Detection system and user interface for a flow cytometer system

1. A detection system for a flow cytometer having an interrogation zone through which a flow cytometer sample passes, comprising:

a detector that receives photonic inputs from the interrogation zone and produces an analog signal, the detector having a wide dynamic range;

an analog-to-digital converter (ADC), coupled to the detector, that converts an analog signal to a digital signal, the ADC having a high bit resolution;

wherein the digital signal includes an initial data set of the full dynamic range of the input signals from the flow cytometer sample; and

an analysis engine that recognizes aggregate particle events in the initial data set and annotates the recognized aggregate particle events, thereby creating an annotated data set of the full dynamic range of the input signals from the flow cytometer sample.

2. The detection system of claim 1 wherein the analysis engine recognizes a “peak-trough-peak” waveform produced by aggregate particle events and annotates the recognized aggregate particle events, thereby creating an annotated data set of the full dynamic range of the input signals from the flow cytometer sample.

3. The detection system of claim 2 wherein the analysis engine labels an aggregate particle event as an “aggregate particle event” and a non-aggregate particle event as a “nonaggregate particle event”.

4. The detection system of claim 1 wherein the high bit resolution of the ADC is defined as greater than or equal to 16-bits.

5. The detection system of claim 1 wherein the wide dynamic range of the detector is defined as greater than or equal to 100 dB.

6. A method of extracting and analyzing data from a flow cytometer system comprising the steps of:

collecting a full dynamic range of input signals from a flow cytometer sample;

storing an initial data set of the full dynamic range of the input signals from the flow cytometer sample;

recognizing aggregate particle events in the initial data set with use of an algorithm;

annotating aggregate particle events in the initial data set to create an annotated data set of the full dynamic range of the input signals from the flow cytometer sample;

storing the annotated data set; and

displaying at least one of the initial data set and the annotated data set.

7. The method of claim 6 wherein recognizing aggregate particle events in the initial data set occurs substantially simultaneously with collecting a full dynamic range of input signals from a flow cytometer sample.

8. The method of claim 6 wherein recognizing aggregate particle events in the initial data set includes identifying a “peak-trough-peak” waveform produced by aggregate particle events.

9. The method of claim 8 wherein annotating aggregate particle events in the initial data set includes labeling an aggregate particle event as an “aggregate particle event” and a non-aggregate particle event as a “non-aggregate particle event”.

10. The method of claim 9 wherein annotating aggregate particle events in the initial data set includes further labeling an aggregate particle event with the number of aggregate particles in the event.

11. The method of claim 6 further comprising the steps of:

allowing modification of at least one of the initial data set and the annotated data set;

saving the modified data set; and

exporting the saved data set.

12. The method of claim 11 wherein displaying at least one of the initial data set and the annotated data set includes permitting a user to observe at least one of the initial data set and the annotated data set from the full dynamic range of input signals, and permitting the user to identify the appropriate modifications for at least one of the initial data set and the annotated data set.

13. The method of claim 11 wherein allowing modification of at least one of the initial data set and the annotated data set includes permitting a user to manipulate at least one of the initial data set and the annotated data set across the full dynamic range of input signals from the flow cytometer sample and to generate a modified data set.

14. The method of claim 13 wherein allowing modification of at least one of the initial data set and the annotated data set further includes permitting the user to:

perform real-time comparisons between the initial data set, the annotated data set, and the modified data set on a single flow cytometer sample;

adjust or undo modifications, to make multiple adjustments on the same initial data set or annotated data set; and

generate at least one subset of data that corresponds to one or more sample populations contained within at least one of the initial data set and the annotated data set.

15. The method of claim 14 wherein annotating aggregate particle events in the initial data set includes labeling an aggregate particle event as an “aggregate particle event” and a non-aggregate particle event as a “non-aggregate particle event”, wherein allowing modification of at least one of the initial data set and the annotated data set further includes permitting the user to:

isolate at least one of the annotated aggregate particle events and the annotated non-aggregate particle events; and

remove the annotated aggregate particle events or the annotated non-aggregate particle events.

16. The method of claim 11 wherein allowing modification of at least one of the initial data set and the annotated data set further includes providing adjustable virtual instrument settings.

17. The method of claim 11 wherein allowing modification of at least one of the initial data set and the annotated data set further includes utilizing a graphical, menu-driven format that accepts and displays data sets.

18. The method of claim 17 wherein utilizing a graphical, menu-driven format includes displaying separate graphs that are generated from the initial data set, the annotated data set, and the modified data set in separate frames.

19. The method of claim 18 wherein aggregate particle events are displayed in a first color and non-aggregate particle events are displayed in a second color.