Of data analysis, biology and random other things

Table of Contents Introduction to ggplot  This post will give you a quick introduction to a great way of plotting using R: the ggplot2 package (which for simplicity I will call ggplot from now on). ggplot is an R package which makes it super-easy to create visually pleasing plots with just a few lines of code!

Create awesome plots with ggplot!

In the first part of this series, we looked at how to open images and perform some basic manipulations using Scikit Image. This second part will introduce you to digital image filters! Digital filters for image analysis Often we need to perform some operation on our images.

In the first part of this series, we looked at how to open images and perform some basic manipulations using Scikit Image. This second part will introduce you to digital image filters! Table of Contents Digital filters for image analysis   Often we need to perform some operation on our images. For example, we may want to reduce noise, find object edges in the image. Digital filters can help with that!

Image analysis made easy with Scikit Image! Part 2 &#8211; image filters

There are lots of amazing tools for image analysis out there, to perform some really powerful and complex analyses. Sometimes, however, you may want to create automated pipelines, for example, to analyse large amounts of images, or to process a video feed in real-time. Countless options are out there, each with its own strengths and weaknesses.

Image analysis made easy with Scikit Image! (part 1)

In today’s post, we are going to perform some sample classification using machine learning in R. We are going to use a publicly available dataset, the Breast Cancer Wisconsin (Diagnostic) Data Set (WDBC), which is available on the UC Irvine Machine Learning Repository. This is a fairly simple dataset, which is good to get started on this topic. We are going to use one of the simplest machine learning algorithms,

 binary trees

.

Binary trees as a simple approach to image classification.

Table of Contents In this series of posts I will introduce one of the simplest, yet very powerful statistical models,

 linear regression

. We’ll start with some theory, then move on to a few practical examples in R. The basic theory

 Not interested in the maths? Skip to the examples!

Linear regression is part of the larger family of statistical models called linear models.

Linear regression in R &#8211; part 1

Welcome to my blog! Here is where I collect my thoughts and materials about all things data analysis! Some of these thoughts come from my teaching material, some are just things I find interesting! Interested in something in particular? Just leave me a comment and I will see whether I can write about it!

Welcome to my blog! Here is where I collect my thoughts and materials about all things data analysis!  Some of these thoughts come from my teaching material, some are just things I find interesting!  Interested in something in particular? Just leave me a comment and I will see whether I can write about it!

Hello!

In previous posts, we have looked at the theory of what is a neural network and how the training process works. Today we are going to put  that into practice and build our own neural network using Python! Table of Contents Requirements   For the example in this post, I will be using Python 3.10. We will build a neural network using keras from the tensorflow package. TensorFlow is one of the most used solutions for building neural networks.

Build a simple neural network in Python!

This is the first of a series of posts where I will introduce you to

 neural networks

. Even if you do not know how they work, you have probably heard of neural networks in the news as something used for

 artificial intelligence

. Many areas, such as image recognition, natural language processing, fraud detection and many more (biology included!), have taken incredibly huge steps forward in the last few decades thanks to

A basic introduction to neural networks &#8211; Part 1

In the previous post, we introduced neural networks and described the forward pass, the process of going from the inputs to the output(s) of the ANN. If you remember, we perform a weighted sum of the inputs (plus the bias) and pass it through an activation function. The question remained of how we decide which parameters (weights and biases) to use for our network.

A basic introduction to neural networks &#8211; Part 2: Training

Table of Contents The second part of this series of posts will tackle multiple regression, that is, linear regression with more than one predictor. If you are not familiar with simple linear regression and have not read

 part 1

be sure to check that out first!

Linear regression in R &#8211; a step-by-step guide (part 2)

In a previous post, I discussed how to use linear regression to analyse whether one or more variables influence an outcome. Linear regression is great, but it is not always the best solution; one of the extremely important assumptions of linear regression is that the observations are

 independent

. However, there are a lot of situations when that does not apply.

A gentle introduction to mixed-effects models

Introduction In previous posts, I discussed how to deal with situations where you measure a continuous outcome and you want to explain its variability as a function of one or more continuous or discrete variables, using linear regression or mixed-effects models.

Table of Contents Introduction  In previous posts, I discussed how to deal with situations where you measure a continuous outcome and you want to explain its variability as a function of one or more continuous or discrete variables, using linear regression or mixed-effects models. However, there are situations where these linear models are not the best solution to use.

GLM for the analysis of binary outcomes

In the previous two posts we saw how to use Scikit Image to perform some basic image processing. This post will introduce you to image segmentation, one of the most important steps in image analysis. I am going to cover some of the “traditional” methods that work well in many situations.

In the previous two posts we saw how to use Scikit Image to perform some basic image processing. This post will introduce you to image segmentation, one of the most important steps in image analysis. I am going to cover some of the “traditional” methods that work well in many situations. These methods require adaptation (or don’t work at all!) for more complex cases… but that is maybe a story for another post!

Easy methods for segmentation of biological images.

In this post we will introduce the concept of statistical power and we are going to use R to perform a power analysis through simulation.

 I know what power analysis is! Just show me the code!

Table of Contents Introduction – what is power? In science, we often want to assess the effect of a certain factor or set of factors on an outcome.

Nicola Romanò

Create awesome plots with ggplot!

Linear regression in R – a step-by-step guide (part 2)

Linear regression in R – part 1

Hello!