (under intervention or no intervention), then there is no relation. Otherwise, we can draw an edge between those nodes and according to the level, up or down, the edge will be activating or inhibiting. These causal edges are context-specific so in different cell line data, we may have different relations.