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The following tutorial uses the 3K PBMC data from 10x Genomics which is pre-loaded in scExplorer. Go to the Upload section located in the left menu (1), click on Tutorial (2) and select the 3K PBMC dataset (scExplorer have also pre-loaded two additional datasets from mouse cortex and from zebra fish cranial neural crest). If you are using your own dataset, you need to set a name for the analysis (3), indicate the species (4) and gene ID format (supports human, mouse, and zebrafish) (5), drag or select the desired file to upload (6), and click on Upload button (7). Optionally, if you want to receive notifications when the analysis is finished you can indicate your email in (8). In addition, you can run older analysis by indicating the UUID in (9).
Go to the Upload section located in the left menu (1), click on Tutorial (2), and select the 3K PBMC dataset. (scExplorer has also pre-loaded two additional datasets from mouse cortex and zebrafish cranial neural crest.)
A single-cell object is a n × d matrix where n are observations (e.g. barcoded cells), and d are dimensional vectors that correspond to cell features or genes.
h5ad is a file format used for storing annotated data matrices, commonly in single-cell RNA-seq analysis. The format is hierarchical data and allows for efficient storage and retrieval of large datasets. An h5ad file typically contains a matrix of expression data, along with associated metadata, such as gene annotations and cell metadata, all stored in a single file. This format is widely used in the Python-based single-cell RNA-seq analysis ecosystem, particularly with the Scanpy library.
rds is a file format used in R, particularly by the Seurat package, which is another widely-used tool for single-cell RNA-seq data analysis. rds files store R objects in a binary format, allowing for efficient saving and loading of complex R data structures. In the context of single-cell RNA-seq, an rds file typically contains:
- Seurat Objects: Comprehensive objects that include data matrices, metadata, analysis results, and other relevant information for single-cell analysis.
- Data Components: Including raw and normalized expression data, cell and gene annotations, and results from various analyses such as clustering and dimensionality reduction.
cellranger format consists of three different files. A feature file containing information about measured genes, a barcode file that contains the list of cell barcodes identified in each experiment, and a sparse matrix that contains gene expression counts for each cell. These three files together form the core output of a cellranger run, representing the processed single-cell RNA-seq data in a structured and efficient manner, ready for downstream analysis.
These file formats are crucial for handling the complexity and volume of data generated in single-cell RNA-seq experiments, providing a structured and efficient means of storing and accessing this information. In public repositories such as the Gene Expression Omnibus, data is usually stored in the three file formats described here.
After loading the dataset, below you will see a Dataset Summary (1) and three quality control (QC) plots. The plot on the left shows the number of genes per cell (2), the plot in the middle shows the total counts or UMIs per cell (3), and the left shows the percentage of mitochondrial genes per cell (4). Each dot in the plots represents a unique cell. To continue, click on Continue to Preprocessing (5) and to export the quality plots click on Export (6).
Drag file(s) here to upload.
Alternatively, you can select a file by clicking here
Dataset Summary
Quality Control Metrics
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