Today, I submitted in silico and experimental data network inference results on Synapse for the next leaderboard on this Wednesday.
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.
As I can plot expression curves for in silico data. I moved on experimental data which is more complex and larger. This data is the result of RPPA experiments on different breast cancer cell lines and it includes protein abundance measurements for about 45 phophoproteins. These phosphoproteins are treated with different inhibitors and stimuli and by comparing their expressions, I will try to infer relations between them.
As I mentioned in my previous post, experimental data from the challenge has missing data values that create problems during analyses. To solve it, first thing I did was to optimize data, which includes detecting missing conditions and putting NAs for data values and sorting them if necessary.
As I almost finished with in silico data, I moved on to analyses of experimental data using the same script. But since the characteristics of data is somehow different, before inferring network, I need to modify the script to be able to read experimental data files.
I’m almost done with the analysis of in silico data, although I need to decide if I need further analysis with the inhibiting parent nodes in the network. Last, I couldn’t filter out duplicate edges, which were scored differently. Now, with some improvements in the script, low scores duplicates are filtered and there is a better final list of edges which is ready to be visualized.
I have programmed with Perl, Python, and PHP before, and string manipulation was more direct and easier in them than in R. But still there are useful functions for string manipulation in R. I’m not an expert in R but I’ve been dealing with it for a while and I’ve learned some good functions for this purpose.
I have improved network inference part of the script slightly by changing the way of comparing intervention (presence of inhibitor and stimulus) and no intervention (presence of stimulus) data from in silico part.
Yesterday, I managed to infer a network for some part of in silico data from the challenge. Since the challenge also asks for scoring the edges in networks, I developed the script further and add a function for that.
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.