jorussell-idm / magude Goto Github PK

1. Spatial differentiation (from here or not here)
2. Sweeping over number of sites and degree of heterozygosity with outcome as ??? information? origin?

Rework slides from 2.25 presentation deck into a high level
Illustrator magic on https://www.dropbox.com/home/Malaria%20Team%20Folder/projects/parasite_genetics/genomics/synthetic_genomes/eir_sweep/plots?preview=variants1000_af0.01_LMH_077_year_repDistribution.png

Should be private but shareable and to contain documented .ipynb scripts

rerun the Magude sim framework from GR

Three pipelines GenEpi+DTK

1. Layered modular (CoTransmission): Scenario and Scientific Question (What's the isolated contribution of stochastic genotype seed replicates to the variance in genetic feature, allows for some sensible discussion of how broad changes in initial conditions affect results) True IBD and Pairwise IBS with some discussion of how certain number of sites under certain conditions of diversity fare in poorly in tools like hmmIBD.

• 5 replicates
• Distribution of pairwise IBD at monthly,
• Classification by COI
• Next step: How do we meet you in the middle? Let's say we use REAL McCOIL

2. FPG alone: True pairwise IBD - What to show before sharing externally? Mapping sims onto ref epi data from Garki, but do these map onto realistic representations/relationships? Pairwise IBD a function of EIR?

• 3 dfs:
  indexed: numpy arrays: timestamp, individual id, infection id,
  indexed: genotype values
  indexed: allele roots

3. Hybrid: Separate evaluation of neutral sites on a tree of breakpoints. Drug selection sweep for exploring independence of unlinked neutral sites from drug resistance markers on small effective population sizes in the simulation.

Questions of interest to collabs:

1. Working towards combining genotype and phenotype with relevance to modeling emergence and spread of drug resistance (of interest to Malaria PST, Senegal at large group)
2. Highest level distinction tree-representation to amplicon representation: IBD in polyclonal infections : is there a threshold in the value of information contributed by 2, or 2+ infections within polyclonal infections, Senegal is more interested in classification of 2 or 2+, UCSF is more interested in the statistical metric for collapsing complex infections.

• Questions:

• What is the right threshold for informative contributions to polygenomic infections? (2?) By use case?

• What is the right classification for polygenomic infections?

• What is sensitivity of genetic metrics to the threshold of observability or classification?

• What is a prototypical use case to demonstrate?

• Q for Bryan: Is the first step the deconvolution of microhaplotype frequencies across sites? If low diversity between strains, you will underestimate true clonality because of relatedness. Does r refer to relatedness calculated by site or r by strain representation/deconvolution

• Q: If you don't know how many unique strains you have (given sequence data from a polygenomic infection), how do you make estimate of COI, without accommodating relatedness in the parasite population you would tend to underestimate COI? Baseline estimate is unique contributors to the diversity at sites. Because of unknown phasing, you will almost certainly be underestimating true COI.

• ROLE for MODEL!: Simulate a true COI to help calculate the effective scale factor for converting effective COI to true COI across range of COI (for example effCOI of 2 vs effCOI of 10, also dependent on number of sites and their diversity). Calculation of scale factor may depend on assumptions of transmission intensity and initialization of diversity, structure and linkage of diversity?

What is the most appropriate statistic to calculate for IBD relatedness when there is within host and between host relatedness (average by host? rank ordered?) Does this apply when true IBD is simulated

A collection of work items related to the GenMoz collaboration, designing and evaluating sample strategy in and around Magude as well as in the higher transmission Northern sites.

Try using a larger population, lower seasonal amplitude, and consistent outbreaks as a method for sustaining lower prevalence scenarios for use in GenEpi.

From Meeting with Jessica and Albert on representations (and calculations) of COI from EMOD+GenEpi

Sample id, locus id, haplotype id, unique values for each column

Download analyzer here: C:\git\malaria-cotransmission\analyzers\download_analyzer.py

Stored output here: C:\Users\jorussell\Dropbox (IDM)\Malaria Team Folder\projects\parasite_genetics\DTK\example_with_cotransmission\transmission_report_outputs\EIR_sweep

Deliverable: white paper in Q2

Strategy of sampling over size of sampling, this is what we can do in this time frame

Hash out in Wed MPG

in main reporters dir

header
arg less constructor
constructor
Serialize
Deserialize
for loop

When done building
flatten_config
Run regressions 79,80
After run, do diff checker and manual inspection of values in conInfIds

Copy over the new JSON into outputs

Create migration csv specifying two way migration pattern and generate binaries

See line 188 in genetics.variants.py

def make_dummy_24snp_panel(allele_ratio=0.5):

nsnps = 24

panel = SimpleVariantPanel()
panel.add_chromosomes(np.ones(14) * 100)  # 14 dummy chromosomes of 100 "sites" each

root_properties = pd.DataFrame(index=range(nsnps))
root_properties['variant_frequencies'] = np.array([[allele_ratio, 1. - allele_ratio]] * nsnps).tolist()

panel.add_sites([20, 80, 190, 330, 470, 508, 570,
                 615, 630, 632, 640, 641, 642, 655, 695,
                 740, 840, 904, 980, 1005, 1015, 1205, 1250, 1320],
                ['0'] * nsnps,
                [['0', '1']] * nsnps,
                None,
                metadata=['test' + str(i) for i in range(nsnps)],
                root_properties=root_properties)
return panel