Hackathon for the DREAM FINRISK Challenge cardiovascular event prediction from microbiome

Placeholder README for the Hackathon taking place in Denver 14th to 18th November 2022.

• R (4.2.1 or preferably newer)
• R Studio suggested if you want a bit of assistance
• Highly recommend git, so we can merge and share code easily
• GitHub (where you're currently at) will serve as our main platform

Install packages from the terminal with install.packages("pckgName") (replace pckgName with the exact name of the package you want to install. Note that this only works for Central R Archive Network packages (CRAN).

For Bioconductor you need to use:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ComplexHeatmap")

Packages (and for what purpose they could be used):

• ggplot2 (for visualizations beyond base visualizations)
• ComplexHeatmap (for advanced heatmap visualizations)
• glmnet (for regularized regression with L1/L2 norms)
• ...

name of R function; description of the purpose (if it needs a non-base package, package name indicated here)

• write.table; for writing output
• read.table; for reading output
• setwd; for setting current working directory, notice that R understands this as your root directory
• |>; R's native pipe operator (R >= 4.3.0)
• plot; an ordinary x-axis vs. y-axis plotting
• boxplot; a boxplot
• ls; list objects in current R workspace
• <-; placement operator (recommended for using when not talking about function parameters)
• =; placement operator or function parameters (recommended to use only with function parameters)
• ...

• Learning how to use Singularity containers: https://docs.sylabs.io/guides/3.5/user-guide/introduction.html (the DREAM Challenge comes with an example container)

For example, reading

• Salosensaari A, Laitinen V, Havulinna AS et al., Taxonomic signatures of cause-specific mortality risk in human gut microbiome. Nat Commun 12, 2671 (2021). https://doi.org/10.1038/s41467-021-22962-y would be useful.

Registration on Synapse platform is highly encourated:

Overview to the Challenge phases https://www.synapse.org/#!Synapse:syn27130803/wiki/619280

Data description is essential for understanding what we're trying to model: https://www.synapse.org/#!Synapse:syn27130803/wiki/619274

• train_x / test_x : rank inverse normal distributed counts
• train_raw / test_raw : read counts before above normalization
• train_p / test_p : phenotype (clinical) info for train and test sets respectively
• train_y / test_y : Surv-response vector for train and test sets respectively

• "Valtimokovettuma tautiin eli ateroskleroosiin ja siihen liittyviin sydän- ja verisuoniongelmiin on mikrobiomilla tutkittuja yhteyksiä. Asteikolla 1-10 mitattuna, mitä enemmän suojaavia tekijöitä löydetään mikrobiomista, sitä korkeampi on suojaava vaikutus näihin sairauksiin. Jotkut bakteerit, kuten Erysipelotrichaceae tuottaa trimetyyliamiinia (TMA) koliinista, jota löytyy kananmunan keltuaisesta, sekä L-kartiniinia, jota saadaan punaisesta lihasta ja kalasta. TMA hapetetaan maksassa trimetyyliamiini-typpioksidiksi TMAO, johon liittyy kohonnut riski sairastua ateroskleroosiin ja muihin sydän- ja verisuonitauteihin." (https://www.riskigeeni.net/mikrobiomi.html)

-> Some bacteria such as Erysipelotrichaeae produce trimethylaminine (TMA) from coline and L-cartinine; TMA is oxidated in the liver to TMAO, which involves a heightened risk to suffer from ateroscloreosis and other cardiovascular diseases