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9

There are multiple ways to do this, and multiple protein interaction databases besides the ones you mentioned, such as BioGRID or IntAct. Interaction databases are different in how interactions are defined, sometimes it can be experimental evidence of interaction, sometimes coexpression, orthology-based predictions, etc. There is no single solution to your ...


7

In matlab this can be done using the SimBiology toolbox. In mathematica this can be done using the SystemModeler. In python there are multiple packages, e.g. (in no particular order) PySB, Tellurium, PySD


6

Have a look at the GSVA package. It allows to convert a matrix with genes x Samples to a pathways x Samples using several methods ssgsea, gage, gsva... Afterwards you can use that matrix as input for differential expression of pathways or classification algorithms or whatever. However it depends on the input of the "pathways" you give it. Make sure that ...


6

using the REST API of togows: one can fetch a json structure of the genes associated to a kegg pathway. eg: http://togows.org/entry/kegg-pathway/hsa00010/genes.json [ { "3101": "HK3; hexokinase 3 [KO:K00844] [EC:2.7.1.1]", "3098": "HK1; hexokinase 1 [KO:K00844] [EC:2.7.1.1]", "3099": "HK2; hexokinase 2 [KO:K00844] [EC:2.7.1.1]", "80201": "...


6

I whipped this little script up using the KEGG API: #!/usr/bin/env python3 import urllib.request import re import sys pathway = 'hsa00010' # glycolysis url = "http://rest.kegg.jp/get/" + pathway with urllib.request.urlopen(url) as f: lines = f.read().decode('utf-8').splitlines() want = 0 for line in lines: fields = line.split() ##...


5

Many interaction databases now work with PSI format files. Most of the main databases can do this and the EBI has set up PSICQUIC View, a very useful page where you can query multiple databases at once. Note that it is very important to limit the results according to the detection method. There is a lot of noise in protein interaction databases. Depending ...


4

This sounds like something that might be amenable to a clustered heatmap plot, or a correlation matrix plot, or something similar. Have you looked at a correlation matrix of the dice coefficient matrix (or maybe just a heatmap plot of that matrix without the correlation matrix)? The corrplot package looks like it might be useful, in particular the hclust / ...


4

I assume that you are talking about the implementation of these methods in the limma package. Otherwise this answer does not apply. I think that your questions can be answered with some simulations where we can test with some "genes" with a known relationship: library("limma") set.seed(123) # Create some genes and samples nGenes <- 40 nSamples <- ...


4

The problem here is that any one GO term with a small number of members is unlikely to include any differentially expressed genes by chance, but if you test a large number of GO terms, some of them definitley will. The solution is to correct the p-values for multiple comparisons. If you do this, then none of those GO terms with a single DE member will be ...


3

This is a a symmetric matrix with the pathways in both axes. So for each pair a similarity index is calculated (usually it takes into account the number of genes in each pathway and the total number of genes in both pathways). There are several similarity indexes available. A typical one is Jaccard or Sørensen-Dice index, usually 0 means no similarity and 1 ...


3

Answers from Biostars: GeneSCF 'prepare_database' module will extract all the pathways with corresponding genes as simple table format in plain text file. Check 'Two step process' (sub-heading) from the provided link (First step will do your job). You can use KEGG REST API for extracting gene names from the pathway. Python request library can be used to ...


3

One way to group similar significant pathways, is to quantify how many genes overlap between the pathways, and then use this in clustering (heatmap). I have made a tool in R which calculates the overlap index between GO terms and subsequently clusters them in a heatmap. Overlap index is the fraction of genes that overlap (number between 0-1). Also Pearson ...


2

If you're happy with a more confident ranking of the most representative gene sets, rather than necessarily cutting down the list, you might try EGSEA. It uses an ensemble approach to give a ranking of the most relevant gene sets, and also produces an interactive HTML output with statistics, heatmaps, pathway maps, summary plots and GO graphs which allows ...


2

You can start by looking at the correlation (if data is non-parametric) of each variable with each variable of each data point you have. One thing that might change is the correlation between variables along the disease. Next you can determine which relationships are constant along the disease progression/clinical trial. Identify also the ones that don't ...


2

As you alluded to in your question, KEGG does provide a curated set of enzyme and metabolite information. This information can be parsed and used to create a network that you can analyze to look at how gene products could be working together. Additional steps could include mapping transcriptome data to the map. See this paper as an example of how this can ...


2

See a project like Plant reactome, where they infer the pathways of all sequenced plant genomes using orthology to well annotated species. http://plantreactome.gramene.org/


2

EMT describe cells' change in their state from being epithelial to the mesenchymal class. So, if a cancer cell line is gaining properties that allow it to move, it might reach the metastasis phase, were a cell can move to any point of the body and start a new tumor there. As in the wikipedia page you linked: EMT has also been shown to occur in wound ...


2

If you run this Rscript using the gene name as an argument, you'll get a file with the pathway written to the directory. #!/usr/bin/Rscript args = commandArgs(trailingOnly=TRUE) library(paxtoolsr) id <- args[1] write.table(graphPc(source=id,kind='neighborhood', format='BINARY_SIF',verbose=TRUE), file=paste(id,'pathway',sep='_'), ...


2

I am not sure if I understood correctly how you classify the drugs. But what you attempt to do is similar to what they do here but in that article they use the drugs targeted to a molecule/pathway to find combination of drugs that are better for the disease (not an alternative drug), see the first image: In that article they mention the pathway cross-talk ...


2

Package Hipathia allows you to compute a value of "activity" for each pathway and each cell, so that the matrix of gene expression is transformed into a matrix of pathway activity. You can use this matrix for further analysis, cell differentiation and clustering. You can also compute functional activity matrices in the same way.


2

If you want to separate each set, you can do something like this dat <- readLines("~/Desktop/scsig.v1.0.metadata.txt") dat <- gsub("\t"," ",dat) # get the indeces of each type standardName <- grep("STANDARD_NAME",dat) organism <- grep("ORGANISM",dat) organSystem <- grep("ORGAN_SYSTEM",dat) pmid <- grep("PMID",dat) # return the values ...


2

The DAVID tool is always interesting, and easy to try: https://david.ncifcrf.gov/gene2gene.jsp You submit a list of genes, it uses several different annotation databases to annotate the gene list, and then it clusters the results and tests for statistical enrichment. You can potentially learn about functional enrichment in your gene list from several ...


2

To measure if two genes are functional similar I developed the BioCor package. It calculates a similarity score between genes by the measuring the amount of pathways shared. However, it doesn't take into account the strength of the connection between two proteins. You could multiply the similarity by a value between 0 and 1 according to the number of ...


1

Production of a chord diagram is described in detail here. You use the R package with either, library(chorddiag) library (circulize) The first level clusters are predefined and the second layer of clustering is what you need to work out. The obvious stat is correlation, secondly (better approach IMO) you might look at covariance, although I don't exactly ...


1

KEGG has a webtool for this, KEGG Mapper. You can just copy-paste your gene ids in there and see how your pathways are colored.


1

Firstly you wanted a webserver to build a tree, ok, I would use PhyML server. The good thing is that with only 22 taxa the calculations are easily doable. What I'm assuming is you can map your rearrangements to produce a single homologous gene alignment for all taxa. So you appear to infer you can break your 20-30kb contig into individual aligments of ...


1

From Wikipedia: In the mathematical field of dynamical systems, an attractor is a set of numerical values toward which a system tends to evolve, for a wide variety of starting conditions of the system. In biology you can think of an attractor as a state towards the system is moving to. A good example is the process of differentiation where stem cells ...


1

The AUCell R package identifies enriched gene sets within cells. It uses the Area Under the Curve (AUC) to calculate whether a gene set is enriched within the expressed genes for each cell.


1

Answer from @llrs, converted from comment: Probably is a question for the maintainer but I would guess that is a variance of the gene in the control dataset. So, if in normal samples the gene has a standard deviation of 0.5 this is the expected too in the tumoral cells. calculate the sd only on the control samples. So if the healthy/control samples have such ...


1

The epithelial-mesenchymal is a process by which cells lose some of the structures specific to their cell type. This is normal during embryonic development or wound healing for the formation of new cells. However, it is very uncommon in healthy adults and is thereby associated with the cancers in which it occurs. EMT allows cancer cells to become more like ...


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