Standards

Overview of Data Standards

Definition: Data standards are a set of guidelines and specifications that ensure data is collected, stored, and analyzed in a consistent and reproducible manner
Importance:
- Data Sharing: Facilitates data sharing and collaboration among researchers
- Reproducibility: Ensures that experiments can be reproduced by others
- Data Integration: Enables the integration of data from different experiments and different laboratories
- Long-Term Archiving: Ensures that data can be accessed and analyzed in the future
Key Data Standards in Flow Cytometry:
- Image File Format
- FCS Format
- Listmode
- MIFlowCyt Checklist
- Storage Requirements

Definition: The format in which images from imaging flow cytometers are stored
Common Formats:
- TIFF (Tagged Image File Format): A flexible and widely supported format for storing images
- JPEG (Joint Photographic Experts Group): A lossy compression format that is suitable for storing images with complex scenes
- PNG (Portable Network Graphics): A lossless compression format that is suitable for storing images with sharp lines and text
Considerations:
- Lossy vs. Lossless Compression: Lossy compression reduces file size but can also reduce image quality; lossless compression preserves image quality but results in larger file sizes
- Metadata: The image file should contain metadata that describes the image, such as the date and time it was acquired, the instrument settings, and the staining protocol
For flow cytometry, it is recommend to keep all files in the lossless compression to maintain image quality

Definition: A data file format specifically designed for flow cytometry data
Purpose:
- Standardize data storage: Provides a standardized way to store flow cytometry data, ensuring that it can be read and analyzed by different software packages
- Preserve Metadata: Stores metadata about the experiment, such as the instrument settings, the sample preparation protocol, and the antibody information
Versions:
- FCS 2.0: The original FCS standard, released in 1990
- FCS 3.0: An updated version of the FCS standard, released in 1997
- FCS 3.1: A further updated version of the FCS standard, released in 2010
Structure:
- Header: Contains information about the FCS file, such as the version number and the location of the text and data segments
- Text Segment: Contains metadata about the experiment, such as the instrument settings, the sample preparation protocol, and the antibody information
- Data Segment: Contains the raw data from the flow cytometer, such as the fluorescence and scatter values for each cell
FCS files must include the following:
- Keywords: instrument settings (e.g. PMT voltages)
- Units: values or parameters
- Scaling: such as linear or logarithmic

Definition: A method of storing flow cytometry data in which each event (cell or particle) is recorded as a list of values for each parameter being measured
Advantages:
- Flexibility: Allows for the analysis of the data using different gating strategies and analysis methods
- Preservation of Raw Data: Preserves the raw data from the flow cytometer, allowing for reanalysis of the data in the future
Storage:
- Listmode data is typically stored in FCS files
- The data segment of the FCS file contains the listmode data
Parameters:
- Each parameter in the listmode data corresponds to a specific measurement made by the flow cytometer, such as forward scatter, side scatter, or fluorescence intensity

Definition: A checklist of the minimum information that should be reported about a flow cytometry experiment to ensure reproducibility and data quality
Purpose:
- Promote Transparency: Encourages researchers to be transparent about their methods and results
- Improve Reproducibility: Provides a standardized way to report flow cytometry experiments, making it easier for others to reproduce the results
- Enhance Data Quality: Ensures that all relevant information is included in the data analysis
Categories:
- Experiment Information: Details about the experiment, such as the title, the date, and the investigators
- Sample Information: Details about the samples being analyzed, such as the source, the treatment, and the cell type
- Instrument Information: Details about the flow cytometer used, such as the manufacturer, the model number, and the laser configuration
- Reagent Information: Details about the antibodies and dyes used, such as the manufacturer, the clone number, and the fluorochrome
- Data Analysis Information: Details about the data analysis methods used, such as the gating strategy and the compensation method
Before submitting a study report, be sure that the MI flow Cyt checklist has been reviewed!

File Size: Flow cytometry data files can be quite large, especially for high-dimensional experiments
Storage Media:
- Use reliable storage media, such as hard drives, solid-state drives, or cloud storage
- Back up the data regularly to prevent data loss
Long-Term Archiving:
- Store the data in a format that is likely to be readable in the future (e.g., FCS)
- Include metadata that describes the experiment and the data analysis methods
- Consider using a data repository to archive the data

Consistent Naming Conventions:
- Develop and adhere to a consistent naming convention for files and folders
- Include information about the experiment, the date, and the sample in the file name
Folder Organization:
- Organize data into folders based on experiment, date, or other relevant criteria
- Use a hierarchical folder structure to make it easy to find data
Documentation:
- Keep detailed notes about the experiment, the data analysis methods, and any problems that were encountered
- Store the notes in a text file or spreadsheet in the same folder as the data
Version Control:
- Use version control software to track changes to data and analysis scripts
- This makes it easy to revert to previous versions if needed

Incompatible Files:
- Possible Causes:
  - Incorrect file format
  - Corrupted file
- Troubleshooting Steps:
  - Convert the file to the correct format
  - Repair or restore the file
Missing Metadata:
- Possible Causes:
  - Incomplete data entry
  - Software error
- Troubleshooting Steps:
  - Enter the missing metadata manually
  - Update the software
Data Loss:
- Possible Causes:
  - Hardware failure
  - Human error
- Troubleshooting Steps:
  - Restore from backup
  - Implement better data management practices

Data Standards: Guidelines and specifications that ensure data is collected, stored, and analyzed in a consistent and reproducible manner
Image File Format: The format in which images from imaging flow cytometers are stored
FCS Format: A data file format specifically designed for flow cytometry data
Listmode Data: A method of storing flow cytometry data in which each event is recorded as a list of values for each parameter
MIFlowCyt Checklist: A checklist of the minimum information that should be reported about a flow cytometry experiment
Metadata: Data that describes other data