You are making my learning journey much more easier for an intermediate use and making follow vignettes a breeze! Glad to have found your SEURAT videos!
Honestly, this was so precisely what I needed in a previously very frustrating situation. The way you manage to explain the background of what you are doing connected to how it is done is absolutely awesome. Thank you so so much!
First, thank you a lot. Your tutorial videos on scRNAseq analysis are a treasure! I would like to know if you already have a video on differential cell-type composition analysis or if you are planning to record one. Thank you so much!
Hi! there is not tutorial on YT on combining microarray and rna sequencing data under one analysis. this would be a very helpful tutorial! thanks again for all your amazing content!
Since microarray and RNA-Seq are different technologies, I am not sure of any use cases where there would be a requirement to combine data from both these technologies.
@@Bioinformagician I was thinking of when one does a meta-analysis on data from GEO. Some studies have done this for example: doi: 10.3389/fgene.2021.663787 however, it is difficult to learn how to replicate this. Thank you though.
Hi Khushbu, first of all I really appreciate the effort you put in sharing your knowledge with us. I want to know from where I can get dataset for trajectory analysis which can be further published?
Thank you for the tutorial. Can you extend this to "plot_genes_in_pseudotime" after the "graph_test"? that is one confusing part of the code on their tutorial
Hi @Bioinfomagician..Loved your tutorial, and much-needed channel for budding bioinformaticians. I wanted to know if we don't have information on root cluster then how do we order cells according to Pseudotime?For eg in your case ProB cells are from cluster 5 but In my case, I just have CD4T cells in 4 clusters...how do I order them? Thanks in advance🙏
@@Bioinformagicianthanks for your reply😍 is there any strategy to find out root cluster? I was doing this like..looking for lymphoid differentiation markers and setting those clusters or cells as root I am not sure if this way is right I appreciate your help! Thanks a lot... you are doing a great job..whenever i am stuck somewhere i look into your channel for help😊
@@youvikasingh7955 Unfortunately I am not aware of any reliable methods to find out root cluster. Your best bet is to dig up literature and research papers to find out whether any lineage studies have been done previously for the cells you are studying which can give you clues for the origin cell type and associated markers. I am glad to hear my channel is serving as a useful resource :)
Thank you so much for this great video! Curious if you have found a way to plot individual genes in pseudo time? There is a way in Monocle, but when using this integration with Seurat there is not an expression family that is needed to plot specific genes in pseudo time or to find which genes are driving the trajectory. Any advice would be greatly appreciated! Thanks again!
Thank you! I am not seeing any '^MT- genes showing up in the data seuratobj I made from the downloaded data. Just get error: Error in validObject(object = x) : invalid class “Seurat” object: 1: all cells in assays must be in the same order as the Seurat object invalid class “Seurat” object: 2: 'active.idents' must be named with cell names Thanks if you have seen this error before
Your videos are awesome, very helpful! For setting the clusters in the cell data set, do you think it could be a viable option to use the defined subclusters or is it better to use the clusters which seurat found with FindClusters? Thank you!
I have hundred of laser-microdissected samples, is there a pipeline for trajectory analysis for this kind of data? Do you think that I could just format my libraries as if they were single cells instead of single microdissections and trick it into working?
'redefined_cluster' was already present in the data I am using, as the data was already annotated. The term 'subset' is to just get certain group of cells from the entire dataset.
Hi Bioinfomagician, great video as always. You spoke about the importance of having the optimal cluster resolution, is there any objective way to determine this? Is there any tools you would recommend? Thank you!
What I usually do is run clustering with multiple clustering resolutions like this: FindClusters(object = seurat_integrated, resolution = c(0.2, 0.5, 0.8, 1.0)) and then plot each of them to visually see which resolutions provide optimal separation. If you have known cell types in your data, it might be of help to visualize the markers for those cell types too for different resolutions to make sure different cell types, group into separate clusters.
ZLJ 3周前 Thanks a lot! you are so excellent !!and i have a question,the s5" filter(status=="OK")",what's difference between “OK" and "failure"，i cannot figure out the meaning and difference.
Hey, your videos are truly amazing! Thank you so much! I have a question concerning point 3 "# 3 Learn trajectory graph -----------". I obtain a trajectory different from the one that you obtain in the video and the same happens if I copy your code from github. Do you have any idea of what I am doing wrong? Thank you again!! This is the error I get when I run the plot of the third point: Warning message: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps
You could build separate trajectories for each condition/treatment, get top 50 or 100 genes that change expression over pseudotime from both groups and compare them.
As always these are wonderful. Quick question - if we're doing a trajectory analysis following integration do we just run as.cell_data_set() on our existing Seurat object? Or do we have to re-create the object transforming the expression matrix?
I think you could read the integrated object into as.cell_data_set() and use the clustering information from Seurat's UMAP to cluster cells and learn trajectory on those embeddings.
Hello Khushbu. 2 points I am confused about. 1. I was wondering when converting from Seurat to Monocle3 cds object, are the counts used for downstream analysis in your tutorial or the data from Seurat's data slot? Counts are not normalized and wouldn't that lead to erroneous calculations if not normalized in Monocle3 later on? I am not sure if this was done in this tutorial. 2. Why take Seurat's UMAP coordinates for analysis in Monocle3? Wouldn't it be more informative to know how Monocle3 projects the Seurat generated clusters in 2D space? Otherwise, isn't Monocle just drawing or projecting pseudotime over Seurat based proximity? Thank you.
Thank you for all the Rstudio vedios. They're so clear and helpful !!!! I've failed to instal SeuratWrappers (Mac Monterey 12.5, Rstudio IDE 4.2) using remotes or devtools; troubleshooting follow the github site was also nothing worked. Any suggestion would be greatly appreciated !!! Many thanks,
Thanks for the video you are saving my PhD, but I have the same problem, I can´t install SeuratWrappers, this is the Warning in install.packages :package ‘SeuratWrappers’ is not available for this version of R , I tried to install from but devtools::install_github("satijalab/seurat-wrappers") and remotes::install_github('satijalab/seurat-wrappers')nothing, any recommendation? I have R version 4.2.2
It seems to be working fine for me. Here's the link to the associated publication: academic.oup.com/nsr/article/8/3/nwaa180/5896476#267860186 They have their data deposited in NCBI GEO - GSE137864 and GSE149938
@@Bioinformagician Hi. Love these videos! The link to the data is not working for me as well. I am able to get the expression matrix file from NCBI GEO. But having difficulty finding the gene and cell metadata files. Any assistance would be greatly appreciated! :)
Hi, thank you for this great video, but I have a problem. When I install monocle3, I get a error. ** byte-compile and prepare package for lazy loading Hata: package or namespace load failed for 'SummarizedExperiment' in library.dynam(lib, package, package.lib): DLL 'DelayedArray' not found: maybe not installed for this architecture? Ek olarak: Warning messages: 1: package 'matrixStats' was built under R version 4.1.3 2: package 'GenomicRanges' was built under R version 4.1.2 3: package 'S4Vectors' was built under R version 4.1.3 4: package 'GenomeInfoDb' was built under R version 4.1.2 Çalıştırma durduruldu ERROR: lazy loading failed for package 'monocle3' * removing 'C:/Users/Burak/OneDrive - Dokuz Eylül Üniversitesi/Belgeler/R/win-library/4.1/monocle3' Warning message: In i.p(...) : installation of package ‘C:/Users/Burak/AppData/Local/Temp/RtmpC69TpC/file28646e1844d/monocle3_1.2.9.tar.gz’ had non-zero exit status
This channel will be a respectable resource in our bioinformatics community
If it hasnt been said enough already, I will repeat : what you are doing is AWESOME!
You are making my learning journey much more easier for an intermediate use and making follow vignettes a breeze! Glad to have found your SEURAT videos!
Thanks a lot! The class is easy to follow with the github code and great to know how to do that and why we need to do that. Really appreciate it!!
Honestly, this was so precisely what I needed in a previously very frustrating situation. The way you manage to explain the background of what you are doing connected to how it is done is absolutely awesome. Thank you so so much!
Your tutorials have been very timely, informative and helpful. We will appreciate it if you could also make a tutorial on SCENIC. Thank you so much!
Will surely consider making a video on it! Thanks for the suggestion :)
This playlist is so helpful. Would it be possible to have a Step-by-step tutorial for cell-cell interaction?
very detailed tutorial, I found that myself more susceptible to your pattern compaired to others
thanks for making these awesome videos. I really appreciate it !!!
so helpful!! saved me hours of of reading error messages
First, thank you a lot. Your tutorial videos on scRNAseq analysis are a treasure! I would like to know if you already have a video on differential cell-type composition analysis or if you are planning to record one. Thank you so much!
Thank you so much! Really helpful for me to convert Seurat object for Monocle3
EXCELLENT video thank you very much. Excellent clear explanations accompanied with great demonstrations & slides I really mean that
Hi! there is not tutorial on YT on combining microarray and rna sequencing data under one analysis. this would be a very helpful tutorial! thanks again for all your amazing content!
Since microarray and RNA-Seq are different technologies, I am not sure of any use cases where there would be a requirement to combine data from both these technologies.
@@Bioinformagician I was thinking of when one does a meta-analysis on data from GEO. Some studies have done this for example: doi: 10.3389/fgene.2021.663787
however, it is difficult to learn how to replicate this. Thank you though.
I am learning so much from you, thank you so much!
Very nice Presentation! I liked it... Appreciate you!
Thanks
Excellent tutorial 👏
Thank you,, Please Make same video for Spatial Transcriptomics Analysis
you are the best. Thank you
Thanks so much. Very informative videos!
Excellent! Thank you very much
Amazing clear helpful video
Hi Khushbu, first of all I really appreciate the effort you put in sharing your knowledge with us. I want to know from where I can get dataset for trajectory analysis which can be further published?
Thank you! So so helpful :)
Thank you for the tutorial. Can you extend this to "plot_genes_in_pseudotime" after the "graph_test"? that is one confusing part of the code on their tutorial
Thank you for this video !!
Hi, thank you so much for this video. How to do trajectory analysis in diffusion maps instead of principal components?
Would you consider a tutorial for TI with slingshot? that would be so helpful for me!
Hi @Bioinfomagician..Loved your tutorial, and much-needed channel for budding bioinformaticians.
I wanted to know if we don't have information on root cluster then how do we order cells according to Pseudotime?For eg in your case ProB cells are from cluster 5 but In my case, I just have CD4T cells in 4 clusters...how do I order them?
Thanks in advance🙏
I am afraid pseudotime can be determined accurately for other cells if we don't know the cells it originated from i.e. the root cluster.
@@Bioinformagicianthanks for your reply😍
is there any strategy to find out root cluster?
I was doing this like..looking for lymphoid differentiation markers and setting those clusters or cells as root
I am not sure if this way is right
I appreciate your help!
Thanks a lot...
you are doing a great job..whenever i am stuck somewhere i look into your channel for help😊
@@youvikasingh7955 Unfortunately I am not aware of any reliable methods to find out root cluster. Your best bet is to dig up literature and research papers to find out whether any lineage studies have been done previously for the cells you are studying which can give you clues for the origin cell type and associated markers.
I am glad to hear my channel is serving as a useful resource :)
You are doing a great job. What if the data is not annotated?
Thanks alot !very useful!
Thank you great videos!!
This is great!! Thank you so much. Will it be possible if you can make tutorials on intercellular interaction tools in R? Thank you again!
Thanks for the suggestion, will surely considering making videos covering this topic.
Thank you so much for this great video! Curious if you have found a way to plot individual genes in pseudo time? There is a way in Monocle, but when using this integration with Seurat there is not an expression family that is needed to plot specific genes in pseudo time or to find which genes are driving the trajectory. Any advice would be greatly appreciated! Thanks again!
I am wondering this as well. Thanks in advance!
You can plot specific genes in pseudotime by running cds
Thank you! I am not seeing any '^MT- genes showing up in the data seuratobj I made from the downloaded data. Just get error: Error in validObject(object = x) :
invalid class “Seurat” object: 1: all cells in assays must be in the same order as the Seurat object
invalid class “Seurat” object: 2: 'active.idents' must be named with cell names
Thanks if you have seen this error before
I have the same error message
I have the same issue. I use the AddMetaData function from Seurat to solve the problem.
rownames(metadata)
Why do you also want to compare the cluster before trajectory?
Ma'am, can you please let me know where can I get gene and cell meta data for a particular single cell analysis dataset?
Thank you so much. Best!!!!
Your videos are awesome, very helpful! For setting the clusters in the cell data set, do you think it could be a viable option to use the defined subclusters or is it better to use the clusters which seurat found with FindClusters? Thank you!
What is the benefit of monocle over other trajectory analysis tools such as slingshot?
I have hundred of laser-microdissected samples, is there a pipeline for trajectory analysis for this kind of data? Do you think that I could just format my libraries as if they were single cells instead of single microdissections and trick it into working?
Question? From where did you get the redefined_cluster? I always get confused with the term "Subset" in scRNA analysis.
'redefined_cluster' was already present in the data I am using, as the data was already annotated. The term 'subset' is to just get certain group of cells from the entire dataset.
@@Bioinformagician Gotcha! It was already in the ref database I guess. Thanks!
Hi Bioinfomagician, great video as always. You spoke about the importance of having the optimal cluster resolution, is there any objective way to determine this? Is there any tools you would recommend? Thank you!
What I usually do is run clustering with multiple clustering resolutions like this:
FindClusters(object = seurat_integrated,
resolution = c(0.2, 0.5, 0.8, 1.0))
and then plot each of them to visually see which resolutions provide optimal separation.
If you have known cell types in your data, it might be of help to visualize the markers for those cell types too for different resolutions to make sure different cell types, group into separate clusters.
Thank you SO MUCH :)
ZLJ
3周前
Thanks a lot! you are so excellent !!and i have a question,the s5" filter(status=="OK")",what's difference between “OK" and "failure"，i cannot figure out the meaning and difference.
graph_test() results outputs status "FAIL" for those genes whose p values, Moran I and Moran test statistic values are NA
Hey, your videos are truly amazing! Thank you so much! I have a question concerning point 3 "# 3 Learn trajectory graph -----------". I obtain a trajectory different from the one that you obtain in the video and the same happens if I copy your code from github. Do you have any idea of what I am doing wrong? Thank you again!!
This is the error I get when I run the plot of the third point:
Warning message: ggrepel: 7 unlabeled data points (too many overlaps). Consider increasing max.overlaps
Sorry,my monocle3 cannot find funtion as.cell_data_set(). Can you help me?
Hello Miss, i am unable to download the data, the website is not opening
Thank you for sharing this! Is there a way to compare the trajectories between two conditions/treatments?
You could build separate trajectories for each condition/treatment, get top 50 or 100 genes that change expression over pseudotime from both groups and compare them.
As always these are wonderful. Quick question - if we're doing a trajectory analysis following integration do we just run as.cell_data_set() on our existing Seurat object? Or do we have to re-create the object transforming the expression matrix?
I think you could read the integrated object into as.cell_data_set() and use the clustering information from Seurat's UMAP to cluster cells and learn trajectory on those embeddings.
Make sure to change the active assay to 'RNA' from 'integrated' before you run the monocle or you will get an error.
Hello Khushbu. 2 points I am confused about.
1. I was wondering when converting from Seurat to Monocle3 cds object, are the counts used for downstream analysis in your tutorial or the data from Seurat's data slot? Counts are not normalized and wouldn't that lead to erroneous calculations if not normalized in Monocle3 later on? I am not sure if this was done in this tutorial.
2. Why take Seurat's UMAP coordinates for analysis in Monocle3? Wouldn't it be more informative to know how Monocle3 projects the Seurat generated clusters in 2D space? Otherwise, isn't Monocle just drawing or projecting pseudotime over Seurat based proximity?
Thank you.
Thank you for all the Rstudio vedios. They're so clear and helpful !!!! I've failed to instal SeuratWrappers (Mac Monterey 12.5, Rstudio IDE 4.2) using remotes or devtools; troubleshooting follow the github site was also nothing worked. Any suggestion would be greatly appreciated !!! Many thanks,
What is the error you see?
I had the same problem, I was able to install SeuratWrappers after installing R.utils package install.packages("R.utils"). try doing this and see.
Thanks for the video you are saving my PhD, but I have the same problem, I can´t install SeuratWrappers, this is the Warning in install.packages :package ‘SeuratWrappers’ is not available for this version of R , I tried to install from but devtools::install_github("satijalab/seurat-wrappers") and remotes::install_github('satijalab/seurat-wrappers')nothing, any recommendation? I have R version 4.2.2
Hi, I had the same problem and installed r.utils. Then it worked with SeuratWrappers
link for data is not working :(.. Can you please cite another source
It seems to be working fine for me. Here's the link to the associated publication: academic.oup.com/nsr/article/8/3/nwaa180/5896476#267860186
They have their data deposited in NCBI GEO - GSE137864 and GSE149938
@@Bioinformagician Hi. Love these videos! The link to the data is not working for me as well. I am able to get the expression matrix file from NCBI GEO. But having difficulty finding the gene and cell metadata files. Any assistance would be greatly appreciated! :)
@@zeeman5007 I have uploaded these files here:
drive.google.com/file/d/1CJ9VSrUCoqPsUI1jrdm2nrLRawI04xZ1/view?usp=sharing
The voice and the video do not match on 25:00-33:00 min.
Hi, Following yout script I find that mitopercent is always 0, any ideas why this might be?
Is it that the sample size is small enough that there are actually 0 mitochondrial genes?
I also find that seu.obj.filtered@metadata has the same number of rows as the orignal, is this right?
Error in as.cell_data_set(b.seu) :
could not find function "as.cell_data_set"
Have you loaded SeuratWrappers library before running that command?
Hi, Great video! thanks! I got this error any idea how to solve it? seu.obj$mitopercent
I have the same issue. I use the AddMetaData function from Seurat to solve the problem.
rownames(metadata)
@@xinyuqu4407 It worked, Thank you so much!
@@xinyuqu4407 Hello, I have tried this solution multiple times; it still shows the same error. is there anything else I can do?
Hi, thank you for this great video, but I have a problem. When I install monocle3, I get a error.
** byte-compile and prepare package for lazy loading
Hata: package or namespace load failed for 'SummarizedExperiment' in library.dynam(lib, package, package.lib):
DLL 'DelayedArray' not found: maybe not installed for this architecture?
Ek olarak: Warning messages:
1: package 'matrixStats' was built under R version 4.1.3
2: package 'GenomicRanges' was built under R version 4.1.2
3: package 'S4Vectors' was built under R version 4.1.3
4: package 'GenomeInfoDb' was built under R version 4.1.2
Çalıştırma durduruldu
ERROR: lazy loading failed for package 'monocle3'
* removing 'C:/Users/Burak/OneDrive - Dokuz Eylül Üniversitesi/Belgeler/R/win-library/4.1/monocle3'
Warning message:
In i.p(...) :
installation of package ‘C:/Users/Burak/AppData/Local/Temp/RtmpC69TpC/file28646e1844d/monocle3_1.2.9.tar.gz’ had non-zero exit status