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Project 4: Develop an interactive application to help understand alpha and beta diversity metrics choices #4
Develop an interactive application to facilitate informed sequencing quality control decisions for downstream analysis on many samples
referenced this issue
Sep 5, 2017
Sooo I may have found someone's solution to my proposed project called MultiQC (GitHub link). It was published just over a year ago and is even more robust and has more functionality than just for my 16S rRNA use case. A quick Biostars/Google search could have saved me time
@abaghela if you allow me, I have another proposition for a project I could lead that is specific to microbiome analysis. Let me know if you have any concerns with this new proposed project or not. Thanks.
Title: Develop an interactive application to help understand alpha and beta diversity metrics choices
Problem: There are many alpha and beta diversity metrics to analyze microbial ecological or microbiome data. Alpha diversity describes an estimate of the total number of species in a sample. Beta diversity describes the differences between samples. Below are some example of then number of metrics you can use.
Plot from "Alpha diversity graphics" page for phyloseq showing various alpha diversity metrics to choose from http://joey711.github.io/phyloseq/plot_richness-examples
Below is are just a few beta diversity metrics choose from
> library(phyloseq) > unlist(distanceMethodList) UniFrac1 UniFrac2 DPCoA JSD vegdist1 vegdist2 "unifrac" "wunifrac" "dpcoa" "jsd" "manhattan" "euclidean" vegdist3 vegdist4 vegdist5 vegdist6 vegdist7 vegdist8 "canberra" "bray" "kulczynski" "jaccard" "gower" "altGower" vegdist9 vegdist10 vegdist11 vegdist12 vegdist13 vegdist14 "morisita" "horn" "mountford" "raup" "binomial" "chao" vegdist15 betadiver1 betadiver2 betadiver3 betadiver4 betadiver5 "cao" "w" "-1" "c" "wb" "r" betadiver6 betadiver7 betadiver8 betadiver9 betadiver10 betadiver11 "I" "e" "t" "me" "j" "sor" betadiver12 betadiver13 betadiver14 betadiver15 betadiver16 betadiver17 "m" "-2" "co" "cc" "g" "-3" betadiver18 betadiver19 betadiver20 betadiver21 betadiver22 betadiver23 "l" "19" "hk" "rlb" "sim" "gl" betadiver24 dist1 dist2 dist3 designdist "z" "maximum" "binary" "minkowski" "ANY"
With so many metrics to choose from, how do you know which is the "best" and how will your data affect the calculation of these metrics?
Proposed Project: Create an interactive Shiny application to show changes in your chosen alpha or beta diversity metrics to see how each change based on simulated or real data. Some of these metrics are sensitive to single or double counts of species so this will be good to see how different distributions of counts will change these metrics and your interpretations of them. This should be designed to give an intuitive understanding of how these metrics work.
@ampatzia thanks for your interest! I've created a bare repository for put this project. I plan on getting a base Shiny application up for people to get up and running later this week, along with some ideas of what could be in the application itself. If I come up with anything else, I'll let you know!
Hey team lead, we've been gathering Github IDs for your team members. We see that you've already started a repo for this project. So could you please add the following people as collaborators to that project?
Once the people are added, it'd be a great idea to start a discussion on that repo with information to get your team members started (e.g. some small suggested reading, things to look up, etc). We will also be adding everyone to Slack and creating a specific channel for each project. This may be an easier way to communicate.
We'll forward on any remaining Github IDs through this issue.