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Pipeline Catalog: Flow Cytometry

Uploading Data

Flow cytometry data should be uploaded as a collection of FCS files. There are two options for how the data can be formatted for upload:

  1. Use the sample IDs encoded in the FCS file names (e.g. SampleA.fcs), or
  2. Use a samplesheet CSV to specify the sample IDs for each FCS file.
Organizing data with a sample sheet

The advantages of using a sample sheet when uploading data are (a) the file names do not have to match the sample names, and (b) additional sample metadata can be added en masse. To use a sample sheet, simply create a file named samplesheet.csv in the folder containing the data to be uploaded with the format:

sample,file_1
SampleA,data_for_SampleA.fcs
SampleB,data_for_SampleB.fcs

Note that the file names do not need to match any particular pattern.

Any additional metadata can be added as columns to the sample sheet. For example, treatment, subject, or any other information can be included in columns to the right of file in the example above.

Apply FlowJo Gates

After setting up a gating scheme in FlowJo, it can be helpful to apply those gates across a large collection of FCS files.

Adding Gates:

Before running the analysis, you will need to upload the FlowJo gates as a Reference. This makes it easy to apply the same set of gates across multiple batches of FCS files.

  1. Save the FlowJo workspace as a file with the .wsp extension
  2. Upload that file to the Cirro References page

Running Analysis:

To apply the gating scheme to a batch of samples, first upload the input data in FCS format to Cirro. The analysis can be run on either (1) the complete batch of files (by default), or (2) you may select a subset of files to analyze.

Output Files:

Summary metrics will be provided in CSV format for both:

  1. The absolute number of cells from each file which were assigned to each population
  2. The percentage of cells which were assigned to each population (relative to its parent)

Citation:

  • Finak, Greg et al. “CytoML for cross-platform cytometry data sharing.” Cytometry. Part A : the journal of the International Society for Analytical Cytology vol. 93,12 (2018): 1189-1196. doi:10.1002/cyto.a.23663

Automated Quality Control Analysis

To help provide an automated first-pass analysis of flow cytometry data, we have implemented a workflow which uses a set of open source tools for the unsupervised analysis of these datasets. While manual inspection of flow cytometry datasets is difficult to automate in a single solution, our hope is that this tool can be used to provide a quick look at the contents of a particular dataset.

Flow Cytometry QC Steps

The analysis steps performed by the QC workflow are as follows:

  1. flowClean: Automated identification and removal of fluorescence anomalies in flow cytometry data (ref);
  2. flowAI: Automatic and interactive anomaly discerning tools for flow cytometry data (ref);
  3. If spillover data is available, perform compensation with flowWorkspace (ref);
  4. If possible, perform logicle transform with flowCore (ref);
  5. Automatically identify groups of cell using FlowSOM (ref);

Note: If any of the methods in steps 1-4 can not be performed, that particular step will be skipped.

Output data will be provided for each individual step as FCS files. In addition, the complete set of measurements will be output in CSV format, including the number of cells from each sample which were assigned to a given cluster.