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can i have the data mentioned in the video for processing?

Thx u so much, 非常好视频使我的空转水平激增

讲的真好！帮助了我很多，非常感谢～

Thank you so much for the insightful workshop ! The content was incredibly informative, and I appreciate the effort you put into making it accessible.

Hi there, thank you so much for this video and explanation. Is there any chance we can have the data and scripts to the Spatial Transcriptomics series as we do for other workshops here? Thank you very much for your consideration!

very motivating and engaging presentation with memes. :)

00:06 The workshop aims to increase understanding and accessibility of single-cell rna-seq technology and analysis tools. 02:32 Students will gain understanding of the challenges in single-cell RNA-seq analysis and the utility of existing and emerging methods. 07:08 Single cell RNA sequencing enables studying tissue heterogeneity and cell state transitions 09:28 Approaches to the study of gene expression patterns 14:12 Single cell RNA sequencing allows researchers to measure transcriptional differences across different groups of cells 16:37 Cell types change across all cells 21:20 Fluorescent activated cell sorting (FACS) and droplet encapsulation are popular technologies for cell separation and composition measurement. 23:22 DropSeek and inDrop/10x Chromium are two methods to barcode cells for sequencing 27:22 The BD Rhapsody platform offers an alternative to traditional reverse transcription and PCR. 29:42 The umi count is the total number of unique mrnas for gene that were captured. 34:08 Different methods have varying detection limits, affecting mRNA capture probability 36:14 Limitations of single-cell RNA sequencing technology 40:28 Cells can be labeled using hashtags and conjugated antibodies for easy identification in downstream analysis. 42:34 Cell surface markers are used in single cell transcriptomes, but the approach is expensive. 47:26 Most of the noise in the data is induced by the capture process rather than insufficient sampling. 49:55 Sightseek is a way of identifying specific subpopulations of cells based on cell surface markers. 54:49 Different methods are being used to identify cell types in transcriptional data. 57:21 The process involves using different antibodies to bind to specific cells and count the binding events. 1:03:29 Recap quiz on Kahoot 1:10:20 Single cell RNA sequencing protocols can induce non-linearities and dropouts, leading to potential gene misrepresentation. 1:18:54 Different experimental approaches to single cell RNA sequencing. 1:21:08 Pipeline for filtering and analyzing single-cell RNA sequencing data. 1:25:54 Single cell RNA sequencing captures only about 5% of mRNA from each cell. 1:28:04 Inefficiency in capturing transcripts and the relationship between mean mRNA and number of cells detected 1:32:21 mRNA leakage and low-quality cells can affect data analysis in single-cell RNA sequencing 1:34:23 Consider multiple parameters jointly to remove poorly amplified or damaged cells and doublets. 1:38:58 Gene expression data is transformed and normalized to identify the subset of most variable genes. 1:41:28 Analysis of gene expression levels and cell populations in data 1:46:39 Normalization is not necessary for single-cell RNA sequencing data 1:48:50 The data shows a gamma distribution with many cells having zero counts of a particular gene. 1:53:14 Various methods are used to reduce technical noise and focus on biological variants in single-cell RNA sequencing analysis 1:55:20 Introducing bias to your samples 2:00:11 Quality control is the first step in the analysis pipeline. 2:02:56 If facing installation issues with scanpi in PyCharm, try installing Anaconda from anaconda.com 2:12:34 Use of zero inflated model for mRNA sequencing analysis 2:16:03 Low overall reads and high fraction of mitochondrial mrnas 2:24:14 Mitochondrial RNA can be used as a potential quality control for single-cell nuclei sequencing. 2:26:24 Scan Pi software and required libraries 2:31:50 Importing libraries and understanding Python 2:34:08 Start loading data into python console. 2:39:25 Protein and actin genes are highly expressed in the data 2:41:46 Cells with higher percentages of mitochondrial genes tend to have lower total counts 2:46:34 Filtering the data by retaining cells with more than 200 genes and genes expressed in at least three cells. 2:49:16 Successful removal of outlier data points from the loaded data

Louvain😢

can i have the data mentioned in the video for processing?

which paper is refers to?

Thx for your clarification, also where can i find the code used

the hg19.refgene is an sqlite file that can be open with a DBbrowser sqlite or in R

My CEAS did bnot generate the R pdf, only the excel file

cistrome-ceas package in conda installed in an python 2.7 environment conda create -n old_python python=2.7 conda activate old_python conda install cistrome-ceas conda activate old_python

Great work! Thank you!

Thanks

In the Scree/Elbow plot made in the tutorial What is represented on the x and y axis why are the values given on y-axis negative

Thanks

I am following this course independently. Is it possible to get access to the data to attempt to replicate this on my computer?

I am following this course from outside UCLA. I wonder if its possible to complete this course on my ubuntu machine.

Genetic Information is the TimeStamps embedded in the Atoms. Life gets its energy from the time differences of the biological elements in our body. I am able to explain every physical, chemical and biological processes using this discovery.

Hi! thank you for you videos, im not in your program, but your lectures were the most useful in the internet. wish all the best in your work!

where can I get the Day2 data which was not found from the shared google drive

Can you please share the url of CEAS website. It's not as given in their research paper. Or any alternative tool.

On around 25 minutes, you're talking about normalizing aligned files. How would you normally do it if you don't want to speed up the process?

Could you please add the ATAC-SEQ data analysis to this channel?

such a great video I really appreciate it!

wow thanks really helpful.

Great video. Can we have access to the data and scripts?

Please share the data

Please share the data

20:00 31:00

This was very helpful and thank you so much!

where can I get the R codes

Lukasz! I was looking for some good guidance for building networks and imagine my surprise finding a trusted scientist aht inspired me from graduate school teaching it! I wish you well my friend! Thanks for continuing to teach me <3

what's the difference between multiplexing vs throughput?

Thank you immensely for the enlightening course you have provided us this semester. I have found the content to be rich and stimulating, contributing significantly to my academic growth. To better understand and review the course material, I am in need of some resources like the datasets and PowerPoint presentations used during the lectures. Would it be possible for you to share these materials via Google Drive or any other online platform you find convenient? This would be of great assistance to me and my classmates, enabling us to continue exploring and learning beyond the course end. I greatly appreciate your support and understanding. Looking forward to your positive response. Thank you!

When you do multiple rounds of smFISH, are you moving the slide from the microscope to the bench and back again after each round? How are you able to preserve the same position when you overlay the multiple images? A small shift of the slide placement could result in a shift image, right? How is this overcome?

Thank you for this great introduction! I have learned a ton!

Thank you for this amazing workshop and thank you for sharing it on youtube!

Can we get acces to your GoogleColab Link / Jypyter Notebook

Brilliant. Thanks a lot

he is so nervous

Bro, your dock is sick!

Thank you soooo much my UCLA sister. Im your neighbor from UCSD :D This content is awesome, very comprehensive and clearly-explained. Would it be possible for you to share the slides? I totally understand if there's copyright issue. These are such great learning materials

I'd like to thanks Arjun Bhattacharya and UCLA QCBio for making it available!

skip to 42:44 if you want to skip wait time while sergey got zoom up and running again.

Nice job! but quick question I am Ph.D. student in the pharmacology and toxicology department with a pharmacy degree background wanted to know how feasible it is to learn transcriptomes in 6 months internship?

Great! One observation, we cant do double, triplo fish, Fish and immunofluorescence etc.

This video is a legend... Thank you so much for the explanation!