Differential Gene Expression Analysis in R with DESeq2| Bioinformatics Tutorial for Beginners
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- Опубликовано: 8 сен 2024
- Differential Gene Expression Analysis in R with DESeq | Bioinformatics for Beginners| Bioinformatics Tutorial| Gene Expression Analysis using Deseq2
Description:
Welcome to our comprehensive tutorial on performing differential gene expression analysis using R and DESeq2 for RNA sequencing data! In this video, we'll guide you through the entire process, from data preprocessing to advanced visualization techniques.
🔍 Here's what you'll learn in this tutorial:
1️⃣ Data Import and Preprocessing: We'll start by loading your RNA-seq data into R and performing essential data preprocessing steps, ensuring that your data is ready for analysis.
2️⃣ MA Plot: You'll discover how to create MA plots, a valuable tool for visualizing the distribution of gene expression values and identifying differentially expressed genes.
3️⃣ Dispersion Plot: We'll dive into dispersion plots to assess the variability of gene expression across samples and ensure the reliability of your analysis.
4️⃣ Principal Component Analysis (PCA): Learn how to perform PCA to reduce the dimensionality of your data and uncover underlying patterns, helping you identify potential outliers and clusters.
5️⃣ Volcano Plot: Uncover the secrets of volcano plots, a powerful visualization method for highlighting significantly differentially expressed genes while controlling for false positives.
6️⃣ Heatmap Plot: Finally, we'll create heatmaps to visualize gene expression patterns across samples and clusters, providing insights into gene co-expression and functional enrichment.
Whether you're new to RNA-seq analysis or looking to enhance your skills, this video will equip you with the knowledge and tools you need to effectively analyze and visualize your RNA sequencing data with DESeq2 in R.
Don't forget to like, subscribe, and hit the notification bell to stay updated with more exciting data analysis tutorials and tips. Let's dive into the world of differential gene expression analysis together! 🧬📊📈
#mrbioinformatix #bioinformatics #DESeq2 #differentialgeneexpressionanalysis #normalization #rna #ncbi #genomics #bioinformaticsforbeginners #tutorial #howto #omics #research #ngs #geneexpression #dna #dnaanalysis #dnasequencing
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I was able to analyze my firsts results with this script. You're a crack, thank you so much for sharing such a good material with all us mortals and even teach use why and how to use everything.
My questions is if it's possible to compare 3 treatments at the same time
Thank you for your kind words, I am glad that you find the video useful 😊 yes you can. To compare a control group with multiple treatment groups, follow these steps:
1- Set Up colData: Ensure your colData includes a factor for your treatment groups, with your control group as a reference.
colData
@@MrBioinformatiX Omg 😱. Thank you for such a fast reply☺️
I am a Master's student. Thank you a lot for making this useful video
Thank you for your kind words! I'm happy that you found the video useful. If there's any particular topic you'd like to see covered in future videos or if you have any questions about your studies, please don't hesitate to suggest. Your input is valuable in shaping the content to better suit your needs. Good luck with your master's studies 💪🏻
This video was super helpful. Thank you so much!
You're welcome, thank you for your kind words 😊 I am glad that you found the video helpful 😊
Can u give thr online training,
Hello, I used HISAT for aligment and stringtie to assemble my transcripts to create .gtf files. Can I use DESeq2 over Ballgown in R?
2nd Year PhD Behavioral Neuroscience student here... this video was phenomenal thank you!!! I just got some tag seq data back and was so lost
When it comes to the volcano plots, do you know how I might be able to 're-label' the gene ID's to the actual gene's themselves?
Thank you for your kind words! I'm delighted that you found the video helpful and I wish you the best in your PhD. If you're looking to convert gene IDs to gene symbols in R, I've created a video where I explain 3 different methods to do just that. You can check it out here:
ruclips.net/video/xYUVk1BThTc/видео.htmlsi=6EMxw-RYrS3BJh8h
I hope you find it useful for your data analysis!
Happy Analyzing!
Mr. BioinformatiX
good video
@@gianmarcocastillohuaccho244 Thank you ❤️
Can you please share the code?
Why
I'm about to complete my Masters, and this video has been a great help in my project. Could you please provide guidance on how to rearrange the input columns when presenting them in the graph?
[for example, in this video @26:48 (Heatmap), can I change the order of arrangement as Control 1,2,3, and Treatment 1,2,3]. Is there any way or code to do it?
Thank you so much for engaging and your kind words! I'm glad the video has been helpful for your project. Here is the R code to rearrange the columns in your heatmap to display control samples first followed by treatment samples, and vice versa.
# Scenario 1: Control samples first, then treatment samples
# Specify the new order of columns
new_order_control_first
Hey, I am a masters student at DIT. Have you covered the course bioinformatics Algorithms and Data Structure?
Hey, I wish you good luck in your master studies 😊 I finished all the master courses and graduated 😊
Hello! I am trying to use DESEQ2 as per this video but not able to because of the package installation in new version. Can you suggest me the version that would work for this analysis?
Hello, Thank you so much for engaging with us 😊 for me I used DESeq2 version 1.36.0 in this video, generally sometimes an error happens because we should open R studio with run as administrator, so try to open R with run as administrator, you can also check the version that you installed by running this code :
packageVersion("DESeq2")
Try now and tell me what happened and what kind of error you get or no error but the code does not work ?
Hello! thank you very much for this video, it is very very useful. I have a question about the pre-filtering step: do I have to add the Gene_id column when I create coldata? because I understand that the dimensions of count_new_data and coldata have to match, but I was thinking that maybe you exclude it from the calculation. =)
Hello
Thank you for your kind words :) I'm glad you found the video useful. Regarding your question about the pre-filtering step in DESeq2 analysis, you don't necessarily need to add the Gene_id column to the coldata. The dimensions of count data and coldata need to match for DESeq2 analysis, but the Gene_id column typically isn't included in the coldata because it's not a sample-specific attribute. It's usually part of the count data or metadata associated with the counts. So, you can exclude the Gene_id column from the calculation, and DESeq2 will still work correctly as long as the count data and coldata match in terms of samples. Let me know if you need further clarification! 😊
The video is very informative and appreciated. But I'm still confused about normalizing after DESeq() runs. Can't I use dds(dds
Thank you for your positive feedback on the video! I'm delighted to hear that you found it informative.
Regarding your question about normalization and the creation of a normalization file, let's delve into it further:
1- Normalization in DESeq2: As you rightly pointed out, DESeq2 automatically handles normalization during the DESeq() step. This internal normalization ensures that differences in library sizes between samples are appropriately accounted for in the differential expression analysis.
2- Creating a Normalization File for Downstream Analyses: While DESeq2 performs normalization internally, having a separate normalization file can be beneficial for downstream analyses, such as Gene Set Enrichment Analysis (GSEA). These analyses often require normalized expression values as input, and having a pre-prepared normalization file streamlines the process.
In summary, while DESeq2 handles normalization automatically, creating a normalization file anticipates the need for normalized expression values in downstream analyses, making it easier to conduct additional analyses beyond differential expression.
I hope this explanation provides clarity on the purpose of creating a normalization file. If you have any more questions or need further explanation, feel free to ask.
Thank you again for engaging in the video
Mr. BioinformatiX
@@MrBioinformatiXThank you very much for your response😀!
Sir,i am full of other background, but i joining phd in bioinformatics. Guide alloted me the NGS sequencing. I am feeling very difficult to follow,little bit understood, after i was collapsed.For that,m in depressed state
Can u please give me the online training
@@Paachi8651 Thank you for reaching out, and congratulations on starting your PhD! I understand how challenging it can be to dive into a new field like bioinformatics. Don’t worry, everyone feels this way at first. Start little by little, and remember, I’m here to support you, so don’t hesitate to ask if you have any questions. You’re doing great 🤞🏻☘️
@@MrBioinformatiX Thank u
Hey amazing video its really helpful. I have a question i need to define groups which has 1 control group and 2 treatment groups m each group has 2 replicates. How do i define the groups? I have 0 coding experience.
Also my files are with the extension .bam can i use these itself or do i need to convert them to count data files?
Thank you
Thank you for engaging, I'm glad you found the video helpful!. Since you have one control group and two treatment groups, each with two replicates, here's how you can define the groups in R using DESeq2:
# Load DESeq2 library
library(DESeq2)
# Load count data
countData
@@MrBioinformatiX thank you so much💞. I'll try it.
@@mitalimishra1361 You're welcome 😊
Thank you very much for sharing the helpful video. However, I want know if I need to use normalized data including with genename in the table; how can I do?
Thank you for your kind words, I am happy that you like the video :) If you want to use normalized data with gene names in a table, then:
1- Retrieve normalized data:
Extract normalized counts from the DESeqDataSet object using counts() function.
2- Then, add gene names:
Include gene names as a column in your table. You can extract gene names from the row names of the DESeqDataSet.
3- Finally, create a table:
Combine gene names and normalized counts into a table using functions like data.frame() or cbind()
Here's how to perform it on R:
# Assuming "dds" is your DESeqDataSet
normalized_counts
Thank you very much sir@@MrBioinformatiX
@@OuanhPhomvisith You are always welcome in BioinformatiX :)
Once again sir. I did try but the result came with the gene order number (e.g: 1, 2, 3........) only, but the gene names are not included; so, please teach me again@@MrBioinformatiX
@@OuanhPhomvisithI think the gene names are stored in a different column. If that's the case, you should replace rownames(dds) with the appropriate column name that contains the gene names in your DESeqDataSet. For example, if the gene names are in a column called "GeneID" in your DESeqDataSet, you would modify the code like this:
# Assuming "dds" is your DESeqDataSet
normalized_counts
Should the duplicated gene IDs be zero?
Ensuring there are no duplicated genes in the count matrix during DESeq2 analysis is crucial because DESeq2 assumes a one-to-one relationship between genes and rows. Duplicated genes can violate this assumption, leading to biased statistical modeling, inaccurate results, and difficulties in result interpretation. Preprocessing the data to handle duplicated genes appropriately helps maintain the accuracy of the analysis.
I always get this:/ Error in library(DESeq2) : there is no package called ‘DESeq2’
and my software is updated
If you're encountering an issue where there is no package called DESeq2, it's possible that the package hasn't been installed in your software environment or it may need to be updated.
To install DESeq2:
install.packages("DESeq2")
To update DESeq2 (if it's already installed):
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("DESeq2")
@@MrBioinformatiX now it worked, thank you so much!
@@gulsum9771 you are welcome 😊 That's great to hear! I'm glad the solution worked for you. If you have any more questions or need further assistance, feel free to ask
@@MrBioinformatiX i will be doing this, many thanks!!!
@@MrBioinformatiX i will be doing this, many thanks!!!