Onnicha Alice Lee

'Alice' curiouser & curiouser Interesting: Gut microbiome/Cancer/Immunology

[Shruti @heyshrutimishra]

MIT University just released free online courses.

No payment required.

Here are 10 courses you don't want to miss in 2024:

[R Markdown @rmarkdown]

📊 Explore "Data Visualization: A Practical Introduction" by Kieran Healy (@kjhealy) from @PrincetonUPress. 🚀 Unlock #RStats to transform data into engaging stories. 📘 🔗 https://t.co/1zy53iI3ux via @RosanaFerrero #DataViz #PrincetonUPress #RStudio #DataVisualization #DataScience #DataAnalytics #Stats #Data #Python #Rmarkdown #QuartoPub

[NTU Antimicrobial Resistance, Omics and Microbiota @AROM_NTU]

New book chapter from Professor Lesley Hoyles on the gut microbiome-liver axis in context of fatty liver disease.

Gut Microbiome and Hepatic Steatosis (Steatotic Liver Disease) https://t.co/p9usjYsipJ

[Chenxin Li, PhD (@chenxinli2.bsky.social) @ChenxinLi2]

I learned how to use ggtreeExtra this week for phylogeny visualization and annotation.

ggtree: https://t.co/WsoPckbCBQ
ggtreeExtra: https://t.co/YXeP0IfYbm

[Onnicha Alice Lee @OnnichaEls]

Finally, published!
Our study about soil microbiome in periodic waterlogging in Thailand using 16S and ITS to determine the effect of soil microbiome and its further effect on sugarcane growth.
https://t.co/35ufCv8Wwu

[GutMicrobiota Health @GMFHx]

Could bacterial single-nucleotide polymorphisms improve understanding of interpersonal microbiome differences?

.@Elinav_Lab & colleagues demonstrate the importance of considering nucleotide-level diversity in microbiome studies:

https://t.co/KAb8vbH0HZ

[R Markdown @rmarkdown]

ggplot2 themes for publication ready plots including dark themes by @koundy87 https://t.co/WpYiWpWd6Y #rstats

[GutMicrobiota Health @GMFHx]

Microorganisms may explain some of the health benefits of fermented foods.

.@LiamHWalsh1, @pauldcotter & colleagues defined the milk kefir pan-metagenome through a metagenomics-based approach via @iScience_CP:

https://t.co/FCx0iSIQ2a

[GutMicrobiota Health @GMFHx]

Consequences of stress manifest in the gastrointestinal tract.

.@SarahJane_Leigh, @jfcryan et al. update how acute and prolonged stress exposures evoke changes to the enteric nervous system, gut motility, gut barrier function and the gut microbiome:

https://t.co/5klJWpNBlk

[Dr. Verónica Espinoza @Verukita1]

❤Are you going to illustrate your presentations? I share these open access tools with you!

#AcademicTwitter #DataVisualization #PhD #postdoc #opensource #machinelearning #DataScience #AI #data #chemistry #medicine #neuroscience #Bioinformatics #Biology

[GutMicrobiota Health @GMFHx]

The gut microbiota changes across the gastrointestinal tract.

McCallum & @CTropini explore biogeographical features of the digestive tract at the scale of the whole gut via @NatureRevMicro:

https://t.co/oJh8GwIWxM

[Bioinformatics Advances @BioinfoAdv]

CCPlotR: an R package for the visualization of cell–cell interactions by @sarahe145, @pilib_, @ESzegezdi https://t.co/WT7MK6h8eR CCPlotR is available to download and install from GitHub (https://t.co/K6pPLxAH4G)🇮🇪

[Nature Biotechnology @NatureBiotech]

Identification of mobile genetic elements with geNomad https://t.co/ST2NnmQYWA

[Kareem Carr, Ph.D. @kareem_carr]

Every Data Scientist needs to know these ideas.

They will blow your mind.

1. Correlation vs Causation

P(A | B) is the probability of A given B. It is the probability that we will observe A given that we have already observed B.

P(A | do(B)) is the probability of A given do(B). It is the probability that we will observe A given that we have intervened to cause B to happen.

In this context, an intervention simply means to take an action of some kind. Therefore do(B) means to take an action which causes B to happen.

The expressions P(A | B) and P(A | do(B)) might seem very similar but they represent very different situations.

2. We can only learn P(A|B) from the data alone.

Bob has an extremely accurate weather app and is always very good about bringing his umbrella when it rains. We observe Bob over several years and we find that whenever it rains, Bob always has his umbrella and he never brings his umbrellas on days when it doesn't rain.

In the language of probability, we say P(Umbrella | Rain) = 1 and P(Rain | Umbrella) = 1 as well.

What we can learn from this data alone is how to predict whether it rains with a 100% accuracy by checking whether Bob has an umbrella. We can also learn to predict with 100% accuracy whether Bob has an umbrella by checking if it's going to rain.

What we cannot learn is what will happen if we give Bob an umbrella on a random day of our choosing. The answer to this question is P(Rain | do(Umbrella) ) and it's unknowable from the data alone.

We need prior knowledge about how the world works to properly interpret the data we collected. We need to know that rain has an effect on Bob's behavior, but Bob's behavior has no effect on the rain.

Information about the effects of interventions are simply not available in raw data unless it is collected by controlled experimental manipulation.

3. Scientific Experiments work because they produce a very special kind of data.

You may have heard of what many people call a scientific experiment. Take a collection of objects, animals or people. Randomly split that collection into a control group and a treatment group. Apply your intervention to the treatment group while leaving the control group alone. If you observe any differences between the treatment group and the control group, it is logical to attribute these differences to the treatment. You can therefore say the differences were caused by the treatment.

In statistics, the procedure I just described is called a Randomized Controlled Trial. It is a procedure for generating a specific kind of data where:

P(Difference | Treatment) = P(Difference | do(Treatment) )

This is why traditional science experiments work. They are designed to capture causal information. This is not the case for vast majority of data that we collect in society.

Without human guidance or access to real world knowledge, statistical algorithms and artificial intelligences can only learn P(A | B) from the raw data. This is a fundamental mathematical limitation on the use of data alone.

That's it for now. This post is part of a series of posts about the concept of causal inference. They are based on the content of the Book of Why by Judea Pearl with lots of commentary from me.

Follow me (@kareem_carr) so you don't miss out on the next post.

Please show support by liking and retweeting the thread.

[Don Whitley Scientific @dw_scientific]

The DWS Travel Grant recently allowed @OnnichaEls to give an oral presentation on her work at the Anaerobe 2023 meeting in Cardiff!

Read about the work presented here ~ https://t.co/b99q7PVEuK

#DWSTravelGrant @NTUBIOSCIENCES #PhD #Anaerobe23 #GutMicrobiome #GutHealth

[R Markdown @rmarkdown]

Exploring and Understanding Complex Data Sets with Cluster Analysis in R by Vicky https://t.co/bORkiXZ6uq #rstats

[Loukas Theodosiou @loukesio]

Excited to introduce #ggvloc! 🎉 For everyone working with differential expression datasets & RNAseq data, this can be a game-changer. Visualize & highlight genes of interest in just one line of code
Check out the package here:https://t.co/AJl9sNE5C8. Don't forget to give it a ⭐

[Onnicha Alice Lee @OnnichaEls]

@BugsInYourGuts Ohhh can I say no 😅 but I can make a barbie cover for my thesis book, if you allowed!!!

[Onnicha Alice Lee @OnnichaEls]

Went to Global student lounge and found this. Don't know who wrote this but you made my day and I will remind myself every time I think about it.