That being said, lets start to analyze the dataset. Of course, conventional gating also has its drawbacks. I am just stressing these points as I think that these algorithms sound fancy and often appear in many figures, without really adding much and I think without the proper controls, the data generated may be dangerously misleading and mistakes are difficult to spot. However, using these algorithms, you would potentially get artifacts, since the algorithm isn´t biased (it will analyze IgM and CD25 expression on ALL cells). For example, If you have a-IgM on PerCP-Cy5.5 and a-CD25 on PE-Cy5, you wouldn´t usually notice this as you analyze IgM expression on B cells and CD25 expression on T cells using conventional gating. Especially regarding the panel design: Spillover of similar fluorochromes can cause artifacts in these algorithms that you wouldn´t notice using conventional gating. Assuming you compensated correctly, titrated your antibodies to get optimal results and made sure to use good panel building practices (with a 32-color panel, I am assuming you recorded on a spectral flow cytometer?), I´ll try to run down how to run such an analysis. In my experience, especially for these datasets, the saying stays: Garbage in, garbage out. I think it also depends on the dataset and the panel you are using. However, I found that quite often, I would find the same statement about my dataset with classical gating. The infamous high parameter data sets ) In theory, I like the idea behind these algorithms.
0 Comments
Leave a Reply. |