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README.md

=============================== Omics Integrator

Build Status

Omics Integrator is a package designed to integrate proteomic data, gene expression data and/or epigenetic data using a protein-protein interaction network. It is comprised of two modules, Garnet and Forest.

Contact: Amanda Kedaigle [mandyjoy@mit.edu]

Copyright (c) 2015 Massachusetts Institute of Technology All rights reserved.

Reference:

Network-Based Interpretation of Diverse High-Throughput Datasets through the Omics Integrator Software Package Tuncbag N*, Gosline SJC*, Kedaigle A, Soltis AR, Gitter A, Fraenkel E. PLoS Comput Biol 12(4): e1004879. doi:10.1371/journal.pcbi.1004879.

For a step-by-step protocol for running this software: Discovering altered regulation and signaling through network-based integration of transcriptomic, epigenomic and proteomic tumor data Kedaigle A, and Fraenkel E. Cancer Systems Biology: Methods in Molecular Biology, 2018.

System Requirements:

  1. Python 2.6 or 2.7 (3.x version currently untested) and the dependencies below. We recommend that users without an existing Python environment install Anaconda (https://www.continuum.io/downloads) to obtain Python 2.7 and the following required packages:
  1. msgsteiner package (version 1.3): code, license

  2. Boost C++ library: http://www.boost.org

  3. Cytoscape for viewing results graphically (tested on versions 2.8-3.2): http://www.cytoscape.org

Features

  • Maps gene expression data to transcription factors using chromatin accessibility data

  • Identifies proteins in the same pathway as hits using protein interaction network

  • Integrates numerous high throughput data types to determine testable biological hypotheses

Installation:

Omics Integrator is a collection of Python scripts and data files so can be easily installed on any system. Steps 1 through 4 are only required for Forest, and you may skip to step 5 if you will only be running Garnet.

  1. Boost is pre-installed on many Linux distributions. If your operating system does not include Boost, follow the Boost getting started guide for instructions on how to download the library and extract files from the archive. To use the Homebrew package manager for Mac simply type brew install boost to install the library.
  2. Download msgsteiner-1.3.tgz from http://staff.polito.it/alfredo.braunstein/code/msgsteiner-1.3.tgz (license)
  3. Unpack files from the archive: tar -xvf msgsteiner-1.3.tgz
  4. Enter the msgsteiner-1.3 subdirectory and run make
  • See this advice on compiling the C++ code if you encounter problems and this advice regarding compilation issues on OS X.
  • Make a note of the path to the compiled msgsteiner file that was created, which you will use when running Forest.
  • In Linux, use readlink -f msgsteiner in the msgsteiner-1.3 subdirectory to obtain the path.
  1. Download the Omics Integrator package: OmicsIntegrator-0.3.1.tar.gz
  2. Unpack files from the archive: tar -xvzf OmicsIntegrator-0.3.1.tar.gz
  3. Make sure you have all the requirements using the pip tool by entering the directory and typing: pip install -r requirements.txt
  • Some users have reported errors when using this command to install matplotlib. To fix, install matplotlib independently (http://matplotlib.org) or use Anaconda as indicated above.

Now Omics Integrator is installed on your computer and can be used to analyze your data.

Examples

We provide many scripts and files to showcase the various capabilities of Omics Integrator. To run this:

  1. Download the example files
  2. Unpack by typing tar -xvzf OmicsIntegratorExamples.tar.gz in the dist directory.
  3. Move the unpacked files into the example directory.

For specific details about the examples, check out the README file in the example directory.

Running garnet.py

Garnet is a script that runs a series of smaller scripts to map epigenetic data to genes and then scan the genome to determine the likelihood of a transcription factor binding the genome near that gene.

Usage: garnet.py [configfilename]

  -s SEED, --seed=SEED  An integer seed for the pseudo-random number
                        generators. If you want to reproduce exact results,
                        supply the same seed. Default = None.


Options:
  -h, --help            show this help message and exit
  --outdir=OUTDIR       Name of directory to place garnet output. DEFAULT:none
  --utilpath=ADDPATH    Destination of chipsequtil library, Default=../src

Unlike Forest, the Garnet configuration file is a positional argument and must not be preceded with --conf=. The configuration file should take the following format:

garnet input

[chromatinData]
#these files contain epigenetically interesting regions
bedfile = bedfilecontainingregions.bed
fastafile = fastafilemappedusinggalaxytools.fasta
#these two files are provided in the package
genefile = ../../data/ucsc_hg19_knownGenes.txt
xreffile = ../../data/ucsc_hg19_kgXref.txt
#distance to look from transcription start site
windowsize = 2000

[motifData]
#motif matrices to be used, data provided with the package
tamo_file = ../../data/matrix_files/vertebrates_clustered_motifs.tamo
#settings for scanning
genome = hg19
numthreads = 4
doNetwork = False
tfDelimiter = .

[expressionData]
expressionFile = tabDelimitedExpressionData.txt
pvalThresh = 0.01
qvalThresh =

[regression]
#for generating and saving regression plots
savePlot=False

Chromatin Data

Many BED-formatted (bedfile) and FASTA-formatted (fastafile) files are included in the examples/ directory. bedfile can also be output from MACS (with a .xls extension) or GPS/GEM (with a .txt extension). To use your own epigenetic data, convert to BED and upload the BED-file to http://usegalaxy.org and select Fetch Alignments/Sequences from the left menu to click on Extract Genomic DNA. This will produce a FASTA-formatted file that will work with garnet. We have provided gene (genefile) and xref (xreffile) annotations for both hg19 and mm9 - these files can be downloaded from http://genome.ucsc.edu/cgi-bin/hgTables if needed. The windowsize parameter determines the maximum distance from a transcription start site to consider an epigenetic event associated. 2kb is a very conservative metric.

motifData

We provide motif data in the proper TAMO format, the user just needs to enter the genome used. The default numthreads is 4, but the user can alter this depending on the processing power of their machine. doNetwork will create a NetworkX object mapping transcription factors to genes, required input for the SAMNet algorithm. tfDelimiter is an internal parameter to tell Garnet how to handle cases when many transcription factors map to the sam binding motif.

expressionData

If the user has expression data to evaluate, provide a tab-delimited file under expressionFile. File should have two columns, one containing the name of the gene and the second containing the log fold change of that gene in a particular condition. We recommend only including those genes whose change in expression is statistically significant. P-value (pvalThresh) or Q-value (qvalThresh) thresholds will be used to select only those transcription factors whose correlation with expression falls below the provided threshold.

regression

Linear regression plots are placed in a subdirectory named regression_plots if savePlot=True in the configuration file.

Garnet output

Garnet produces a number of intermediate files that enable you to better interpret your data or re-run a sub-script that may have failed. All files are placed in the directory provided by the --outdir option of the garnet script.

  • events_to_genes.fsa: This file contains the regions of the fastafile provided in the configuration file that are within the specified distance to a transcription start site.

  • events_to_genes.xls: This file contains each region, the epigenetic activity in that region, and the relationship of that region to the closest gene.

  • events_to_genes_with_motifs.txt: This contains the raw transcription factor scoring data for each region in the fasta file.

  • events_to_genes_with_motifs.tgm: This contains the transcription factor binding matrix scoring data mapped to the closest gene.

  • events_To_genes_with_motifs_tfids.txt: Names of transcription factors (or columns) of the matrix.

  • events_to_genes_with_motifs_geneids.txt: Names of genes (or rows) of the matrix.

  • events_to_genes_with_motifs.pkl: A Pickle-compressed Python File containing a dictionary data structure that contains files 4-6 (under the keys tgm,tfs, and genes) respectively as well as a delim key that describes what delimiter was used to separate out TFs in the case where there are multiple TFs in the same family.

  • events_to_genes_with_motifsregression_results.tsv: Results from linear regression.

  • events_to_genes_with_motifsregression_results_FOREST_INPUT.tsv: Only those regression results that fall under the p-value or q-value significance threshold provided in the configuration file, e.g. p=0.05, are included. This file can be used as input to Forest, and the prizes are -log2(pval) or -log2(qval).

  • regression_plots: An optional subdirectory that contains plots visualizing the transcription factor linear regression tests.

Running forest.py

Forest requires the compiled msgsteiner package.

Usage: forest.py [options]

Find multiple pathways within an interactome that are altered in a particular
condition using the Prize Collecting Steiner Forest problem

Options:
  -h, --help            show this help message and exit
  -p PRIZEFILE, --prize=PRIZEFILE
                        (Required) Path to the text file containing the
                        prizes. Should be a tab delimited file with lines:
                        "ProteinName PrizeValue"
  -e EDGEFILE, --edge=EDGEFILE
                        (Required) Path to the text file containing the
                        interactome edges. Should be a tab delimited file with
                        3 or 4 columns: "ProteinA        ProteinB
                        Weight(between 0 and 1) Directionality(U or D,
                        optional)"
  -c CONFFILE, --conf=CONFFILE
                        Path to the text file containing the parameters.
                        Should be several lines that looks like:
                        "ParameterName = ParameterValue". Must contain values
                        for w, b, D. May contain values for optional
                        parameters mu, garnetBeta, noise, r, g. Default =
                        "./conf.txt"
  -d DUMMYMODE, --dummyMode=DUMMYMODE
                        Tells the program which nodes in the interactome to
                        connect the dummy node to. "terminals"= connect to all
                        terminals, "others"= connect to all nodes except for
                        terminals, "all"= connect to all nodes in the
                        interactome. If you wish you supply your own list of
                        proteins, dummyMode could also be the path to a text
                        file containing a list of proteins (one per line).
                        Default = "terminals"
  --garnet=GARNET       Path to the text file containing the output of the
                        GARNET module regression. Should be a tab delimited
                        file with 2 columns: "TranscriptionFactorName
                        Score". Default = "None"
  --musquared           Flag to add negative prizes to hub nodes proportional
                        to their degree^2, rather than degree. Must specify a
                        positive mu in conf file.
  --excludeTerms        Flag to exclude terminals when calculating negative
                        prizes. Use if you want terminals to keep exact
                        assigned prize regardless of degree.
  --msgpath=MSGPATH     Full path to the message passing code. Default =
                        "<current directory>/msgsteiner"
  --outpath=OUTPUTPATH  Path to the directory which will hold the output
                        files. Default = this directory
  --outlabel=OUTPUTLABEL
                        A string to put at the beginning of the names of files
                        output by the program. Default = "result"
  --cyto30              Use this flag if you want the output files to be
                        amenable with Cytoscape v3.0 (this is the default).
  --cyto28              Use this flag if you want the output files to be
                        amenable with Cytoscape v2.8, rather than v3.0.
  --noisyEdges=NOISENUM
                        An integer specifying how many times you would like to
                        add noise to the given edge values and re-run the
                        algorithm. Results of these runs will be merged
                        together and written in files with the word
                        "_noisyEdges_" added to their names. The noise level
                        can be controlled using the configuration file.
                        Default = 0
  --shuffledPrizes=SHUFFLENUM
                        An integer specifying how many times you would like to
                        shuffle around the given prizes and re-run the
                        algorithm. Results of these runs will be merged
                        together and written in files with the word
                        "_shuffledPrizes_" added to their names. Default = 0
  --randomTerminals=TERMNUM
                        An integer specifying how many times you would like to
                        apply your given prizes to random nodes in the
                        interactome (with a similar degree distribution) and
                        re-run the algorithm. Results of these runs will be
                        merged together and written in files with the word
                        "_randomTerminals_" added to their names. Default = 0
  --knockout=KNOCKOUT   A list specifying protein(s) you would like to "knock
                        out" of the interactome to simulate a knockout
                        experiment, i.e. ['TP53'] or ['TP53', 'EGFR'].
  -k CV, --cv=CV        An integer specifying the k value if you would like to
                        run k-fold cross validation on the prize proteins.
                        Default = None.
  --cv-reps=CV_REPS     An integer specifying how many runs of cross-
                        validation you would like to run. To use this option,
                        you must also specify a -k or --cv parameter. Default
                        = None.
  -s SEED, --seed=SEED  An integer seed for the pseudo-random number
                        generators. If you want to reproduce exact results,
                        supply the same seed. Default = None.

Forest input files and parameters

Required inputs

The first two options (-p and -e) are required. You should record your terminal nodes and prize values in a text file. The file example/a549/Tgfb_phos.txt is an example of what this file should look like. You should record your interactome and edge weights in a text file with 3 or 4 columns. The file data/iref_mitab_miscore_2013_08_12_interactome.txt is a human interactome example (this interactome comes from iRefIndex v13, scored and formatted for our code).

A sample configuration file, a549/tgfb_forest.cfg is supplied. The user can change the values included in this file or can supply their own similarly formatted file. Unlike Garnet, the Forest configuration file name must be preceded with -c or --conf=. If the -c argument is not included in the command line the program will attempt to read the default conf.txt. The parameters w, b, and D must be set in the configuration file. Optional parameters mu, garnetBeta, noise, g, and r may also be included. The processes and threads parameters both provide parallelization. By default, Forest parallelizes tasks by running each network optimization task (e.g. for a different set of shuffled prizes or edge noise values) in a different, single-threaded process. If you are not running Forest multiple times with cross validiation, shuffled prizes, or noisy edges, you may set processes = 1 and threads to the number of processors on your computer to run msgsteiner in a multi-threaded manner.

w = float, controls the number of trees
b = float, controls the trade-off between including more
    terminals and using less reliable edges
D = int, controls the maximum path-length from v0 to terminal nodes
mu = float, controls the degree-based negative prizes (defualt 0.0)
garnetBeta = float, scales the garnet output prizes relative to the
             provided protein prizes (default 0.01)
noise = float, controls the standard deviation of the Gaussian edge
        noise when the --noisyEdges option is used (default 0.333)
g = float, msgsteiner reinforcement parameter that affects the convergence of the
    solution and runtime, with larger values leading to faster convergence
    but suboptimal results (default 0.001)
r = float, msgsteiner parameter that adds random noise to edges,
    which is rarely needed because the Forest --noisyEdges option
    is recommended instead (default 0)
processes = int, number of processes to spawn when doing randomization runs
            (default to number of processors on your computer)
threads = int, number of threads to use during msgsteiner optimization
            (default 1)

For more details about the parameters, see our publication.

Optional inputs

The rest of the command line options are optional.

If you have run the garnet module to create scores for transcription factors, you can include that output file with the --garnet option and use garnetBeta in the configuration file to scale the garnet scores.

The --dummyMode option will change which nodes in the terminal are connected to the dummy node in the interactome. We provide an example of this using a549/Tgfb_interactors.txt. For an explanation of the dummy node, see publication.

The --musquared option will apply negative prizes to nodes based on their squared degree, as opposed to linear degree. This is helpful if the default mu behavior is not strict enough to eliminate irrelevant hub nodes from your network.

If the file msgsteiner is not in the same directory as forest.py, the path needs to be specified using the --msgpath option, e.g., '--msgpath /home/msgsteiner-1.3/msgsteiner'.

If you would like the output files to be stored in a directory other than the one you are running the code from, you can specify this directory with the --outpath option. The names of the output files will all start with the word result unless you specify another word or phrase, such as an identifying label for this experiment or run, with the --outlabel option. The --cyto30 and --cyto28 tags can be used to specify which version of Cytoscape you would like the output files to be compatiable with.

We include three options, --noisyEdges, --shuffledPrizes, and --randomTerminals to determine how robust your results are by comparing them to results with slightly altered input values. To use these options, supply a number for either parameter greater than 0. If the number you give is more than 1, it will alter values and run the program that number of times and merge the results together. The program will add Gaussian noise to the edge values you gave in the -e option, or shuffle the prizes around all the network proteins in the -p option, or assign the prizes to network proteins with similar degrees as your original terminals, according to which option you use. In --noisyEdges, Gaussian noise with mean 0 and standard deviation specified by the parameter noise in the configuration file (default 0.333) will be added to the edge scores. The results from these runs will be stored in seperate files from the results of the run with the original prize or edge values, and both will be outputted by the program to the same directory.

The knockout option can be used if you would like to simulate a knockout experiment by removing a node from your interactome. Specify your knockout proteins in a list, i.e. ['TP53'] or ['TP53', 'EGFR'].

The -k and --cv options can be used if you would like to run k-fold cross validation. This will partition the proteins with prizes into k equal subsamples. It will run msgsteiner k times, leaving one subsample of prizes out each time. The --cv-reps option can be used if you would like to run k-fold cross validation multiple times, each time with a different random partitioning of terminals. If you do not supply --cv-reps but do provide a k, cross validation will be run once. Each time it is run, a file called <outputlabel>_cvResults_<rep>.txt will be created. For each of the k iterations, it will display the number of terminals held out of the prizes dictionary, the number of those that were recovered in the optimal network as Steiner nodes, and the total number of Steiner nodes in the optimal network.

The -s option will supply a seed option to the pseudo-random number generators used in noisyPrizes, shuffledPrizes, randomTerminals, and the optimization in msgsteiner itself. If you want to reproduce exact results, you should supply the same seed every time. If you do not supply your own seed, system time is used a seed.

Running forest

Once you submit your command to the command line the program will run. It will display messages as it completes, letting you know where in the process you are. If there is a warning or an error it will be displayed on the command line. If the run completes successfully, several files will be created. These files can be imported into Cytoscape v.3.0 to view the results of the run. These files will be named first with the outputlabel that you provided (or result by default), and then with a phrase identifying which file type it is.

Forest output

  • info.txt contains information about the algorithm run, including any error messages if there were any during the run.

  • optimalForest.sif contains the optimal network output of the message-passing algorithm (without the dummy node). It is a Simple Interaction Format file. To see the network, open Cytoscape, and click on File > Import > Network > File..., and then select this file to open. Click OK.

  • augmentedForest.sif is the same thing, only it includes all the edges in the interactome that exist between nodes in the optimal Forest, even those edges not chosen by the algorithm. Betweenness centrality for all nodes was calculated with this network.

  • dummyForest.sif is the same as optimalForest.sif, only it includes the dummy node and all edges connecting to it.

  • edgeattributes.tsv is a tab-seperated value file containing information for each edge in the network, such as the weight in the interactome, and the fraction of optimal networks this edge was contained in. To import this information into Cytoscape, first import the network .sif file you would like to view, and then click on File > Import > Table > File..., and select this file. Specify that this file contains edge attributes, rather than node attributes, and that the first row of the file should be interpreted as column labels. Click OK.

  • nodeattributes.tsv is a tab-seperated value file containing information for each node in the network, such as the prize you assigned to it and betweenness centrality in the augmented network. To import this information into Cytoscape, first import the network .sif file you would like to view, and then click on File > Import > Table > File..., and select this file. Specify that this file contains node attributes, rather than edge attributes, and that the first row of the file should be interpreted as column labels. Click OK.

When the network and the attributes are imported into Cytoscape, you can alter the appearance of the network as you usually would using VizMapper.

Testing

See the tests directory for instructions on testing Omics Integrator.

Third Party Code

See the 'LICENSE-3RD-PARTY' file for license information for: python-avl-tree by Pavel Grafov