Cosmos Sdk Tutorial
Chapin Ratte
COSMOS is dependent on CARNIVAL for exhibiting the signalling pathwayoptimisation. CARNIVAL requires the interactive version of IBM Cplex orCBC-COIN solver as the network optimiser. The IBM ILOG Cplex is freelyavailable through Academic Initiative here.The CBC solver is opensource and freely available for any user, but has a significantly lowerperformance than CPLEX. Obtain CBC executable directly usable for cosmoshere.Alternatively for small networks, users can rely on the freely availablelpSolveR-package, which is automatically installed with the package.
COSMOS (Causal Oriented Search of Multi-Omic Space) is a method thatintegrates phosphoproteomics, transcriptomics, and metabolomics datasets. COSMOS leverages extensive prior knowledge of signaling pathways,metabolic networks, and gene regulation with computational methods toestimate activities of transcription factors and kinases as well asnetwork-level causal reasoning. This pipeline can provide mechanisticexplanations for experimental observations across multiple omic datasets.
The prior knowledge: The goal of COSMOS is essentially to connectvarious deregulation events together with mechanistic hypotheses. Thosemechanistic hypotheses are basically known potential interactionsbetween molecular biology features, such as proteins and metabolites. Anexample of such interaction can be the activation of MTOR by AKT, in theEGFR canonical pathway. Thousands of such potential interaction can beassembled together to form a large network of interactions. Suchnetworks are thus called prior knowledge networks (PKN), because theysummarize large amounts of prior knowledge in the form of a network. Inthe context of COSMOS this interactions come from the Omnipath database,STICHdb and the recon3D reaction network. With respect to the exampleinteraction between MTOR and AKT, the question that is usually askedwhen presented with a given experimental context is: while MTOR canactivate AKT in general, is this interaction relevant in my experimentalcontext? This brings us to the second component of COSMOS.
The algorithm: The problem described in the data part isformulated as an integer linear optimisation problem. The PKN and thedata can be abstracted as a large set of integer variables operatingtogether in an even larger set of equations (see -019-0118-z formore info). Given a set of fixed value for the variable (that is, asubset of the known deregulation), we need to find the values for theother sets of variable (corresponding to proteins and metabolites forwhich we have no information in our data) that can lead to a satisfyingresult. A satisfying results in the context of COSMOS represent asub-network of interactions that is as small as possible whilecoherently explaining as many of our known deregulation as possible.Thus, the set of equation mentioned earlier needs to be solved, using aninteger linear programming solver. The solvers that are currentlyavailable for COSMOS are lpsolve, cbc and cplex. In general, CPLEX ispreferred over CBC for most real case applications, while lpsolve ismainly used for testing with small networks and sets ofmeasurements.
In the next section, we prepare the input to run cosmosR. Thesignaling inputs are the result of footprint based TF and kinaseactivity estimation. For more info on TF activity estimation fromtranscriptomic data, see: (Especiallychapter 4)
The maximum network depth will define the maximum number of stepdownstream of kinase/TF COSMOS will look for deregulated metabolites.Good first guess for max depth could be around between 4 and 6 (here itis 15 for the toy dataset)
The list of genes in the differential expression data will also beused as a reference to define which genes are expressed or not (allgenes in the diff_expression_data are considered expressed, and genesthat are no in diff_expression_data are removed from the network).
Here we simply take the union of forward and backward runs to createa full network solution lopping between signaling, gene-regulation andmetabolism. Since there is an overlap between the result network offorward and backward run, you may optionally want to check if there areany node sign that are incoherent in the overlap between the twosolutions.
This network represents the flow of activities that can connect MYCup-regulation with Glucitol (Sorbitol) accumulation. Here, NFKB1 canupregulate the expression of SLC2A1, which in turn transport moreglucose in the cytoplasm. The increase transport of glucose can lead tomore glucose being avlaible for conversion into glucitol by the AKR1Aenzyme. Interestingly, glucitol is a now activator of MAPK14, thusleading to the appearance of a positive feedback loop connecting MYC,glucitol and MAPK14.
It is important to understand that each of this links ishypothetical. The come from a larger pool of potential molecularinteractions present in multiple online databases and compiled inomnipath, STITCH and recon metabolic network. They exist in theliterature and are interactions that are known to potentially exists inother experimental contexts. Thus, COSMOS compile all those potentialinteractions together and proposes a coherent set that can explain thedata at hand.
Those links should however be considered only as potentialmechanistic connections, and will need to be further confirmedexperimentally. Those interactions can be searched in the literature tosee in which other disease or experimental context they have been shownto be relevant. Taken together, multiple interactions can help to builda biological story that can guide further underatanding of theunderlying biology and decide on future experiments.
Often it is useful to perform an Over Representation Analysis (ORA)on the resulting nodes of a COSMOS network as a first analysis step toget a more functional interpretation on the modeled signaling cascade. Acommon way to this is to test whether the selected genes (nodes) in theCOSMOS solution network show statistically significant differences incomparison to the prior-knowledge network (PKN).
The differentially expressed genes give information about thecellular processes that are deregulated and if the proportions invarious pathways are SIGNIFICANTLY different from what is expected.Inthis way the significant differences between two biological conditions(e.g. cancer vs. normal tissue, or treatment vs. untreated cells) can beshown.
Algorithms that perform an ORA are implemented in other R packageslike piano or decoupleR. In addition to a gene set collection thesealgorithms require two different lists as inputs: - nodes in the COSMOSsolution network which relate back to the input data(e.g. transcriptomics, proteomics, metabolomics, fluxomics, orperturbations) - all nodes (kinases, transcription factors, metabolites)in the prior-knowledge network (which are used as the background in ouranalysis)
Ready to quilt another walking foot quilting design? Jagged Cosmos is a really fun zig-zaggy design that's super fun to quilt with your walking foot. Learn how to quilt Jagged Cosmos in this new walking foot quilting tutorial:
Click Here to find the book Explore Walking Foot Quilting with Leah Day. Inside you'll find 30 fun walking foot quilting designs with tips on how to use them in real quilts. This book also includes the quilt pattern for Marvelous Mosaic, the quilt we're making together with these squares!
The first step to quilting Jagged Cosmos is to break down your quilting space and pick the center point for the jagged lines to radiate out from. Just like with Bright Star, I positioned that spot slightly off center which made the quilting design much more interesting when quilted in this square sandwich.
As you can tell from the video, quilting this design is a bit of a stop-start process as I had to tie off and bury every thread tail in the middle of the quilt before quilting the next line. It can also take a bit of time to plan the design and decide how close or far apart to space the lines.
What's that weird thing on top of my machine? That's a small magnetic pincushion and a cheater needle. I keep those handy for tying off and burying loose thread tails as I quilt, which I had to do a lot in this tutorial! Click Here to learn how to tie off and bury your thread tails.
When quilting a design like this where all the lines come together in a single point, you'll need to watch out for thread build up. As you quilt more lines starting in a single point, the threads can pile up on top of one another, creating an area that's much more dense than the rest of the quilt.
If you're not careful, this can create distortion and the finished block or quilt will not lay flat over that area. It will bubble up because that area has a lot more quilting lines space tighter together than all other areas.
It's perfectly fine to begin some lines 1/2 or even 1 inch away from the center spot. Pull up thread a small distance away to begin your next jagged line. The texture will look the same, but it will greatly reduce your chance of the density of the quilting causing a problem.
Quilting this design with free motion quilting might feel a bit easier because the foot is smaller and you can better judge the distance between the quilted lines. The walking foot is a bit clunky and can cover so much of the quilt it's hard to see what you've quilted when it's positioned over an important area.
However, it's harder to quilt straight lines with free motion quilting because the foot hovers and doesn't give you the same stability as a walking foot. So there's upsides and downsides to both methods when quilting Jagged Cosmos.
Once fully deployed, the COSMOS testbed will support at-scale experimentation of novel advanced wireless broadband and communication technologies in both sub-6 GHz and mmWave frequency bands in West Harlem in New York City, which is a representative of a densely populated urban environment. The COSMOS testbed platform provides a mix of fully programmable software-defined radio (SDR) nodes for flexible wireless experimentation. It also includes novel 100 Gbps+ fiber, free space optical, and microwave backhaul technologies interconnected with a software-defined network (SDN) switching fabric for minimum latency and flexibility in setting up experimental network topologies. Moreover, the remote accessibility of COSMOS lowers the barrier for experimentation in the area of radio and wireless technology and thus improves education and research productivity. The goal of this tutorial is to provide an introduction to COSMOS testbed management framework OMF and measurement library OML and main technology capabilities.
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