Cell Ranger - Gene Expression
Last updated
Last updated
Cell Ranger is a set of analysis pipelines that process Chromium single cell data to align reads, generate feature-barcode matrices and perform clustering and gene expression analysis for 10x Genomics Chromium Technology [1].
The 'cellranger count' pipeline from Cell Ranger v9.0.0 [2] has been wrapped in Partek Flow as Cell Ranger - Gene Expression task. It does not comprehensively cover all of the options and analysis cases Cell Ranger can handle for now (e.g. does not include Cell Ranger multi support required for 10x Flex data), but converts FASTQ files from 'cellranger mkfastq' and performs alignment, filtering, barcode counting, UMI counting for Single cell gene expression and Feature Barcode data. The output gene expression count matrix in .h5 format (both raw and filtered available for users to download in the output page of task details) then becomes the starting point for downstream analysis for scRNA-seq in Flow. For Feature Barcode data, Flow outputs a unified feature-barcode matrix that contains gene expression counts alongside Feature Barcode counts for each cell barcode.
Note: When using the Cell Ranger - Gene Expression task in Partek Flow, there are more restrictions on sample name -- sample name can only contain letters, digits, underscores and dashes. Please edit the sample names in the Metadata tab in Partek Flow to remove any other characters (e.g. space etc). The index files are not required (if the index files have been added as individual samples automatically during import, please delete these before running the task).
To run the Cell Ranger - Gene Expression task for scRNA-seq data in Flow, select Unaligned reads data node, then select Cell Ranger - Gene Expression in the 10x Genomics section (left panel, Figure 1). For Feature Barcode data, there will be two data nodes once the FASTQ files have been imported into Flow properly - mRNA and protein (right panel, Figure 1). Users should select mRNA data node to trigger the Cell ranger - Gene Expression task.
To create the same reference genomes that are provided in Cell Ranger by default (2020-A), the transcriptome annotations are respectively GENCODE v32 for human and vM23 for mouse, which are equivalent to Ensembl 98 [4]. If users don't have any options in the dropdown list, they can click Add annotation model (GTF file) for Index, or New assembly... (FASTA file) for Assembly and upload the files.
Click the Finish button to run the task as default (Figure 2).
Note: Partek Flow will take time to create the reference before the task runs, if the Cell Ranger ARC reference has not yet been created.
A new data node named Single cell counts will be displayed in Flow if the task has been finished successfully (Figure 4). This data node contains a filtered feature barcode count matrix for gene expression data, but a unified feature-barcode matrix that contains gene expression counts alongside Feature Barcode counts for each cell barcode for Feature Barcode data. To open the task report when the task is finished, double click the output data node, or select the Task report in the Task results section after single clicking the data node. Users then will find the task report (Figure 5) is the same to the ‘Summary HTML’ from Cell Ranger output.
Task report is sample based. Users can use the dropdown list on the top left to switch samples. Under the sample name, there are two tabs on each report - Summary report and Analysis report (Figure 5). Important information on Estimated Number of Cells, Mean Reads per Cell, Median Genes per Cell, as well as information on Sequencing, Mapping, and Sample are summarized in different panels. The Barcode Rank Plot has also been included as an important piece in the Cells panel in the Summary report (Figure 5).
Another two plots -biplots of Sequencing Saturation and Median Genes per Cell to Mean Reads per Cell have been included in the Analysis report as they are important metrics to library complexity and sequencing depth (Figure 6).
Details will be exhibited and the panel will be expanded correspondingly if the the tooltip is clicked. In the example below, the plot of Median Genes per Cell has been expanded while the Sequencing Saturation plot has not (Figure 7).
Other than two additional panels summarized information for Antibody Sequencing and Antibody Application have been added, the task report for Feature Barcode data is the same to scRNA-seq data report.
Users can click Configure to change the default settings in Advanced options (Figure 2).
Include introns: Count reads mapping to intronic regions. This may improve sensitivity for samples with a significant amount of pre-mRNA molecules, such as nuclei.
Expected cells: Expected number of recovered cells. Default: 3,000 cells.
Force cells: Force pipeline to use this number of cells, bypassing the cell detection algorithm. Use this if the number of cells estimated by Cell Ranger is not consistent with the barcode rank plot.
Memory limit (GB): Restricts Cell Ranger - Gene Expression to use specified amount of memory (in GB) to execute pipeline stages.
Once the Assay type has been selected, users will be asked to choose the Reference assembly files for the Cell Ranger - Gene Expression task (Figure 2). Cell Ranger ARC 2.0.0 is used to create the reference assembly for all 10x Genomics analysis pipelines. To create and use a reference assembly, Cell Ranger ARC requires a reference genome sequence (FASTA file) and gene annotations (GTF file), here are the .
While for Feature Barcode data, there are more information needed besides reference assembly. An additional section of Protein has been added to the interface if Single cell gene expression + Cell surface protein has been selected for Feature Barcode data (Figure 3). Users need to Select data node and select the correct data for feature of antibody capture or protein in a new pop-up window (top right, Figure 3). Next, upload the feature reference file (.csv) prepared for the dataset. A Feature Reference CSV file declares the molecule structure and unique Feature Barcode sequence of each feature present in the experiment. It should include at least six columns: id, name, read, pattern, sequence and feature_type. An has been linked here. Users can download it by clicking the link and use it as a template for their own data. But for more details, please refer to 10x Genomics webpage [5].
If you need additional assistance, please visit to submit a help ticket or find phone numbers for regional support.
