Bookmarks: single cell RNA-seq tutorials and tools

These are my bookmarks for single cell transcriptomics resources and tutorials.

scRNA-seq introductions

How to make R objects for single cell data, e.g. SingleCellExperiment, SummarizedExperiment  

• How to take a spreadsheet with a matrix and convert it to the format needed for many other single cell RNA-seq tutorials, e.g. if you download a .csv.gz file from NCBI GEO

Getting Started with Seurat v4  (Satija lab tutorials list)

• Many tutorials here, for different scRNA-seq goals

Guided clustering tutorial with 3000 PBMC cells

• Setup Seurat object
• Standard pre-processing workflow & quality control
• Data normalization
• Identifying highly variable features (genes)
• Clustering, UMAP/tSNE plots
• Differential gene expression analysis

Basics of single cell analysis with Bioconductor

University of Cambridge intro to single cell RNA-seq analysis

• Identification of low-quality cells using MADs values

Lectures, textbooks, video tutorials, & interpretation

Determining the optimal number of clusters with elbow plots

OSCA Basics: Basics of Single-Cell Analysis with Bioconductor by Robert Amezquita, Aaron Lun, Stephanie Hicks, Raphael Gottardo (2024)

• Quality control - various QC metrics, identifying & removing low quality cells, diagnostic plots
• Normalization - library size, deconvolution, spike-ins, scaling and log-transformation
• Feature selection - quantifying variation, sequencing noises, batch effects, etc
• Dimensionality reduction - PCA plots
• Clustering - k means clustering, hierarchical clustering, subclustering
• Marker gene detection - dot plots, expression plots
• Cell type annotation - using other references, specific genes, markers, diagnostic heatmaps

SingleR Book: Assigning cell types with SingleR by Aaron Lun (2024)

• Using references
• Annotation diagnostics
• Using multiple references
• Exploiting cell ontology
• Example dataset from pancreas

Seurat:

• Seurat v5 Command Cheat Sheet
• Dimensional Reduction Vignette - explains where things are stored and how to access them

10x Genomics tutorial:

• Interpreting Cell Ranger Web Summary Files for Single Cell Gene Expression Assays, CG000329 . Highly recommended. They show what plot results look like for typical (good) samples, heterogeneous samples, and compromised (bad) samples.

YouTube tutorials:

• "Seurat Object Explained: Beginner's Guide and Demo" by chatomics
• Introduction to scRNA-seq Data Analysis by 10x Genomics: Cell Ranger, Loupe browser, cloud analysis
• Quality Assessment Using the Cell Ranger Web Summary by 10x Genomics

Data visualization - types of plots and how to make them

Data visualization methods in Seurat  - ridge plots, violin plots, feature plots, dot plots, heatmaps, visualizing coexpression

Split Dot Plot  - color code by an additional variable such as a condition

Clustered dot plot using ComplexHeatmap

Let's Plot 7: Clustered Dot Plots in the ggverse  (Eye Informatician)

tSNE vs UMAP, two methods to show clustering:

• Understanding UMAP  (Andy Coenen, Adam Pearce)
• tSNE vs UMAP: Global Structure

SCpubr - an R package to make publication ready plots for single cell RNA-seq

• Dim plots - dimensional reduction, similar to PCA or UMAP plots
• Feature plots - dim plot with a continuous scale for gene expression visualization across clusters
• Nebulosa plots - computes a density plot for specific gene markers so you can see where they are most expressed
• Bee Swarm plots
• Violin plots
• Ridge plots - multiple violin plots together
• Dot plots - show gene expression of different markers across different clusters
• Bar plots
• Box plots
• Geyser plots
• Alluvian plots
• Sankey plots
• Chord Diagram plots - circos plots
• Volcano plots

Labeling, label transfer, single cell reference mapping

Mapping and annotating query datasets (Satija lab, Oct 2023)

Web Resources for Cell Type Annotation  (10x Genomics Analysis Guide, 2024)

Azimuth: App for reference based single cell analysis - helps annotate clusters. You can upload the Seurat object .rds file to the app and get predictions. Troubleshoot error with Seurat v5.

 

Batch correction

Harmony R package  ( Korsunsky et al 2019 ,  Nature Methods ) - method for batch correction of single cell data

Receptor-Ligand interactions

LIANA: a LIgand-receptor ANalysis frAmework  - an R package and python tool for identifying and scoring receptor-ligand interactions in datasets

Spatial transcriptomics

Analysis of spatial datasets (Sequencing-based)

Analysis of spatial datasets (Imaging-based)

STELLAR ( Python based tool ) from Stanford to annotate single cell data, can be used for cross tissue and cross donor spatial transcriptomics data

Multiomics: scRNA-seq and scATAC-seq

Integrating scRNA-seq and scATAC-seq data  (Satija lab)

Integrative analysis in Seurat v5 (Satija lab, Oct 2023)

"For this vignette, we use a dataset of human PBMC profiled with seven different technologies , profiled as part of a systematic comparative analysis (pbmcsca). The data is available as part of our SeuratData package."

Azimuth annotation for scRNA-seq and scATAC-seq data (Satija lab)

scRNA-seq data analysis for non-programmers

Galaxy  - software for nonprogrammers to use for scRNA-seq analysis

• Galaxy tutorial using example data
• FTP access

Background reading - general

• "The technology and biology of single-cell RNA sequencing" , Kolodziejczyk et al 2015. Molecular Cell . Review.
• "An Introduction to the Analysis of Single-Cell RNA-Sequencing Data" , AlJanahi et al 2018. Mol Ther Methods Clin Dev .
• "The human mitochondrial transcriptome" , Mercer et al 2012. Cell . 
• "In the heart, mitochondrial transcripts comprise almost 30% of total mRNA, whereas mitochondria contribute a lower bound of ∼5% to the total mRNA of tissues with lower energy demands (adrenal, ovary, thyroid, prostate, testes, lung, lymph and white blood cells)."
• Normal proportions of mitochondrial transcripts vary by sample type -- this is relevant for setting cutoffs! Don't just use the percent.mt cutoff from the Seurat 3kPBMC tutorial.

Background reading - placenta, endometrium

• Mareckova M, Garcia-Alonso L, ..., Vento-Tormo R. "An integrated single-cell reference atlas of the human endometrium." Nature Genetics , 2024 . [PMID: 39198675 ; PMCID: PMC11387200 ]
• Human endometrium with/without endometriosis
• ReproductiveCellAtlas.org
• HECA = Human Endometrium Cell Atlas, >313k cells
• Integrated 6 scRNA-seq databases & new Mareckova (cells) dataset
  
  
• Wang M, Liu Y, ..., Wang H. "Single-nucleus multi-omic profiling of human placental syncytiotrophoblasts identifies cellular trajectories during pregnancy." Nature Genetics , 2022 . [PMID: 38267607 ; PMCID: PMC10864176 ]
• Human placenta at first and late third trimester
• n=6 placenta in early pregnancy (6-9 weeks gestation)
• n=6 placenta in late pregnancy (38-39 weeks gestation)
• Integrated separate snRNA-seq and snATAC-seq
  
  
• Ji K, Chen L, ..., Liu H. "Integrating single-cell RNA sequencing with spatial transcriptomics reveals an immune landscape of human myometrium during labour." Clin Trans Med, 2022 . [PMID: 37095651 ; PMCID: PMC10126311 ]
• Human myometrial tissue collected during C-section deliveries (singleton, uncomplicated full term)
• n=6 TIL, term in labor
• n=6 TNL, tern in non-labor
• Tissue was washed with PBS, minced and enzymatically dissociated briefly:
  3 mg/ml collagenase IV, 2 mg/ml papain , and 120 Units/ml DNases I ) at 37C for 20 min . Cell suspension was passed through stacked 70-30um filters, then passed through the Dead Cell Removal Kit (Miltenyi). Washed with PBS + 0.04% BSA twice.
  
  
• Koel M, Krjutskov, ... Altmae S. "Human endometrial cell-type-specific RNA sequencing provides new insights into the embryo–endometrium interplay." Human Reproduction , 2022 . [PMID: 36339249 ; PMCID: PMC9632455 ]
• Human endometrium cells sorted with FACS, then bulk RNA-seq
• n=16 healthy women from Estonia and Spain, mean age 29.7, normal BMI, no hormonal medication for 3 months; normal serum levels of progesterone, prolactin, and testosterone; negative for STIs, no uterine pathologies or endometriosis or PCOS, at least one live birth.
• Per woman, n=2 endometrial biopsies within the same menstrual cycle (early secretory & mid-secretory/receptive phases)
• NCBI GEO accession GSE97929 (32 samples): 16 paired endometrial samples 
  
  
• Sun T*,  Gonzalez TL* , ..., Pisarska MD. “Sexually dimorphic crosstalk at the maternal-fetal interface.” J Clin Endocrinol Metab , 2020 . [PMID: 32772088 ; PMCID: PMC7571453 ]
• Human placenta at late first trimester during CVS appointments
• NCBI GEO accession GSE131696  (6 samples) = Single cell RNA-seq
• NCBI GEO accession GSE131874  (8 samples) = Bulk total RNA-seq of matched decidua and placenta
• Tissue was washed with PBS, minced and enzymatically dissociated:
  300U/ml collagenase , 0.25%  trypsin , and 200μg/ml DNase I  at  37C for 90 min . Cells spun 1200 rpm for 10 min, resuspended in Chang medium (which contains 16% serum), and treated with 1x red blood cell lysis buffer for 15 min, then cells were washed again and strained through a 70um filter. [ Details ]