Signal Processing

Overview of Signal Processing

Definition: Signal processing is the manipulation and analysis of signals to extract meaningful information and improve data quality
Purpose in Flow Cytometry:
- Remove Noise: To reduce background noise and improve the signal-to-noise ratio
- Correct for Artifacts: To correct for spectral overlap, cell doublets, and other artifacts
- Extract Features: To extract relevant features from the data, such as the mean fluorescence intensity or the cell cycle distribution
Common Signal Processing Techniques:
- Binning
- Compensation
- Pulse Processing
- Baseline Restoration
- Background Correction

Definition: A data reduction technique in which data points are grouped into discrete intervals or bins
Purpose:
- Reduce Data Size: To reduce the size of the data file
- Smooth Data: To smooth out noise and reduce variability
- Visualize Data: To create histograms or other visualizations of the data
Implementation:
- Divide the range of data values into a set of non-overlapping bins
- Count the number of data points that fall into each bin
- Replace the original data points with the bin number or the average value of the data points in the bin
Considerations:
- Bin Size: The size of the bins can affect the appearance of the data and the amount of information that is lost
- Number of Bins: The number of bins can affect the resolution of the data
Use case example:
- Creating histogram plots

Definition: A mathematical process used to correct for spectral overlap between fluorochromes
Purpose:
- Accurate Quantification: To accurately quantify the expression of multiple antigens in a single sample
- Multi-Color Analysis: To allow for the use of multiple fluorochromes in a single experiment
Implementation:
- Use compensation controls to calculate compensation values
- Apply a compensation matrix to the experimental data to remove the contribution of each fluorochrome from the other channels
Considerations:
- Compensation Controls: Use high-quality compensation controls
- Compensation Values: Verify compensation values
- Overcompensation and Undercompensation: Proper optimization is required
Compensation is important due to the emission spectra!
- Not all channels only detect the respective fluorochrome. A fluorochrome can spill into another channel’s detection range.

Definition: A set of techniques used to analyze the shape and characteristics of the electrical pulses generated as cells pass through the laser beam in a flow cytometer
Purpose:
- Doublet Discrimination: to differentiate between single cells and cell aggregates
- Cell Sizing: to estimate cell size
- Signal Quantification: to accurately quantify the amount of fluorescence emitted by each cell
Common Pulse Processing Parameters:
- Area: the area under the pulse curve
- Width: the duration of the pulse
- Height: the peak amplitude of the pulse
Implementation:
- Use pulse processing software to analyze the raw data from the flow cytometer
- Gate on single cells based on pulse area, width, and height
- Use pulse processing parameters to correct for variations in cell size and shape
How it relates to voltage:
- The electrical impulses correlate to voltage

Definition: A process used to correct for baseline drift or offset in the flow cytometry data
Purpose:
- Accurate Quantification: To ensure that the fluorescence and scatter values accurately reflect the properties of the cells
- Data Comparison: To allow for comparison of data from different experiments
Causes of Baseline Drift:
- Instrument Instability
- Temperature Changes
- Electrical Noise
Implementation:
- Measure the baseline signal in a region of the data where there are no cells
- Subtract the baseline signal from all of the data points
Considerations:
- Baseline Location: Choose a baseline location that is representative of the entire data set
- Baseline Variability: Account for variations in the baseline signal over time

Definition: A process used to subtract background signal from the flow cytometry data
Purpose:
- Improve Sensitivity: To improve the sensitivity of the assay by reducing background noise
- Accurate Quantification: To ensure that the fluorescence values accurately reflect the expression of the target antigens
Sources of Background Signal:
- Autofluorescence: Natural emission of light by cells
- Non-Specific Antibody Binding: Binding of antibodies to unintended targets
- Electronic Noise: Noise generated by the flow cytometer electronics
Methods for Background Correction:
- Autofluorescence Subtraction: Subtract the autofluorescence signal from all of the data points
- Isotype Control Subtraction: Subtract the signal from the isotype control antibody from the experimental samples
- Gating: Exclude cells that are not of interest from the analysis
Considerations:
- Control Samples: Use appropriate control samples to measure the background signal
- Subtraction Method: Choose a subtraction method that is appropriate for the data
For example: when selecting the best FMO control to use

Poor Resolution:
- Possible Causes:
  - Excessive noise
  - Incorrect compensation
  - Poor gating
- Troubleshooting Steps:
  - Reduce noise
  - Verify compensation settings
  - Optimize gating strategy
Inaccurate Results:
- Possible Causes:
  - Incorrect baseline correction
  - Inadequate background subtraction
  - Data analysis errors
- Troubleshooting Steps:
  - Verify baseline correction
  - Optimize background subtraction
  - Inspect data analysis methods
Signal Saturation:
- Possible Causes:
  - Overwhelmingly strong signal
- Troubleshooting Steps:
  - Increase concentration to have the signal fall within the dynamic range
  - Assess the instrument settings

Signal Processing: The manipulation and analysis of signals to extract meaningful information and improve data quality
Binning: A data reduction technique in which data points are grouped into discrete intervals
Compensation: A mathematical process used to correct for spectral overlap between fluorochromes
Pulse Processing: A set of techniques used to analyze the shape and characteristics of the electrical pulses generated as cells pass through the laser beam
Baseline Restoration: A process used to correct for baseline drift or offset in the flow cytometry data
Background Correction: A process used to subtract background signal from the flow cytometry data