| dc.description.abstract | Single-cell cytometry, a research tool, has been used for almost half a century. Flow cytometry has long been the primary method for cytometric analysis, and data processing/analysis operations have been heavily influenced by well-known legacy applications. Regardless of the type of cytometric analysis (fluorescence, mass cytometry, or array-based cytometry) or its specific conditions, the measurement results do not represent the actual amount of features detected. Only the features used to label the probe are measured. These are typically luminescent molecules, metal isotopes, or continuous measurements. For this reason, data scientists are interested in density-based predictions rather than purely quantitative measurements of features. In this case, each of them is influenced by technical characteristics that you need to be aware of when analyzing data. However, specific questions regarding this complex and difficult-to-research method can impact the overall analysis. For example, changes in the frequency and function of cell populations can occur over time or in response to disease stage. These changes need to be examined by comparing the direction of change over time across populations, within individuals, or between individuals. The law is that changes over time in a single healthy individual tend to be smaller than differences between unrelated individuals/populations. Additionally, there are other experimental factors that can influence the analysis. Proper separation and proper storage of the sample are the most important factors. Conditions such as whether the sample is properly fixed before staining and whether the cells are stimulated during analysis affect all analyses. Single cell cytometry consists of the following steps: sample preparation, probe selection, panel design, staining, washing, cell number selection, cell barcoding mass labeling, data collection, data preprocessing and data analysis. | tr |
