This data is used for the Seurat version of the batch correction and integration tutorial on the Galaxy Training Network. The input data was provided by Seurat in the 'Integrative Analysis in Seurat v5' tutorial. The input dataset provided here has been filtered to include only cells for which nFeature_RNA > 1000. The other datasets were produced on Galaxy. The original dataset was published as: Ding, J., Adiconis, X., Simmons, S.K. et al. Systematic comparison of single-cell and single-nucleus RNA-sequencing methods. Nat Biotechnol 38, 737–746 (2020). https://doi.org/10.1038/s41587-020-0465-8.

This archive contains supplementary material used in the workshop "Introduction to single cell RNAseq analysis" taught by the Danish National Sandbox for Health Data Science. The course repo can be found on Github. Data.zip contains 6 10x runs on Spermatogonia development. 3 from healthy individuals and 3 from azoospermic individuals. Data has been already preprocessed using cellranger and can be loaded using Seurat (R) or scanpy (python). Slides.zip contains slides explaning theory regarding single cell RNAseq data analysis Notebooks.zip contains Rmarkdown files to follow the course in using R in Rstudio. Updated version of the notebooks.

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.
To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.

Data:

MDAlab_cDC1_maturation.tar : Docker image used for the analysis

This dataset contains processed single-cell RNA-sequencing (scRNA-seq) data from the PBMC 3K experiment.

It includes quality-control (QC) visualizations, cell-level metrics, clustering outputs, and exploratory analysis plots.

The dataset is designed to guide beginners and intermediate users through the essential steps of scRNA-seq preprocessing and analysis.

The PBMC 3K dataset represents human peripheral blood mononuclear cells sequenced using the 10x Genomics platform.

Included QC metrics help identify low-quality cells, doublets, stressed cells, and outliers based on standard thresholds.

The dataset covers filtering based on mitochondrial gene percentage, total UMIs, and number of detected genes.

All plots follow widely accepted scRNA-seq workflows commonly used in tools like Seurat, Scanpy, and SingleCellExperiment.

The QC violin plots illustrate distributions of nFeature_RNA, nCount_RNA, percent.mt, and other metrics used to assess cell quality.

The data also highlights the effect of filtering on overall dataset structure and variability.

Clustering-related files provide a visual understanding of how cells segregate into biologically meaningful groups.

Dimensionality-reduction plots also show patterns such as immune-cell diversity present in PBMC populations.

This dataset is suitable for hands-on learning, tutorial creation, classroom instruction, or benchmarking workflows.

It serves as a ready reference for researchers who wish to practice QC interpretation and cluster inspection.

The dataset allows quick reproduction of PBMC 3K quality-control visualizations without running the entire analysis pipeline.

It provides an accessible introduction to scRNA-seq analysis concepts for students, data scientists, and bioinformaticians.

Summary: Dendritic cells (DCs) orchestrate innate and adaptive immunity, by translating the sensing of distinct danger signals into the induction of different effector lymphocyte responses, to induce different defense mechanisms suited to face distinct types of threats. Hence, DCs are very plastic, which results from two key characteristics. First, DCs encompass distinct cell types specialized in different functions. Second, each DC type can undergo different activation states, fine-tuning its functions depending on its tissue microenvironment and the pathophysiological context, by adapting the output signals it delivers to the input signals it receives. Hence, to better understand DC biology and harness it in the clinic, we must determine which combinations of DC types and activation states mediate which functions, and how.
To decipher the nature, functions and regulation of DC types and their physiological activation states, one of the methods that can be harnessed most successfully is ex vivo single cell RNA sequencing (scRNAseq). However, for new users of this approach, determining which analytics strategy and computational tools to choose can be quite challenging, considering the rapid evolution and broad burgeoning of the field. In addition, awareness must be raised on the need for specific, robust and tractable strategies to annotate cells for cell type identity and activation states. It is also important to emphasize the necessity of examining whether similar cell activation trajectories are inferred by using different, complementary methods. In this chapter, we take these issues into account for providing a pipeline for scRNAseq analysis and illustrating it with a tutorial reanalyzing a public dataset of mononuclear phagocytes isolated from the lungs of naïve or tumor-bearing mice. We describe this pipeline step-by-step, including data quality controls, dimensionality reduction, cell clustering, cell cluster annotation, inference of the cell activation trajectories and investigation of the underpinning molecular regulation. It is accompanied with a more complete tutorial on Github. We anticipate that this method will be helpful for both wet lab and bioinformatics researchers interested in harnessing scRNAseq data for deciphering the biology of DCs or other cell types, and that it will contribute to establishing high standards in the field.

Data:

1. negative_cDC1_relative_signatures.csv : Negative signatures for performing Connectivity Map (cMAP) Analysis

2. positive_cDC1_relative_signatures.csv : Positive signatures for performing Connectivity Map (cMAP) Analysis