- 29
- 253 077
Biostatsquid
Spain
Registrace 3. 11. 2022
Welcome to Biostatsquid!
In this channel, you will find clear explanations and hands-on tutorials for analysing and interpreting biological data.
- Learn basic techniques in biostatistics, from heatmaps to pathway enrichment analysis.
- Manage your data and create publication-ready visualisations by following my easy step-by-step tutorials in R and Python.
- Practical explanations without complex formulas for you to understand your datasets and be able to interpret your results.
Are you ready to dive in? Don't miss any other video and hit that subscribe button!
In this channel, you will find clear explanations and hands-on tutorials for analysing and interpreting biological data.
- Learn basic techniques in biostatistics, from heatmaps to pathway enrichment analysis.
- Manage your data and create publication-ready visualisations by following my easy step-by-step tutorials in R and Python.
- Practical explanations without complex formulas for you to understand your datasets and be able to interpret your results.
Are you ready to dive in? Don't miss any other video and hit that subscribe button!
Violin plots tutorial with ggplot2 in R (part 2)
In this tutorial I will explain how to create and customise your own violin plots in R. In particular, we will cover facet_wrap, facet_grid, and how to create your own violin plot function. For this tutorial, I’ll be using RStudio, and you’ll need the package ggplot2.
You will learn how to:
- plot a violin plot in R
- customise and edit labels, colours, themes
- plot grouped violin plots
- and more!
Check out part 1 here:
czcams.com/video/5zDA9EdJa-0/video.html
And as always, you can find the code I am using in this tutorial at biostatsquid.com, where you can also find a step by step explanation of the code. For this tutorial you will need R, or Rstudio, and you will need to install the package listed above.
Hope you like it!
biostatsquid.com/easy-violin-plots-tutorial-ggplot2/
--------------------------------------------------------------------------------------------------------------------
Watched it already?
If you liked this video or found it useful, please let me know! Your comments and feedback are very much appreciated😊
If you have questions, don't hesitate to leave me a comment down below, I will answer as soon as I can:)
If you like my content and would like to support me (in a more capitalistic manner), you might consider buying me a coffee.
www.buymeacoffee.com/biostatsquid
Big thanks for your support!
--------------------------------------------------------------------------------------------------------------------
Are you into biostatistics and computational analysis?
For more biostatistics tools and resources, you can visit:
biostatsquid.com/
Follow me on Instagram at @biostatsquid for more
• simple and clear explanations of biostatistics methods
• computational biology tools
• easy step-by-step tutorials in R and Python
to analyse and visualise your biological data!
Don’t forget to subscribe if you don’t want to miss another video from me!
You will learn how to:
- plot a violin plot in R
- customise and edit labels, colours, themes
- plot grouped violin plots
- and more!
Check out part 1 here:
czcams.com/video/5zDA9EdJa-0/video.html
And as always, you can find the code I am using in this tutorial at biostatsquid.com, where you can also find a step by step explanation of the code. For this tutorial you will need R, or Rstudio, and you will need to install the package listed above.
Hope you like it!
biostatsquid.com/easy-violin-plots-tutorial-ggplot2/
--------------------------------------------------------------------------------------------------------------------
Watched it already?
If you liked this video or found it useful, please let me know! Your comments and feedback are very much appreciated😊
If you have questions, don't hesitate to leave me a comment down below, I will answer as soon as I can:)
If you like my content and would like to support me (in a more capitalistic manner), you might consider buying me a coffee.
www.buymeacoffee.com/biostatsquid
Big thanks for your support!
--------------------------------------------------------------------------------------------------------------------
Are you into biostatistics and computational analysis?
For more biostatistics tools and resources, you can visit:
biostatsquid.com/
Follow me on Instagram at @biostatsquid for more
• simple and clear explanations of biostatistics methods
• computational biology tools
• easy step-by-step tutorials in R and Python
to analyse and visualise your biological data!
Don’t forget to subscribe if you don’t want to miss another video from me!
zhlédnutí: 64
Video
Violin plots tutorial with ggplot2 in R (part 1)
zhlédnutí 155Před 23 dny
In this tutorial I will explain how to create and customise your own violin plots in R. For this tutorial, I’ll be using RStudio, and you’ll need the package ggplot2. You will learn how to: - plot a violin plot in R - customise and edit labels, colours, themes - plot grouped violin plots - and more! And as always, you can find the code I am using in this tutorial at biostatsquid.com, where you ...
EASY violin plots and boxplots - simple explanation with examples
zhlédnutí 219Před měsícem
In this video, we will discuss the main concepts behind violin plots and boxplots - easily explained! We will go through what are violin plots and boxplots and how to interpret it and use it to visualise our biological data. And as always, you can find the full explanation at biostatsquid.com: biostatsquid.com/interpret-density-plots/ Hope you like it! Watched it already? If you liked this vide...
How to interpret density plots - simple explanation with examples!
zhlédnutí 690Před 2 měsíci
In this video, we will discuss the main concepts behind density plots - easily explained! We will go through what is a density plot and how to interpret it and use it to visualise our biological data. And as always, you can find the full explanation at biostatsquid.com: biostatsquid.com/interpret-density-plots/ Hope you like it! Watched it already? If you liked this video or found it useful, pl...
Logistic regression - easily explained with an example!
zhlédnutí 782Před 3 měsíci
In this video, we will discuss the main concepts behind Logistic regression - easily explained! We will go through what is logistic regression, when to use it and how to interpret the coefficients. And as always, you can find the full explanation at biostatsquid.com Hope you like it! biostatsquid.com/easy-logistic-regression/ Watched it already? If you liked this video or found it useful, pleas...
SingleR EASY TUTORIAL: step-by-step cell type annotation in R
zhlédnutí 872Před 4 měsíci
In this tutorial I will explain how to do cell type annotation with the R package SingleR. After a brief introduction to reference-based automatic cell type annotation and SingleR, we will go step by step through the workflow, including preparing our input data, running SingleR, interpreting the results and some tips and tricks to get the most out of SingleR. For this tutorial, I’ll be using RS...
COMPLETE SURVIVAL ANALYSIS tutorial in R: Kaplan-Meier, Cox regression, Forest Plots...
zhlédnutí 4,7KPřed 7 měsíci
In this tutorial, I will explain how to perform survival analysis in R, including log rank test, Cox regression, Kaplan-Meier curves, and more! We will use the R packages ggsurvplot, survminer and survival. You will learn how to: - plot a Kaplan Meier curve - test for differences between groups using the log rank test - build a survival model with Cox regression - and visualise your results wit...
COX REGRESSION and HAZARD RATIOS - easily explained with an example!
zhlédnutí 13KPřed 7 měsíci
In this video, we will discuss the main concepts behind Cox regression for survival time analysis - easily explained! We will go through hazard ratios, coefficients, p-values and confidence intervals. I will also give you simple and practical guidelines on how to interpret the results from Cox regression, with an example! And as always, you can find the full explanation at biostatsquid.com Hope...
LOG RANK TEST for survival analysis - easily explained with an example!
zhlédnutí 5KPřed 8 měsíci
In this video, we will discuss the main concepts behind the Log Rank Test - easily explained! I will also give you simple and practical guidelines on how to interpret the results from the Log Rank test And as always, you can find the full explanation at biostatsquid.com Hope you like it! biostatsquid.com/easy-log-rank-test/ Watched it already? If you liked this video or found it useful, please ...
How to interpret KAPLAN-MEIER curves - Easily explained!
zhlédnutí 10KPřed 8 měsíci
In this video, we will discuss the main concepts behind Kaplan-Meier curves- easily explained! I will also give you simple and practical guidelines on how to interpret a Kaplan-Meier curve. And as always, you can find the full explanation at biostatsquid.com Hope you like it! biostatsquid.com/kaplan-meier-curve/ Watched it already? If you liked this video or found it useful, please let me know!...
Easy survival analysis - simple introduction with an example!
zhlédnutí 1,6KPřed 8 měsíci
In this video, we will discuss the main concepts behind survival time analysis - easily explained! Survival time analysis is really common in biostatistics. You might have heard of Kaplan-Meier curves, Cox regressions or the log rank test. In clinical trials, survival time analysis is used to compare the performance of two different kinds of treatment, for example. Survival time analysis can al...
Top tips to create pretty plots in R (ggplot2)
zhlédnutí 1KPřed 9 měsíci
In this tutorial, you'll find some of the best tips and tricks I use to create pretty and publication-ready plots with ggplot2 and more! You will find out what are the top visualisation tricks you should know in R. And as always, you can find the code I am using in this tutorial at biostatsquid.com, where you can also find a step by step explanation of the code. For this tutorial you will need ...
Gene Set Enrichment Analysis (GSEA) with fgsea - easy R tutorial
zhlédnutí 7KPřed 10 měsíci
In this tutorial, I will explain how to perform gene set enrichment analysis on your differential gene expression analysis results. We will use the R package fgsea() and you will learn how to: - Install and start fgsea() - Prepare your dataset to perform GSEA - Set the analysis parameters and run the analysis. - View the GSEA results and get some nice plots! And as always, you can find the code...
Pathway Enrichment Analysis plots: easy R tutorial
zhlédnutí 7KPřed 11 měsíci
In this tutorial, I will explain how to create pretty plots to visualise your pathway enrichment analysis results. This Part 2 of my R tutorial series on Pathway Enrichment Analysis. Check out Part 1 for a step-by-step tutorial on performing PEA analysis with clusterProfiler(): czcams.com/video/4MZ2fEvTj0c/video.html And as always, you can find the code I am using in this tutorial at biostatsqu...
Pathway enrichment analysis tutorial in R with clusterProfiler()
zhlédnutí 12KPřed 11 měsíci
In this tutorial, I will explain how to perform pathway enrichment analysis on your differential gene expression analysis results. We will use the R package clusterProfiler() and you will learn how to: - Download and use gene sets in the context of PEA - Prepare your differential gene expression results for PEA - Perform functional enrichment analysis with clusterProfiler() - and interpret the ...
Thank you so much for this video! I have a question regarding the forest plot of the cox regression, can we add the global p-value (summary) to the forest plot? is there any way? I would appreciate your help with this!
Hey! Thanks for your comment, I'm glad it was useful:) The global p-value should be already there, in the bottom of the plot. If you'd like it somewhere else, you can easily extract it from the object as a variable (assign it to gloabl_p_val or similar), and then use annotate() as you would to annotate a ggplot object! Hope this helps:)
Very informative session 👍🏻
Please say loudly
I am a bit confused by the hazard ratio. It seems like its group A is HR times as like to die as group B. So in the smoking example where smoking had a hazard ratio of 7.4. I took non_smokers as 0 being group A and smokers as 1 being group B. Would this mean that non-smokers were 7.4 times as likely to die compared to smokers?
Thanks for your question! The positive HR for smoking means that there is an increase in the hazard for the smoking group compared to the control (non-smoker group) at any given time. Is this what you were asking? As a sidenote: Hazard ratios are a bit different to relative risk - the HR accounts for also the timing of the event (death), whereas the relative risk only checks if it happened or not. An HR = 1 indicates no change in the hazard (probability of death given that you have survived up to a specific time), if HR > 1 it's increased, and if HR < 1 it's decreased. But this does not translate directly to "7.4 times more likely to die", because it's a ratio, not a probability. To get the probability you can use this equation P = HR/(1 + HR). So for example, a hazard ratio of 2 means there's a 67% chance of the smoking group dying first, and a hazard ratio of 3 corresponds to a 75% chance of dying first. A HR of 6.7 means there's an 87% chance a smokers will die before a non-smoker at any given time. Does this make sense? This paper is really useful in case you want to read more about it: www.ncbi.nlm.nih.gov/pmc/articles/PMC478551/
@@biostatsquid Ahhh I think I was not thinking of things in terms of a group vs control, but was thinking of it in terms of the first group and second group which doesnt make as much sense. Lmao also it being called a ratio should make it obvious to me that it is a ratio and not a probability. I appreciate the clarification, this makes a ton more sense now. Time to finish running this cox-prop model on my GBM survival data. Fingers crossed this paper gets out by Oct T-T
The P value for the red smarties still says P > 0.05 (1:28), whereas it should be P < 0.05. Same for 2:12.
Amazing work
Here's part 1: czcams.com/video/5zDA9EdJa-0/video.html
A very clear and engaging introduction to PCA. It was new to me, and I came away with a good impression of how it would be used. Thanks very much!😀
The differential data that you loaded in the r script initially, which has approx 30 thousand something genes and four variables, are they pre-processed data, like removing the duplicates and adjusting the p values and log FC?? Or are they raw data tT saved from r script?
Hi there. Your videos are really helpful. Can you make a video on RNA sequencing profile?
Hi, thanks for your feedback! What do you mean by profile? single-cell or bulk?
This is really helpful , thank you!
Hi Dear Biostatsquid, can you please check out Plotnine in Python too? It provides a great visualization for statistical outputs. 😊
Here's the link to the step-by-step tutorial: biostatsquid.com/easy-violin-plots-tutorial-ggplot2/
Amazing. Thank you
Hi. This is a nice video. I am new to data visualisation and I find it very complex as to how to memorise the code or understand how to use it with various datasets. Could you please share some tips on how you do that?
Hi, thanks so much for your comment! My recommendation is... don't memorise code! You'll end up remembering the most common functions and bits and pieces anyway if you use them a lot - but a lot of bioinformatics is just googling:) As for what to use in which case and with which data... honestly, it comes with practice. Seeing and reading what other people do with similar problems / datasets definitely helps, e.g., from publications, tools, github repos... if you encounter a problem, odds are someone already did too! And probably solved it:) Good luck, you'll see how it gets easier the more you do it! Just have fun with it:)
Your videos are great and very easy to follow. For the background genes, how do you download GSEA GMT files for only genes expressed in the specific tissue you are interested in.
Thanks so much for your feedback! Hmm as far as I know, you cannot do that. But you can download the full .gmt file and then just filter it for all of the genes you detected in your tissue.
This was very helpful, your way of teach just keep me engaged and understanding, thanks ❤
Thank you for a very nice video. I have trouble understanding the fold change for gene 1 in the table example. Wouldn't the fold change (FC) be 3 (9 divided by 3) and log2(FC) 1.585?
Yes, apologies, that was a typo! You are correct:)
Tus videos me estan ayudando muchisimo!!! Sigue asi!!
Could you please make a video on DEWSeq or any other tool to analyse the eCLIP data to find the motifs in rna through which it is bound to a protein
Absolutely amazing! Thank you for doing this! Great job
Your accent is very good. Thank you!
This is great 👍, it was well explained.
Thank you, very useful !
Eres la mejor!! Saludos desde Colombia :)
Can you do a video for pathway enrichment analysis using pathfindR package in R
Whole world creator's godfather bless you all always and you all love and remember godfather with your pure hearts.
Thank you for explaining it well.. Can you pls provide information on the inputs needed to perform ssGSEA ...
Thank you for this amazing video!
How to explain which factors contribute to PC1 and PC2? by biplot graph.
This was such an informative video! Helped explain so much for me as I have never been exposed to Volcano plots before. Will definitely be tuning in more for more videos! Thank you.
I'm currently watching without logging into my Google account. 😊 However, halfway through, I made the decision to log in, hit the like button, and subscribe to your channel. 🎉 Thank you for your valuable content-it's truly helpful, and I encourage you to keep up the great work! 👍
Thank you so much!!
Thank you for a very clear explanation
Thanks for uploading the valuable video. I could not install the Rqc and QuasQ packages in R 4.3.2. Do you think I should use a lower version?
Hi thank you so much for explaining PCA in such a clear way. I've been really stressed about understanding it for my uni stats exam, but now I feel much more confident :)
nice explanation
Thank you a lot! I'm struggling with my data. is there any option to create a clustering within a group on the same heatmap? I have many groups of species I want to analyze but I just want the clustering only within the same group.
thank you very much , that was very informative and joy to watch .
I am with zero experience, and failed so many times by following youtubers, you script works and I can easily catch up, even different methods. Thankyou sooooooooomuch.
How I can do interrogating the sample PBMC clusters for the following genes : CD68 CD45 Sox10 CD44 any similar video will be great ? thank you
Super helpful video! Please make more videos with easy explanations for basic concepts in this field.
Thanks for the wonderful video! I'm interested in marker-based method. Hope to see the video soon!!:)
Many thanks for this video. It was extremely helpful! Just a quick question, do you have a link to any papers that use the same method for ranking genes? I've gone for the same approach, but will need to defend it in my viva and I am struggling to find publications using this method. Secondly, I just want to confirm that you use regular p-values rather than adjusted p-values for the ranking calculation?
VERY INFORMATIVE VIDEO, THANKS A LOT IT MADE MY LIFR EASIER
Very good high level video!
Thank you for the clear explanation!! Great help!! Looking forward to upcoming videos:)
you explain so well!! thank you
this video helped me so much!!!!!
Please can u tell me how can we calculate principal loading. I am a bit confused to this part.