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Chipster Tutorials
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Registrace 5. 07. 2015
scRNA-seq -Integrated analysis: Introduction, preprocessing and combining samples (Seurat v5)
This video will give an introduction on how you can compare two (or more) single cell sequencing samples, and takes a closer look on the preprocessing and combining the samples in Chipster.
zhlédnutí: 46
Video
scRNA-seq -Integrated analysis: Aligning samples and clustering
zhlédnutí 28Před 21 dnem
Learn how Seurat (v3 - v5) tools align two samples so that the cells in both samples can be clustered and analysed. View Ahmed Mahfouz's (Leiden Computational Biology Center, LUMC, Netherlands) presentation about the Seurat v3 alignment method and more about the integration step in general: czcams.com/video/4KwW90RQz-8/video.html View Rahul Satija's (Seurat is developed in Satija's lab) own ver...
scRNA-seq -Integrated analysis: Conserved markers and differentially expressed genes (Seurat v5)
zhlédnutí 31Před 21 dnem
In this third video of the two sample analysis pipeline, we will see how to get the conserved cluster markers and the differentially expressed genes between the samples, and how to visualise genes/gene lists.
scRNAseq: Pseudobulk analysis
zhlédnutí 48Před 28 dny
Why and how to do pseudobulk analysis in Chipster. More info on pseudobulk: Article: "Confronting false discoveries in single-cell differential expression", Squair, J.W., Gautier, M., Kathe, C. et al. Confronting false discoveries in single-cell differential expression. Nat Commun 12, 5692 (2021). doi.org/10.1038/s41467-021-25960-2 rdcu.be/dLXcR Seurat vignette: satijalab.org/seurat/articles/pa...
8 Visium data (2024): Integration with single-cell RNA-seq reference data
zhlédnutí 60Před 28 dny
This is the eighth video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how perform integration with a single cell RNA-seq reference data set.
7 Visium data (2024): Subsetting out anatomical regions
zhlédnutí 27Před měsícem
This is the seventh video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how to subset out clusters of anatomical regions.
6 Visium data (2024): Visualizing gene expression and identifying spatially variable genes
zhlédnutí 68Před měsícem
This is the sixth video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how visualize gene expression of selected genes, and how to identify spatially variable genes.
9 Visium data (2024): Analysis with multiple samples
zhlédnutí 26Před měsícem
This is the ninth video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how perform the analysis with multiple samples.
5 Visium data (2024): Clustering
zhlédnutí 30Před měsícem
This is the fifth video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how perform the clustering step.
4 Visium data (2024): Normalization and PCA
zhlédnutí 45Před měsícem
This is the fourth video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how to perform the normalization and PCA step.
3 Visium data (2024): Filtering
zhlédnutí 37Před měsícem
This is the third video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how perform the filtering step to filter out low-quality spots.
2 Visium data (2024): Setup and quality control
zhlédnutí 54Před měsícem
This is the second video of the updated Visium spatial transcriptomics data analysis playlist. In this video, we show how to set up the Seurat object for the analysis. This step also produces quality control plots.
scRNA-seq: Remove background contamination with CellBender
zhlédnutí 519Před 6 měsíci
In this lecture you will learn -What is background contamination -How to remove it with CellBender in Chipster
8 Visium data: Subset and integrate with single-cell data (update, August 2023)
zhlédnutí 238Před 11 měsíci
This video describes the steps in integration with single-cell data in spatial transcriptomics analysis.
5b. Trimming and filtering single-end reads (Ion Torrent data)
zhlédnutí 321Před 11 měsíci
This tutorial covers trimming and filtering single end reads such as Ion Torrent data.
scRNA-seq: Quality control and filtering cells (update, July 2023)
zhlédnutí 958Před rokem
scRNA-seq: Quality control and filtering cells (update, July 2023)
5a. Filter contigs and remove identical sequences
zhlédnutí 143Před rokem
5a. Filter contigs and remove identical sequences
4b. Converting VSEARCH contigs for Mothur analysis
zhlédnutí 129Před rokem
4b. Converting VSEARCH contigs for Mothur analysis
4a. Expected error filtering with VSEARCH
zhlédnutí 123Před rokem
4a. Expected error filtering with VSEARCH
3. Combine paired reads to contigs with VSEARCH
zhlédnutí 320Před rokem
3. Combine paired reads to contigs with VSEARCH
scRNA-seq: Normalize gene expression values with SCTransform
zhlédnutí 1,7KPřed rokem
scRNA-seq: Normalize gene expression values with SCTransform
scRNA-seq: Extract information from Seurat object
zhlédnutí 1,3KPřed rokem
scRNA-seq: Extract information from Seurat object
scRNA-seq: Quality control and filtering cells (update, June 2023)
zhlédnutí 785Před rokem
scRNA-seq: Quality control and filtering cells (update, June 2023)
9 Visium data: Identifying cell types using deconvolution
zhlédnutí 867Před rokem
9 Visium data: Identifying cell types using deconvolution
8 Make a phyloseq object for ASV data in Chipster
zhlédnutí 1,1KPřed rokem
8 Make a phyloseq object for ASV data in Chipster
6 Make an ASV table and remove chimeras with DADA2
zhlédnutí 520Před rokem
6 Make an ASV table and remove chimeras with DADA2
5 Combine paired reads to contigs with DADA2
zhlédnutí 300Před rokem
5 Combine paired reads to contigs with DADA2
Nice explanation, thank you!
So helpful content, thank you so much!
Thank you so much for the helpful information! Is it possible to have these slides of the RNA-Seq course?
Audio is not clear, hardly understood anything.
love this!
Hi, Thank you for the informative tutorial! However, I got the error when the epoch reached to the checkpoints: cellbender typeerror: cannot pickle 'weakref.referencetype' object Would you mind telling me how to solve this issue? Thank you very much in advance.
I got a similar error when using python 3.8. Switching to python 3.7 with cellbender solved this issue.
Such a great lecture, easy to follow and full of valuable information, also great questions! Thank you for sharing!
RT happens before breaking the gel beads not before, right?
That is correct, good catch! Here's a nice video explaining the wet lab procedure in more detail: czcams.com/video/uFrrKHB9weY/video.htmlsi=F1fa6-XipaLcg1iq&t=406
is he smoking while presenting
thank you for the presentation. What is the method of keeping unaligned reads by excluding reads aligned to the host using BWA?
The VirusDetect pipeline takes care of this internally. I don't remember the details, but this could be accomplished for example using the samtools command "samtools view -b -f 4 file.bam > unmapped.bam"
The voice is not very clear.
Amazing teacher
Great job. Wondering if there are any further updates regarding the cell segment free software, over the past year? Thank you for sharing~~!
Very nice introduction to filtering and QC!
I am watching from Taiwan. How can I use Chipster to analyze my own data?
Thank you for this interesting work. It really support us.
Bowtie2. I use it for aligment with reference before I call variant by GATK ?
Can i get the ppt?
Well done!
all I need is a permanent chipster tool in my life, is that too much to ask for? :D
Thanks a lot for sharing this content! I cleared many doubts, including the order between local realignment and BQSR
Thank you Chipster Tutorials. Greetings from a bioeng grad student.
bravo
what do you think about filtering out ALL mitochondrial genes?
Thank you so mus!
great job keep on posting such videos
excellent work
Cant focus because of that "rubbing" in the background.
This is a live recording from a course, so unfortunately the sound quality is not the same as for the videos we make in a normal recording setup.
Thank you for the videos!
Hii! I have three SRA data with each having different samples. So how and at what step should I merge them all together
Aligning reads to reference and counting aligned reads per genes (e.g. with HTSeq) is done separate for each sample. You can then combine the count files together to a count table where the rows are genes and columns are samples. Using this table you can then look for statistically significantly differentially expressed genes with tools like DESeq2.
This was really very helpful, especially the statistical properties section. Although all the characteristics you mention are obvious after they have been pointed out, it's not necessarily an intuitive thing that you automatically realize.
the echo is horrible
In part 1 you removed mitochondrial genes, but in part 2 you use them for filtering? Why?
Good point! In this example data the mitochondrial genes had actually already been removed from the data. In part 2 it is shown how to filter out the spots that have high percentage of mitochondrial transcripts if your data has them.
Too many “er” during the presentation
very very good .b
very good .b
Thank you! It was really helpful
Long time
excellent and easy illustration, thank you
Beautiful presentation. Very nicely explained and straight forward schematics!
What if we are getting an error like "id line did not start with @"?
This means that your file is corrupted. Please see the possible solutions proposed by Simon Andrews at github.com/s-andrews/FastQC/issues/37
Thank you for your information but how to do ? I mean we need to use a program for quality control? Is there any specific program?
For read quality we recommend the FastQC program, or MultiQC for many samples. We have integrated them (and many other programs) in the Chipster analysis software which runs in a Web browser. For more info, please see chipster.csc.fi/.
@@ChipsterTutorials Thank you and what if we zipped the FASTQ file , doest it be any problem? or it doesn't matter?
@@MissAsdfb99 gzipped FASTQ files work fine in Chipster, no problem :)
Very nice introduction, thank you.
too many filler words aaaaaaa... next time prepare well. Also, please fix your mic
Thank you for your comment -please note, that this is a recorded live lecture of our guest lecturer. Thus we cannot redo this video.
@@ChipsterTutorials well then convey my feedback to your guest lecturer or next time select someone who does not stutter a lot
Maybe its due to fact that his primary language isn't English
whats the commandline for genotype gvcf?
At what stage in the analysis workflow can you use gene set enrichment analysis (GSEA)?
You could do enrichment analysis for the lists of differentially expressed genes.
@@ChipsterTutorials Awesome! Thank you for your help, I really appreciate it!!
If we use 5-15 PCs, then how do we represent all of these dimensions visually? I understand that with 2-3 dimensions we can put the data onto a single graph, so with this number of dimensions would we have to draw out many different graphs during the analysis stage? How would we present all of these dimensions in a research project?
Or do we put the large amount (5-15) of PCs into t-SNE and UMAP to further reduce dimensionality until we are able to create one singular 2-D graph (2 dimensions)?
You got it right! Chipster (and the corresponding Seurat vignettes) give you few different plots for estimating the (true) dimensionality of the data, i.e. how many PCs to use for the next steps of the analysis. These plots usually show one or two components at once, and for example the heatmaps are plotted for first 12 PCs by default (you can tune this). I suppose it would be enough to show some of the plots to justify the choice for the number of PCs. So PCA is step 1 in reducing the dimensions, so that clustering step won't take for ever and struggle with the excess of information. Different plots showing the PCs are there to help you to choose the number of PCs you want to continue the analysis with: whether it's 10, or 15, or 50 first principal components. After clustering, tSNE and UMAP are used for visualisation: to really show the data in 2D (step 2 in dimension reduction).
@@ChipsterTutorials Thank you so much!! Your guides are so helpful for beginners like me :)
Can I ask how did you draw the heatmap for each PC, what is exactly shown in the heatmap each PC ? I am really confused. Thank you a lot.
Of course you can, excellent questions! Those plots are from Chipster (chipster.csc.fi), but the codes within are pretty much directly from Seurat, so you can check the R-commands for example from here: satijalab.org/seurat/articles/pbmc3k_tutorial.html The heatmaps for the PCs show the "extreme" cells on the x-axis and "extreme" genes on y. They are "extreme" in their PCA scores, so those genes that basically best determine that particular principal component, i.e. the separation between the cells. Similarly for the cells: these cells "furthest away" (in the yellow or purple end) from each other on this spectrum of PC1. So what one might want to eye-ball with these plots is whether the genes reveal what that particular PC might be all about: for example, if the genes seem to be related to cell-cycle phase, one might want to consider regressing out that effect, or at least it's good to acknowledge this.
Summary: "AAAAAHAAAAHAHAHHAHUM ..."
Hi, Can you please do a tutorial on performing phylogenetic and phylogeographic analysis using RNA seq data??
Great idea! Unfortunately, our knowledge on the topic is currently a bit limited. We'll keep this idea in mind!