Presenter notes contain extra information which might be useful if you intend to use these slides for teaching.
Press P
again to switch presenter notes off
Press C
to create a new window where the same presentation will be displayed.
This window is linked to the main window. Changing slides on one will cause the
slide to change on the other.
Useful when presenting.
Presenter notes contain extra information which might be useful if you intend to use these slides for teaching.
Press P
again to switch presenter notes off
Press C
to create a new window where the same presentation will be displayed.
This window is linked to the main window. Changing slides on one will cause the
slide to change on the other.
Useful when presenting.
Before diving into this slide deck, we recommend you to have a look at:
Single-cell data consists of 4 main components:
All complex single-cell data formats will contain these 4 main sections, it's important to understand what these components are, how to identify them, and their importance in single-cell analysis
The core component of single-cell data is the gene expression matrix. This is a 2D matrix representing the gene expression values for each gene in the sequenced cells.
Typically each row of the matrix represents the genes and the columns represent the cells. However, it is important to note that this is not always the case and some matrices may need to be transposed before further processing.
The matrix can come in two different forms: full or sparse.
Full matrices act as one large table where every entry contains a value, including genes that were not sequenced (in which case their value will be 0). This is the simplest format but can be inefficient to store as many of the values in the matrix are redundant.
A more compact method to store these values is with a sparse matrix. This is a compressed representation of the matrix that removes all values that contain no useful information whilst keeping track of the overall structure of the matrix.
The first section of metadata that will be explored is the cell metadata, commonly referred to as barcodes when stored in a file. This contains metadata on each cell. This metadata includes the cell identifiers/barcodes, data on the origin of each cell, and quality control metrics generated with further analysis tools.
Cell barcodes consist of the 4 nucleotide letters and occasionally suffixed with a dash and a number (which has various meanings). If a file contains a column containing data in this format, then you are likely looking at cell metadata!
The next section of metadata to explore is the gene metadata. Commonly referred to as features when stored in a file. This contains metadata about each gene that was sequenced. This metadata includes gene identifiers/ensembl IDs, expression metrics and quality control metrics, both generated with additional analysis tools.
Gene identifiers typically consist of a sequence of letters and numbers. If a column in your file contains these types of values then your likely looking at the gene metadata! (If you're unsure, performing an internet search on one of the potential gene names will likely reveal whether that string represents a gene)
The last section of metadata to explore is the unstructured metadata. This is data that is not associated with an individual cell or gene but instead the data as a whole. Because of this, the metadata contained here can vary, but may include information such as when the cells were sequenced, what sequencing platform was used, the source of the cell samples, etc.
The following formats are fairly basic and only store sections of single-cell data (expression matrix, metadata, etc.)
Tabular files are the most basic format for storing single-cell data. This format stores: expression matrix, cell identifiers, and gene identifiers in a single file. The data is separated with either commas (for .csv) or tabs (for .tsv/Tabular).
Whilst being simplistic and easy to read, the tabular format does have limitations, mainly its inability to store any additional metadata outside of the gene or cell identifiers.
The Matrix Market format (MTX) is common for storing the gene expression matrix. This stores the expression data in the more compressed sparse matrix form. This type of file will likely be found alongside two additional files for the cell metadata (barcodes) and the gene metadata (features).
The following formats/objects are more complex and can all support storing all 4 core sections of single-cell data:
The AnnData format is a Python-based single-cell object built upon the HDF5 format. The primary library for performing single-cell analysis with AnnData objects is Scanpy.
It's important to note that AnnData does not support sparse matrices, therefore large single-cell files with many expression values of zero will not be very efficient to store in memory.
Supported languages: Python
Supported packages: anndata
Loom is another format based on HDF5. Loom objects are supported in various different programming languages and supports sparse matrices making it efficient for large data files.
Supported languages:
Supported packages:
Seurat is an R-based format that is commonly used with the Seurat package (the naming convention is a bit confusing!). This is a software package that contains various processing and analysis tools for single-cell data.
Supported languages: R
Supported packages: seurat
Single Cell Experiment (SCE) is another R-based format that is widely used within the Bioconductor ecosystem of tools for both processing and analysis of single-cell data.
Supported languages: R
Supported packages: SingleCellExperiment
CellDataSet (CDS) is the last of the R-based formats that is commonly used within the Monocle package which contains tools for performing various types of analysis.
Supported languages: R
Supported packages: monocle
Repository | Link to resource |
---|---|
NCBI | https://www.ncbi.nlm.nih.gov/ |
Human Cell Atlas: Data Explorer | https://explore.data.humancellatlas.org/projects |
CellXGene Collection | https://cellxgene.cziscience.com/datasets |
Single Cell Portal | https://singlecell.broadinstitute.org/single_cell |
EBI Single Cell Expression Atlas | https://www.ebi.ac.uk/gxa/sc/home |
There are many publicly available sources for reusable single-cell data. The table shows some common sources for acquiring this data.
This table is not extensive and there are many other resources available!
It is common for single-cell data to be stored in a compressed format in order to reduce filesizes and make transferring the data simpler. It is important to recognise when a file is compressed as using compressed data in downstream tools may cause errors to occur.
There are two forms of compression that are common:
Individual files are typically compressed with zip (.zip) or gzip (.gz) and should be unzipped prior to processing.
When data is stored in multiple files/folders they may be archived into a single file (commonly called a tarball), this is done with the TAR tool (.tar). Like individual file compression, you will need to untar/extract the .tar file before further processing.
Finally, it is also common for multiple files/folders to both be compressed and archived into a single file, this is indicated with the extension .tar.gz. In this case the data will need to be both unzipped and then extracted before the data is available in it's original form.
Follow one of our recommended follow-up trainings:
This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!
Author(s) |
![]() |
Editor(s) |
![]() ![]() |
Reviewers |
|
Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.
Before diving into this slide deck, we recommend you to have a look at:
Keyboard shortcuts
↑, ←, Pg Up, k | Go to previous slide |
↓, →, Pg Dn, Space, j | Go to next slide |
Home | Go to first slide |
End | Go to last slide |
Number + Return | Go to specific slide |
b / m / f | Toggle blackout / mirrored / fullscreen mode |
c | Clone slideshow |
p | Toggle presenter mode |
t | Restart the presentation timer |
?, h | Toggle this help |
Esc | Back to slideshow |