Create a giant Table 1 of a few select curatedMetagenomicData variables, stratified by study.

Change-point model of stepwise treadmill test

Note that this is a workaround for the harmonized weights that NYCDOHMH can provide.

This analysis selects a subset of samples (all CRC-related in this example), sorts those those species in order of descending prevalence, then displays a table of prevalences and writes the prevalences to file.

These datasets have case-control labels (study_condition) and two or more independent datasets. It produces two versions: a TreeSummarizedExperiment containing all sample metadata and a phylogenetic tree, and a csv file containing only taxonomic data and a few key metadata columns.

## Day 4 outline [Book](https://genomicsclass.github.io/book/) chapter 8: - Distances in high dimensions - Principal Components Analysis and Singular Value Decomposition - Multidimensional Scaling - Batch Effects (Chapter 10)

Session 2 outline Hypothesis testing for categorical variables Resampling methods Exploratory data analysis

Day 1 outline * Some essential R classes and related Bioconductor classes * Random variables and distributions * Hypothesis testing for one or two samples (t-test, Wilcoxon test, etc) * Confidence intervals * Introduction to dplyr Book chapters 0 and 1

code loading and ioslides demo

- Distances in high dimensions - Principal Components Analysis - Multidimensional Scaling - Batch Effects

* Multiple linear regression + Continuous and categorical predictors + Interactions * Model formulae * Generalized Linear Models + Linear, logistic, log-Linear links + Poisson, Negative Binomial error distributions * Multiple Hypothesis Testing Textbook sources: * Chapter 5: Linear models * Chapter 6: Inference for high-dimensional data

- Hypothesis testing for categorical variables - Fisher's Exact Test and Chi-square Test - Resampling methods + Permutation Tests + Cross-validation + Bootstrap - Exploratory data analysis

## Day 1 outline - Some essential R classes and related Bioconductor classes - Introduction to `dplyr` - Random variables and distributions - Hypothesis testing for one or two samples (t-test, Wilcoxon test, etc) - Confidence intervals

A survey of Prof Waldron's age to help think about confidence intervals.

This workshop demonstrates data management and analyses of multiple assays associated with a single set of biological specimens, using the `MultiAssayExperiment` data class and methods.

* Multiple linear regression + Continuous and categorical predictors + Interactions * Model formulae * Generalized Linear Models + Linear, logistic, log-Linear links + Poisson, Negative Binomial error distributions * Multiple Hypothesis Testing

An example for improving intuition and interpretation of confidence intervals

An encode track

This workshop demonstrates data management and analyses of multiple assays associated with a single set of biological specimens, using the `MultiAssayExperiment` data class and methods. It introduces the `RaggedExperiment` data class, which provides efficient and powerful operations for representation of copy number and mutation and variant data that are represented by different genomic ranges for each specimen.

curatedMetagenomicData, distances, PCA / PCoA

Results of a survey given in BIOS 611 in 2015 and 2017

messy example analysis

## Outline * Multiple linear regression + Continuous and categorical predictors + Interactions * Model formulae * Design matrix * Generalized Linear Models + Linear, logistic, log-Linear links + Poisson, Negative Binomial error distributions + Zero inflation

## Outline - Statistical properties of metagenomic data - Distances for high dimensional data - Principal Components and Principal Coordinates Analysis - Alpha diversity

Distances in high dimensions Principal Components Analysis Multidimensional Scaling Batch Effects Book chapter 7

- Inference in high dimensions: multiple testing - Hypothesis testing for categorical variables - Fisher's Exact Test and Chi-square Test - Exploratory data analysis

Linear and Generalized Linear Models, multiple testing

Introduction to random variables and to R

Resampling: cross-validation, bootstrap, and permutation tests

Iowa Institute of Human Genetics 2017 bioinformatics short course

Iowa Institute of Human Genetics 2017 bioinformatics short course

Data Analysis for the Life Sciences, Chapter 8 section 1

Loglinear models part 2

Pokemon GO data analysis with dplyr and ggplot2

GLM review, interactions, model matrices

Logistic regression as a GLM

A short demonstration of curatedMetagenomicData and ExperimentHub

Focus on exploratory data analysis and regression

Distances, SVD, PCA, MDS

Hypothesis testing

Random Variables Intro to R

A demonstration of the use of MultiAssayExperiment on a toy dataset

Case study in meta-analysis of survival-associated genes in ovarian cancer

biocMultiAssay vignette

Creating an eHub for TCGA ovarian cancer dataset.

Vignette for copy number / expression quality control analysis of TCGA ovarian cancer.

for R Bootcamp August 19 2014

for R Bootcamp August 9, 2014