Here are the instructions for the ImageJ and Cell Profiler analysis of Liver Microtumors +/- AMPK activators 

High-content image data set for one 384-well plate of liver microtumors formed from HepG2 cells.

I look at differences in a side-by-side, from a practitioner's perspective (from FlowingData)

This is the community group focused on using R in the classroom.  The following links may be of particular interest.

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Discussion Forum

In this interactive course from DataCamp, you will learn how to to perform sophisticated data manipulation tasks using dplyr. Master the five verbs of data manipulation, and complementing techniques to chain your operations, perform group-wise calculations and access data stored in a database outside R.

Outline:

• Introduction to dplyr and tbls
• Select and mutate 
• Filter and arrange 
• Summarise and the pipe operator 
• Group_by and working with databases 

Online course  DataCamp
 

Learning objective:  To be able to use the 6 main dataframe manipulation ‘verbs’ with pipes in dplyr.

Part of a software carpentry online course R for reproducible scientific analysis.

Dataframe manipulation with dplyr

R for reproducible scientific analysis

Software Carpentry

Data:

Gapminder dataset

Learning objective:  To understand the concepts of ‘long’ and ‘wide’ data formats and be able to convert between them with tidyr.

Part of a software carpentry online course R for reproducible scientific analysis.

Dataframe manipulation with tidyr

R for reproducible scientific analysis

Software Carpentry

Data:

Gapminder dataset

Gapminder dataset (wide format)

Download

Link

by Sharon Machlis
edited by Johanna Ambrosio

Computerworld

These notes are an introduction to using the statistical software package R for an introductory statistics course. They are meant to accompany an introductory statistics book such as Kitchens “Exploring Statistics”. The goals are not to show all the features of R, or to replace a standard textbook, but rather to be used with a textbook to illustrate the features of R that can be learned in a one-semester, introductory statistics course.

Download

Chapter from "Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan (Second Edition)" by John K. Kruschke

Proxy Access

Last year, I presented an informal course on the basics of R Graphics University of Turku. In this blog post, I am providing some of the slides and the full code from that practical, which shows how to build different plot types using the basic (i.e. pre-installed) graphics in R

Website Supplementary Files

Website

by Tim Appelhans

This tutorial was developed as part of a one-day workshop held within the Ecosystem Informatics PhD-programme at the Philipps University of Marburg. The contents of this turtorial are published under the creatice commons license 'Attribution-ShareAlike CC BY-SA'.

Website

Created by Thomas Malloy (requires Flash)

A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and its value will be highlighted in the combined computation. The circle diagram show the partitioning of the sums of squares, and if you hover a part it will show from where the variation is coming. I tried to make the plots look like plots from the R-package ggplot2.

Created by Kristoffer Magnusson

This visualization is meant as an aid for students when they are learning about statistical hypothesis testing. The visualization is based on a one-sample Z-test. You can vary the sample size, power, signifance level and effect size using the sliders to see how the sampling distributions change.

Created by Kristoffer Magnusson

Note:  To run this visualization, you need a QUBES account.  Click here to register (it's free!)

From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.

From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.

From the authors of the textbook:

We have put together a few interactive animations that will illustrate some important statistics concepts (with funding from the University of British Columbia). These are all released into the public domain, so anyone can use them as they wish. Click on the “tutorial” button to be walked through the concepts, or just explore them on your own.

Datasets  R Code  R Pubs