{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Qurro QIIME 2 \"Moving Pictures\" Tutorial\n", "\n", "In this tutorial, we'll demonstrate the process of using [Qurro](https://github.com/biocore/qurro) to investigate a compositional biplot generated by [DEICODE](https://github.com/biocore/DEICODE/).\n", "\n", "## 0. Introduction\n", "\n", "### 0.1. What is Qurro?\n", "\n", "Lots of tools for analyzing \" 'omic\" datasets can produce __feature rankings__. These rankings can be used as a guide to look at the __log-ratios__ of certain features in a dataset. Qurro is a tool for visualizing both of these types of data.\n", "\n", "#### 0.1.1. ...What are feature rankings?\n", "The term \"feature rankings\" includes __differentials__, which we define as the estimated log-fold changes for features' abundances across different sample types. You can get this sort of output from lots of \"differential abundance\" tools, including but definitely not limited to [ALDEx2](https://bioconductor.org/packages/release/bioc/html/ALDEx2.html), [Songbird](https://github.com/biocore/songbird/), [Corncob](https://github.com/bryandmartin/corncob/), [DESeq2](https://bioconductor.org/packages/release/bioc/html/DESeq2.html), [edgeR](https://bioconductor.org/packages/release/bioc/html/edgeR.html), etc.\n", "\n", "The term \"feature rankings\" also includes __feature loadings__ in a [biplot](https://en.wikipedia.org/wiki/Biplot) (see [Aitchison and Greenacre 2002](https://rss.onlinelibrary.wiley.com/doi/full/10.1111/1467-9876.00275)); you can get biplots from running [DEICODE](https://github.com/biocore/DEICODE), which is a tool that works well with microbiome datasets, or from a variety of other methods. **In this tutorial we'll show how to use Qurro with feature loadings**, but if you'd like to try out Qurro with differentials we encourage checking out the [Qurro transcriptomics tutorial](https://nbviewer.jupyter.org/github/biocore/qurro/blob/master/example_notebooks/ALDEx2_TCGA_LUSC/transcriptomic_example.ipynb) (which demonstrates using Qurro with those).\n", "\n", "In either case -- differentials or feature loadings -- both of these flavors of \"feature rankings\" can be interpreted as, well, __rankings__ (i.e. you can just sort them numerically). Visualizing these rankings gives us a list of features in a dataset sorted based on their association with some sort of variation, in either a supervised (in the case of differentials) or unsupervised (in the case of feature loadings) way. We call this a **rank plot**.\n", "\n", "