Flow cytometry analysis
Step by step gating strategy of targeted cellular markers. The workflow starts from data cleaning, selected cells based on complexity and viability, and the last gated population based on the cell markers. Examples of the analysis showed below.
Unique cell discovery pipeline
I have developed this pipeline during my doctorate research from 2017 to 2021. This workflow was applicable to any flow cytometry data as long as raw data (FCS files) was available. In general it constists of 4 steps:
Data cleaning
Data analysis
Deep analysis of specific immune cells
Statistical analysis
An output of unique cell discovery pipeline
The data were collected from 40 HIV patients and 20 healthy subjects. 10 thousand cells were collected from each sample and combined into three groups healthy subjects, HIV patients before ART (antiretroviral therapy), and HIV patients six months on ART.
Unique cells discovery pipeline applied to the selected cell population. The machine learning method successfully recognized the main difference among healthy subjects vs HIV patients.
The result generated from unsupervised method has equality with supervised analysis that published in Clinical Immunology (PMID: 33621667).
OPEN FOR COLLABORATION
The pipeline was not perfect yet, so I am open to collaboration on Flow Cytometry data analysis with other flow cytometry data. Let me know your interest by fill the contact form.