# lwaldron

## Recently Published

##### 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
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