Algorithms used to analyze ddPCR droplets | Step 1: Identify failed wells | Step 2: Identify outlier droplets | Step 3: Identify empty droplets | Step 4: Gate droplets | Step 4.1: Identify rain droplets | Step 4.2: Identify mutant versus wildtype droplets | Step 4.3: Classify wells as mutant versus wildtype | Step 5: Revisit gating of wildtype wells

ddpcr: Analysis and visualization of Droplet Digital PCR data in R and on the web | Table of contents

A plate object is a list | Using S3 for plate objects to override base generic functions | Using S3 for plate objects to support inheritance | Analysis of different built-in plate types | How the analysis steps work

ggExtra - Add marginal histograms to ggplot2, and more ggplot2 enhancements | Installation | Marginal plots RStudio addin/gadget | Usage | ggMarginal - Add marginal histograms/boxplots/density plots to ggplot2 scatterplots | Using ggMarginal() in R Notebooks or Rmarkdown | removeGrid - Remove grid lines from ggplot2 | rotateTextX - Rotate x axis labels | plotCount - Plot count data with ggplot2

Table of contents

ezknitr - Avoid the typical working directory pain when using 'knitr' | Table of contents

lightsout - Implementation of the 'Lights Out' Puzzle Game in R | Play the game | Quick start | Create a game board | Press a light | Press multiple lights | Get the solution | Solving boards algorithmically

Add shinyjs features | Final code

Default plate type: ddpcr_plate | Built-in plate types | Functions to implement when adding a new plate type | parent_plate_type(): Define parent plate type | define_params(): Define plate type parameters | define_clusters(): Define droplet clusters | define_steps(): Define analysis steps | Changing the algorithm of an existing step | plot(): Plot a ddPCR plate | Example