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
- Using references
- Annotation diagnostics
- Using multiple references
- Exploiting cell ontology
- Example dataset from pancreas
- Seurat v5 Command Cheat Sheet
- Dimensional Reduction Vignette - explains where things are stored and how to access them
- 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.
- "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)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
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 ]
No comments:
Post a Comment