class: center, middle, inverse, title-slide # Single-cell RNA sequencing ~ Session 1
### Rockefeller University, Bioinformatics Resource Centre ###
http://rockefelleruniversity.github.io/scRNA-seq/
--- ## Overview In this course we are going to introduce basic analysis for single-cell RNAseq, with a specific focus on the *10X system*. The course is divided into three sessions. In the first session, we will introduce how to interpret the **Cell Ranger QC report** and how to do analysis with the **LOUPE Browser**. We will also demonstrate the customized analysis we can support right know. In the Second session, we will demonstrate how to process scRNAseq data and make QC reports with **Seurat**. In the third session, we will discuss how to to do more advanced analysis and QC with **Bioconductor** packages. --- class: inverse, center, middle # Cell Ranger results <html><div style='float:left'></div><hr color='#EB811B' size=1px width=720px></html> --- ## Cell Ranger [Cell Ranger](https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) is a widely used software for single-cell sequencing data analysis. It supports various kinds of analysis for *10X single-cell sequencing data*. - Alignment and counting - Analysis of VDJ changes - Datasets aggregation - Convert BCL files (raw files) into FASTQ files - Generation of customized GTF and Genome files --- ## Basic reports of Cell Ranger - [QC Report](../../data/web_summary.html) - cloupe file: can be processed by [LOUPE Browser](https://support.10xgenomics.com/single-cell-gene-expression/software/visualization/latest/what-is-loupe-cell-browser) <img src="imgs/cell_count.png" width="50%" style="display: block; margin: auto;" /> --- ## Session information <img src="imgs/session_info.png" width="75%" style="display: block; margin: auto;" /> --- ## Sequencing QC <img src="imgs/seq_qc.png" width="75%" style="display: block; margin: auto;" /> --- ## Mapping QC <img src="imgs/map_info.png" width="75%" style="display: block; margin: auto;" /> --- ## General information <img src="imgs/cell_count.png" width="75%" style="display: block; margin: auto;" /> - How many cells should we get? - Is the throughput enough? *It depends on the sample characteristics and library complexities* --- ## How to determin cell numbers? <img src="imgs/knee_plot.png" width="50%" style="display: block; margin: auto;" /> --- ## Knee plot - [Knee plot](https://liorpachter.wordpress.com/tag/knee-plot/) is applied to determine real cell numbers in a single-cell cohort. - The x-axis represented the barcodes **ranked by UMI counts** inside the cell barcodes. Y-axis represents the UMI counts detected per cell barcode. - A knee plot usually contains two bumps. We would take the **mid-point** of the first bump as a cut-off to differentiate real cells and backgrounds (empty droplets). - As we defined the real cells and empty droplets, we could estimate the transcripts detected in real cell or empty droplets. - The transcripts in empty droplet, so call **ambient RNA**, are cell-free RNAs falsely included in droplets. We will demonstrate how to remove interference of ambient RNAs in session 3. --- ## How to determine throughput? - Downsampling and evaluate median gene per cells in different throughput (Mean reads per cell) - Downsampling and evaluate proportion of unique UMIs in different throughput (Mean reads per cell) --- ## genes vs throughput <img src="imgs/gene_eval.png" width="75%" style="display: block; margin: auto;" /> *Seems not saturated yet* --- ## Saturation vs throughput - Saturation: uniquely detected UMIs in total UMI - Can evaluate both saturation and library complexity, like PCR duplication <img src="imgs/umi_eval.png" width="50%" style="display: block; margin: auto;" /> *Seems still not saturated and with lower library complexity, probably higher PCR duplicates.* --- ## tSNE plot and clustering <img src="imgs/tsne.png" width="100%" style="display: block; margin: auto;" /> *Seems the cells were grouped by UMI counts.* --- ## Data visualization with LOUPE Browser - Please install the latest version - How to access the expression of particular genes - How to use split view - How to define sub-groups - How to calculate differential gene expression - Could I import projections and more information generated by external programs? --- class: inverse, center, middle # Customized Analysis <html><div style='float:left'></div><hr color='#EB811B' size=1px width=720px></html> --- ## Demonstration Here we use an article published by Fuchs Lab as an example to demonstrate the customized analysis we have done here at the BRC. Please access this [article](https://elifesciences.org/articles/56980). - General QC plots - Data integration and normalization - Clustering - Annotate cell types - Pseudotime analysis - Differential gene expression and single-cell pathway analysis --- ## The next two sessions We will also use the data of the eLife paper in next two sessions. You can download the data from GEO [link](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE147415) or from DropBox [link](https://www.dropbox.com/sh/ztu0pucvu21szxm/AABGUhTryKp1T1CsoEUFRPcwa?dl=0).