## Recently Published

##### curatedMetagenomicData for Machine Learning

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.

##### Applied Statistics for High-throughput Biology Day 4

## 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)

##### Applied Statistics for High-throughput Biology: Session 2

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

##### Applied Statistics for High-throughput Biology: Session 1

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

##### assignment 3 demo

code loading and ioslides demo

##### Applied Statistics for High-throughput Biology 2019, Day 4

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

##### Applied Statistics for High-throughput Biology Day 3

* 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

##### Applied Statistics for High-throughput Biology 2019, Day 2

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

##### Applied Statistics for High-throughput Biology 2019, Day 1

## 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

##### Guess the professor's age!

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

##### UNIVR Applied Statistics for High-throughput Biology 2019: Day 4

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.

##### UNIVR Applied Statistics for High-throughput Biology 2019: Day 2

* 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

##### Guess Professor Waldron's age

An example for improving intuition and interpretation of confidence intervals

##### AnnotationHub Simple Demo

An encode track

##### Workflow for multi-omics analysis with MultiAssayExperiment

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.

##### EPIC 2018 - intro lab

curatedMetagenomicData, distances, PCA / PCoA

##### Guess the Professors' Ages

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

##### Sub1

messy example analysis

##### Lecture: linear modeling for microbiome data in R/Bioconductor

## 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

##### Lecture: Exploratory analysis of microbiome data in R/Bioconductor

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

##### Applied Statistics for High-throughput Biology Day 2: distances, PCA, batch effects

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

##### Applied Statistics for High-throughput Biology Day 3: categorical data, exploratory data analysis

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

##### Applied Statistics for High-throughput Biology Day 2: linear models

Linear and Generalized Linear Models, multiple testing

##### Applied Statistics for High-Throughput Biology 2017, day 1

Introduction to random variables and to R

##### CSAMA 2017 - Resampling Methods

Resampling: cross-validation, bootstrap, and permutation tests

##### Resampling Methods

Iowa Institute of Human Genetics 2017 bioinformatics short course

##### Analysis of Microbiome Data

Iowa Institute of Human Genetics 2017 bioinformatics short course

##### Book club - distances

Data Analysis for the Life Sciences, Chapter 8 section 1

##### BIOS 621 session 5

Loglinear models part 2

##### BIos621_session1_lab

Pokemon GO data analysis with dplyr and ggplot2

##### BIOS 621 session 3

GLM review, interactions, model matrices

##### BIOS 621 session 2

Logistic regression as a GLM

##### curatedMetagenomicData and ExperimentHub example

A short demonstration of curatedMetagenomicData and ExperimentHub

##### Statistical analysis for metagenomic data

Focus on exploratory data analysis and regression

##### Applied Statistics for High-throughput Biology: Session 5

Distances, SVD, PCA, MDS

##### Applied Statistics for High-throughput Biology: Session 3

Hypothesis testing

##### Trento Session 1 Lecture

Random Variables
Intro to R

##### MultiAssayExperiment demonstration vignette

A demonstration of the use of MultiAssayExperiment on a toy dataset

##### forestplotexample

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

##### biocMultiAssay

biocMultiAssay vignette

##### ovc_ehub

Creating an eHub for TCGA ovarian cancer dataset.

##### OVC multi-assay QC analysis

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

##### Iris dataset regression examples

for R Bootcamp August 19 2014

##### Introduction to R graphics

for R Bootcamp August 9, 2014