wow, you made it like a promenade in the park on a nice spring day. Thanks. Plz never stop making these videos, you are a true prophet!

I very much appreciate that you gave a very clear and concise explanation of the workflow of DESeq2. I've learned a lot from it

this is my first time using DESeq2 and your explanations and demonstrations in this video were amazing! tysm

Thank you for an excellent and pedagogical video on how to operate DESeq2! I had some initial issues, as I had to replace the row numbers with my gene symbols (which where in their own column). But once I had that figured out, for both files (counts and coldata), everything worked smoothly. If someone has the same issue, use this script (for colData; similar process for counts): DF

Thanks for your tutorial! It was clear, concise and extremely helpful.

Your tutorials are some of the best on you tube for sure! Well done and thank you so much!

For an Indian PhD student like me (who is not familiar with Bio Info) this channel is a blessing . I will share it with my batchmates. Very nice youTube channel. Keep it up.

@Bioinformagician

2 жыл бұрын

Thank you Arpan, I am glad you find my videos helpful! :)

@AbdullahSharabati

Жыл бұрын

So, even Indians needed tutorials? Sorry it's just a bad joke, please don't mind me, all respect. :)

@animatedbiologywitharpan

Жыл бұрын

@@AbdullahSharabati Yes why not we all need help. I actually learn from her channel quite frequently. As Indians, we have developed a culture of Peer Learning.

@AbdullahSharabati

Жыл бұрын

@@animatedbiologywitharpan I totally understand you and know you are meaning, I was just kidding, really, sorry

SO HELPFUL!!!! I wish I knew about this channel during my phd...

Your videos make every step so easy to understand!

Absolutely amazing! Thank you so much! You are so gifted.

This was super helpful and easy to follow, thank you sooo much 🙌🙏💓. You are a star

Congratulations for your channel. I'm subscribing because of this video and your clear explanation...

Your channel is extremely helpful to me and has been a real world saviour for gaining a fundamental understanding of my projects. I am working with gene knockout vs control conditions and will be using your pipeline to do the further analysis. Thanks again and keep up the amazing work!! 💛💛

@niharikasingh7677

Жыл бұрын

Hi again! I tried to use this method but I'm facing a small error from my end. The Gene IDs are a separate column and hence my no. of rows are not equal to the no. of columns. How did you ensure that the Gene IDs don't get counted as a separate column?

@aymsagagi

Жыл бұрын

I am having the same problem !

Beyond your excellent content, you nailed with your channel name choice (bioinformagician) 🤣😂😂. Thank you!

Thank you the tutorial, was highly helpful and informative.

This is so nicely explained, thanks for your videos :3

Thank you so much for preparing this video for us. It was extremely useful! I will definitely subscribe to your channel!

That's excellent magic indeed. You have done perfectly. Would you please create the next series according to the same data, how to analyze up and down-regulated gene comparison between treated and untreated groups by using box plot or something? It will be helpful as a newbie for me.

@Bioinformagician

2 жыл бұрын

I will surely consider making a video covering downstream processing and visualization of these DEG :)

Thank you so much from Thailand!

Thanks a lot for making this video! This is very very help ful!!!

Well made video. Thanks for sharing it with others.

Thank you so much! This was incredibly helpful.

I have learned a lot from your videos! you are the best :)

Very well explained, thank you preparing this video. . .

thank youuuuu, you just saved my life literally

Thank you very much! Such a great video!

Greatly explained. Thanks

It was very helpful and clear. Thank You

Thank you, awesome explanation, I am now a subscriber :)

well done! wait for more videos.

absolutely great videos

You did a great job

This was great! Thank you.

Easy to understand video. thank you

Hey great stuff! I was wondering, what if you wanted to compare treated vs untreated but per cell line, would you have something in your design when creating your deseq object like (design = cell_line + condition) or is this extracted using contrasts or both?

Keep up the good work.

Thank you for this !!

Now I'm working on projekt and writing application reading disfunction expression From genotypem by cell repair In C++ - This video is very professional and helping Me to understanding data set From IT. Thanks You

@user-vk8bd1re8c

2 ай бұрын

DeSeq2 - That I nedeed 💪❤️🙏

Supremely useful!!!

very well explained. Can you tell how to plot heatmap for the data you analyzed in this video ?

Your videos are wonderful! would you consider expanding on the use of contrast, perhaps a demonstration with a sample with 3 conditions or more exploring the results? Thank you for considering it and keep up the great work you are doing!

@Bioinformagician

Жыл бұрын

I will surely consider making a video on contrasts. Thanks for the suggestion.

so wonderful!!!! thanks a lot!

Hello there, thank you so much for all amazing tutorials. Quick question: If I trying to analyze a counts normalized matrix (median of ratio DESeq2) Do I need to run DEGA? or log2 to that counts matrix?. Thank you so much for your help, I naive bioinformatician

it was very easy and informative..... but I really wish you could also work on rice genome

Hello. I have an quick question in terms of normalization. Since Deseq2 itself has a normalization algorism, I do not need to do further normalization such as FPKM? Or, before performing Deseq2 run, should I first do normalization my read count data?

Thanks for the videos

Medical student who was struggling with this ! You're so kind and helpful, Thx!! And I'm curious about how to export the DESeq2 results into csv file or Excel file to check which gene is on the Upper/Lower right quadrant on the MA plot.

@Bioinformagician

Жыл бұрын

This is how you can export your results: write.csv(as.data.frame(results), file="results.csv")

Hi. Thanks for the video. Please how do I view all the genes (expressed and unexpressed)?

Thank you for the video

Thank you! How can I plot now the heatmap? something like > heatmap(as.matrix(res)) ??

Hi! I have one question, if data is given in the TPM format can you still apply the DESeq2? Does it only work with raw data? Thank you!

Great tutorial. One comment: reducing the size of the input is not done primarily because of reducing the computational burden, but to lessen the impact from multiple testing correction.

Hey. Thanks for the video. Can you just lemme know that whether you took one data or two different types of data?

Thank you so much for your videos. I would like to learn the pipeline for RNA seq data analysis and I am wondering if there is any order to follow up on the videos. Thank you so much

really helpful! Thank you so much! But would love to know the explaination of each command. Like what do "~", "," do in the command. Thankssssssss

thank you so much

When we are performing de novo, how to make counts and sample info files? Can you suggest me any tool for making those files

Hi, thank you for this amazing video. I am currently doing a gene expression analysis. Even though I have the same row and col names in my counts and coldata I am still getting the FALSE arguments for all(colnames(Counts) %in% rownames(Coldata)) can you please help with that?

Mam help. Which is better mam, ballgown or DEseq2 ?

Hello, thank you so much for sharing the very helpful vdo. I want to know that when you load read counts data, the head of your read counts table shows only 8 columns of sample data excluding gene_id column, but when I do with my data, it still shows 9 columns (9 variables) including gene_id column. So, how can I do as you did? @Bioinformagician

Error in `[.data.frame`(countData, , rownames(colData)) : undefined columns selected

Hi, thank you for your explanation!!! Very useful video :) I only have one question: once I got the results, how do I select the most differentially expressed genes? Let's say I wanna view only the top 40 genes.

@Bioinformagician

2 жыл бұрын

You can sort your results by largest log fold changes and lowest adjusted p-values. The first 40 genes on your list are the ones which are most differentially expressed. NOTE: log fold changes can be positive or negative, if you sort log fold changes descending, you will only get top genes with largest positive fold changes. If you wish to get top differentially expressed genes irrespective of the direction, you can sort by taking absolute log fold change values.

Excellent videos, what does it means the following error when doing the DeSeq matrix? "In DESeqDataSet(se, design = design, ignoreRank) : some variables in design formula are characters, converting to factors" thank you very much

I get stuck at 7:44 when we put "design = ~ dexamethasone" - Its shows an error of "some values in assay are negative"!! Can someone help here

how would you do a differential expression between multiple cell lines? do them in pairs and then find the shared highly differentially expressed genes? or is there a way of doing it in one analysis?

@aliciagarciaalonso6930

9 ай бұрын

Same issue!! I guess one may be able to do this comparisons in one go using the 'contrast' parameter of the result function? But I haven't checked it out...

Thanks!

Sorry if my question is wrong. You have done DEseq with raw gene counts. is not required to convert these id to gene name and normalize to FPKM or TPM for further analysis?

kindly share a video on how to perform differential expression analysis of transcriptome data from TCGA database

How did you take that geneids to the serial number ?? Please guide mee

thank you so much for this very help videos, can you plz explain why you didn't do cpm tpm rpk rpkm before DESQ?

@Bioinformagician

2 жыл бұрын

DESeq2 requires raw un-normalized read counts as it performs its own set of normalization steps. CPM, TPM, RPKM are all normalization methods that DESeq2 does not use.

Would you be willing to do a video on more complicated design setups for DESeq?

@Bioinformagician

2 жыл бұрын

Thank you for the suggestion. I will surely consider making a video covering this topic :)

I enjoy your videos! I have one question about your design. It seems like there are two categorical variables - cellLine and dexamethasone in the colData table. Is there some reason you didn't include the cellLine variable in the design matrix?

@Bioinformagician

2 жыл бұрын

You are right, I could have used both. However, I wanted to keep it simple for this tutorial and explain how the DEseq2 works for one design factor (i.e. dexamethasone) and so my goal for this analysis was to study the effect of treatment on gene expression. I could have used complex designs like ~ cellLine + dexamethasone, if I wanted to test for the effect of dexamethasone while controling for the effect of cellLine. But in this case, to keep it intuitive I chose to demonstrate with one factor. Hope that answers your question. Thank you! :)

@jkim9931

2 жыл бұрын

@@Bioinformagician Thanks for the explanation. I was thinking about that. This is a tutorial video so it doesn't have to be more difficult. I think forming a design matrix is involved in linear models which is another topic to explain. Thanks!

@Bioinformagician

2 жыл бұрын

@@jkim9931 That is true, I have tried to explain linear models in my previous video (kzread.info/dash/bejne/YpZmls-pqrDFZbA.html). But yes, it can be a whole separate video in itself!

Hello, I really loved the walkthrough of DESeq2. Definitely going to follow your channel. I have a question. I have a counts matrix with following groups: control, treatment 1, treatment 2, treatment 3. First, I need to draw comparison between each treatment and control as reference and then compare between treatments. While setting the factor level, if I use control as the reference, then will it draw comparisons as follows: control vs treatment 1, control vs treatment 2 and control vs treatment 3? I believe I can use the contrast function for getting the comparison between treatments? Thank you!

@Bioinformagician

2 жыл бұрын

I am glad my video has been helpful! Regarding your question, yes you can use contrast to make a comparison between treatments.

@manavgandhi2503

2 жыл бұрын

@@Bioinformagician Thank you. Could you also confirm if setting the reference as control for factors would draw the same comparisons that I wrote?

@Bioinformagician

2 жыл бұрын

@@manavgandhi2503 Yes, setting control as the reference will allow you to make comparisons between control and treatment levels. Only difference being, you will be able to see the order reversed i.e. "condition_treatment1_vs_control", which essentially gives you genes up/down regulated in treatment 1 compared to control.

@manavgandhi2503

2 жыл бұрын

@@Bioinformagician Got it. Thank you!

when i use my data, i have error "more columns than column names". I check your file and see 2 files are same format. Why can you read file without error?

@Bioinformagician very helpful. can you please make vedios on functional annotation of RNA seq data. It would be very helpful

Thanks for your useful video! I am a beginner and I have some problems. When I upload my airway package (It is done well) I do not get to obtain the files.csv in my file folder. It looks like nothing happens. Is there another way to get them?

@Bioinformagician

2 жыл бұрын

Can you give me exact commands you ran to get the data?

Informative video. Thanks I have a query regarding data analysis if you could please help me in that. I have a data set for tumors that I downloaded from cancer data portal so now I have gene expression data and clinical data for both tumors. I want to compare the gene expression of both tumors but I am no getting from where I should start, how can I compare these tumors by using DESeq2. Please guide me. Thank you

@Bioinformagician

2 жыл бұрын

A couple of questions - 1. What data have you downloaded - RNA-Seq reads or quantified expression values? 2. What is the format of the data - are these raw counts or normalized expression values?

Congrats for your videos! They are really, really very useful and well explained. Just one question, maybe you can help me; Do you know how can I find, using R, the gene names or symbols from the ENSG numbers?

@Bioinformagician

2 жыл бұрын

I am in the process of making a video on it. Please stay tuned! :)

@fabioseiva-uenp9155

2 жыл бұрын

​@@Bioinformagician Thank you for your prompt reply. You can bet I'll be tuned. Also, could you tell me if it is possible to get the gene names from the ASHG numbers?

@Bioinformagician

2 жыл бұрын

@@fabioseiva-uenp9155 I haven't dealt with ASHG numbers, can you tell me what database are they associated with?

@fabioseiva-uenp9155

2 жыл бұрын

@@Bioinformagician Ok, I am learning about using datasets, extracted from GEO, so maybe I am asking the wrong question. The dataset I am referring to is GSE55191. After extracting the data, the ID I have is based on ASHG. Sorry for not being more specific, but if you could take a look and answer me, I would be extremely grateful.

@Bioinformagician

2 жыл бұрын

@@fabioseiva-uenp9155 Found the mappings - www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL24530

Hello Madam Thanks for you video, I am having 12 samples, in that 3 controls, 3 one trtmt, 3 another trtmt, 3 another trtmt,. So like this when there are 4 conditions, how to perform DESeq of those?

@Bioinformagician

2 жыл бұрын

Did you try following my video or the DESeq2 vignette? Could you tell me what you tried and what didn't work?

Fot the "set factor level" step, What should I do if I have 3 levels and I want to compare gene expression between three level?

@Bioinformagician

2 жыл бұрын

You could do pairwise comparisons first and then take an intersection between them.

Hi, I am new in this domain so please tell about how you got expression data for GSE52778 and how to club all 8 sample data in one csv file.

@Bioinformagician

2 жыл бұрын

The data was already merged and provided by the authors. However, if you are interested to learn how to merge datasets, I have previously covered it. Here's where you can find it - kzread.info/dash/bejne/eqaWx8N-etSxk7w.html

May I ask how to download the CountData file in this tutorial please.... I tried looking at the study page but i just couldnt find a way to download the csv file.

@Bioinformagician

Жыл бұрын

Here is the code to get the data: github.com/kpatel427/KZreadTutorials/blob/main/getData.R

I did the same workflow with the same database, however the Log2FC all come out with the inverse sign .. what can this be due to? thank you in advance

@Bioinformagician

2 жыл бұрын

Did you set the reference level before performing your analysis? Also could you paste the output from summary(result)?

Congratulations for the video. Very helpful. I am having problems with the DESeq2 installation, R tells me that the path is not writeable. Any help? Thanks

@Bioinformagician

2 жыл бұрын

Can you paste the exact error?

@alvaroruiztabas5627

2 жыл бұрын

@@Bioinformagician Of course, when I run "BiocManager::install("DESeq2")", in the end of the run the console show "There were 16 warnings (use warnings() to see them)" and hen I run "warnings()", R shows 1: In install.packages(...) : installation of package ‘png’ had non-zero exit status 2: In install.packages(...) : installation of package ‘curl’ had non-zero exit status 3: In install.packages(...) : installation of package ‘openssl’ had non-zero exit status 4: In install.packages(...) : installation of package ‘RCurl’ had non-zero exit status 5: In install.packages(...) : installation of package ‘RcppArmadillo’ had non-zero exit status 6: In install.packages(...) : installation of package ‘httr’ had non-zero exit status 7: In install.packages(...) : installation of package ‘GenomeInfoDb’ had non-zero exit status 8: In install.packages(...) : installation of package ‘Biostrings’ had non-zero exit status 9: In install.packages(...) : installation of package ‘GenomicRanges’ had non-zero exit status 10: In install.packages(...) : installation of package ‘KEGGREST’ had non-zero exit status 11: In install.packages(...) : installation of package ‘SummarizedExperiment’ had non-zero exit status 12: In install.packages(...) : installation of package ‘AnnotationDbi’ had non-zero exit status 13: In install.packages(...) : installation of package ‘annotate’ had non-zero exit status 14: In install.packages(...) : installation of package ‘genefilter’ had non-zero exit status 15: In install.packages(...) : installation of package ‘geneplotter’ had non-zero exit status 16: In install.packages(...) : installation of package ‘DESeq2’ had non-zero exit status I don't really know how to fix it. Thanks!!

@anamikapandey4769

2 жыл бұрын

@@alvaroruiztabas5627 install all the dependencies one by one, your problem will be resolved

Can you make a video on functional enrichment analysis and GSEA?

@Bioinformagician

Жыл бұрын

That's definitely in the pipeline! Hopefully should be able to come up with a video on it soon. Please stay tuned :)

Are you planning to do a video on Gene enrichment analysis?

@Bioinformagician

Жыл бұрын

Yes, I surely have plans to make a video on it.

I am getting this error when I am trying to create dds Error: unexpected '=' in: "dds

@Bioinformagician

2 жыл бұрын

"dds

Nice video! Really helped, but I have 3 sample groups or levels. I have done pairwise comparison between the levels, but I don't know how to get final results

@Bioinformagician

2 жыл бұрын

You can just intersect DE genes between both those comparisons.

@excelobiageli9446

2 жыл бұрын

Okay, but how will my final result be like? Like the table containing the degs, will it still contain the original number of samples? And what values would it contain? That is where I am confused

@Bioinformagician

2 жыл бұрын

@@excelobiageli9446 So for each pair-wise comparison, you will have a data.frame with differentially expressed genes, log fold change values, p-values, min.pct and q-values. You will have 2 such data frames from each comparison. You can filter differentially expressed genes based on p-values and/or q-values and log fold changes, and intersect genes column from both data.frames. So what you end up with a vector of genes differentially expressed from both comparisons.

@excelobiageli9446

2 жыл бұрын

@@Bioinformagician okay!! Thank you very much

What are technical replicates and biological replicates madam

@Bioinformagician

2 жыл бұрын

I have explained this in one of my previous video - kzread.info/dash/bejne/hWWExbZuotOek5c.html

I am getting this error, column 1 contains gene names counts_data

@aritahalder9397

Жыл бұрын

what needs to be done when there are duplicated gene names(with diff expression values) in the data?? Should we keep just one of the duplicated values or average out the values?

thanks

HELP thanks a lot for this tutorial I have a question: I have a set of data which has this condition: 1. severe drought in short time 2. severe drought in long time 3. mild drought in short time 4. mild drought in long time now, I want to make a meta data but I'm not sure how to do it, I tried many times and always get an error the most recurrent one is "the model matrix is full ranked..." any suggestion would be appreciated.

@Bioinformagician

Жыл бұрын

The error indicates you are providing redundant metadata to Deseq(). Can you email me the exact command and how your metadata looks like?

Error in DESeqDataSetFromMatrix(countData = countData, colData = colData, : ncol(countData) == nrow(colData) is not TRUE

What can you do if the column names in the counts data does not match the rownames of the coldata? I had to create my own sampleinfo file, the names are identical and yet it says they do not match. I have even created an entirely new data frame using the for_matching_df

@KiwiAteMonkey

2 жыл бұрын

hello, I have the same problem. Could you solve it for your data?

@mattmarino3460

2 жыл бұрын

@@KiwiAteMonkey use the colnames() function on your counts data. Copy those one by one into an excel file, add other rows next to each group aka “treated vs u treated” save as .txt or .csv. Then read into R using read.delim(data, header = true, sep = “,”, row.names = 1, stringsAsFactors = FALSE) that should fix it. Then you can check rownames of sample info match the column name of the data using all(colnames(data) %in% rownames(sampleinfo))

@Bioinformagician

2 жыл бұрын

Convert column col1 in for_matching_df into rownames, and check if all(rownames(for_matching_df) == colnames(counts)) is TRUE? If it is not true, then run this: counts

@Bioinformagician

2 жыл бұрын

In general there are two things you will check: 1. Are all column names in counts present as rownames in colData? all(rownames(colData) %in% colnames(counts)) # should be TRUE 2. Are all rownames in colData in the same order as column names in counts? all(rownames(colData) == colnames(counts)) # should also be TRUE In case if 2. is FALSE, then column order can be changed by running this: counts

@hongyu9455

2 жыл бұрын

@@Bioinformagician Thanks for your tutorial. I have similar issue regarding matching column and row name. I’ve copied the command from you tutorial and got the following error message: all(colnames(counts_data) %in% rownames(colData)) Error in h(simpleError(msg, call)) : error in evaluating the argument 'x' in selecting a method for function '%in%': error in evaluating the argument 'x' in selecting a method for function 'colnames': object 'counts_data' not found Many thanks for your help!

In my metabarcoding dataset, I have multiple factors that I am interested in testing for differences. Does the factor level matter? Would I need to re-run DESeq with different factors for each of my different questions?

@Bioinformagician

Жыл бұрын

If I am understanding your question correctly, factor level does not matter. All levels are compared with a reference. Even then you could create contrasts between different levels. contrast in results() will allow you can compare any two groups you want.

@devinjones7271

Жыл бұрын

@@Bioinformagician Great! So if I am interested in looking at time, sex, age, season, and a few other factors downstream analysis, I can just use something like sequencing run for my design to normalize across different sequencing runs and then pull that normalized data for other analyses?

# making sure the row names in colData matches to column names in counts_data all(colnames(counts_data) %in% rownames(colData)) TRUE # are they in the same order? all(colnames(counts_data) == rownames(colData)) False what can I do now? Can you please help?

@Bioinformagician

2 жыл бұрын

counts_data

Hi, I am PhD student struggling to use the R studio. would you please help me to perform differential abundance analysis for my data, please let me know, I will mail you the data. I would be very grateful to you.

I tried this protocol for a series matrix file that had log2 normalized value (all decimal values, downloaded from GEO). I received the error: _Error in DESeqDataSet(se, design = design, ignoreRank) :_ _some values in assay are not integers_ Does this mean this package cannot be used for normalized decimal values? If yes, Which package is more suitable? A link to any relevant protocol would be appreciated. Thanks

@Bioinformagician

2 жыл бұрын

You cannot run DESeq2 on log2 normalized counts. You need raw counts to run this analysis.

I have a question, you chose 10 for filtering genes. Is there any method to find this number? I used 10 for my data analysis, I got a lot of reads that was differentially expressed. and I think it is wrong. please help me, if there is method for finding filtering threshold.

@Bioinformagician

2 жыл бұрын

I followed the threshold provided in the vignette. There is no method to find a threshold, it depends on how relaxed or stringent you want to be while filtering for genes. However, you need to be careful as having a higher threshold might filter out genes that may be differentially expressed but are not highly expressed. You said you found a lot of reads differentially expressed, can you explain why you think it is wrong?

@kobrarahimi9164

2 жыл бұрын

@@Bioinformagician I got the data from ncbi and working on it. In the summary that was provided with the data, it is written that they are comparing knockdown strain with wild strain in both irradiation and unirradiation condition. They found 31 genes that was differentially expressed. Unfortunately, they did not submit the protocol in supplementary data. When I run this command with threshold 10, I get more than 3000 genes. I dont know where is my problem.

@kobrarahimi9164

2 жыл бұрын

And when I set the pvalue

@Bioinformagician

2 жыл бұрын

If you have retrieved the data from NCBI GEO, you will find associated publication. Read through the paper to find out what thresholds they used and why. Without those, it is really difficult to re-create what they found. You should also be interested in "why" those thresholds were used, it is important to be able to justify your choices.

That is very helpful. i have a question, i always have problem with my metadata, i tried to save is as csv, text and so on but it always give me an error message "Error in DESeqDataSetFromMatrix(countData = cts, colData = metaData, design = ~condition) : ncol(countData) == nrow(colData) is not TRUE" could you please help me sort it out? Thank you.

@Bioinformagician

2 жыл бұрын

The error is telling you that the the number of columns in your countData is not equal to the number of rows in your colData. They should be equal. Check the columns and rows in your countData and colData to make sure you don't have any extra columns.

@aymsagagi

2 жыл бұрын

@@Bioinformagician i solved that, thank you. But one more problem, i always gets error saying that i have duplicate rows, any suggestions on how to resolved that, especially how to easily check all the rows from a big data.

@Bioinformagician

2 жыл бұрын

@@aymsagagi To check for duplicate rows in a data frame: df[duplicated(df),] To remove duplicated rows: df % distinct()

@aymsagagi

2 жыл бұрын

Thank you so much.