Talent Program

In 2013, the "Talents Program" was launched jointly by the China Association for Science and Technology and the Ministry of Education. It selects academically outstanding high school students with learning potential to engage in a one-year research and academic practice program under the guidance of university experts. Through this initiative, students experience scientific research firsthand, stimulate their interest in science, and enhance their innovative capabilities.

Over the past decade, the program has cultivated more than 11,000 students, serving as a talent pipeline for basic disciplines. It explores a new model for collaborative talent development between universities and high schools, creating a supportive environment for young scientific talent to emerge and thrive.

My primary contribution was serving as the critical bridge between wet lab experiments and computational analysis, guiding the project through the complete data science pipeline.

During the data analysis phase, I independently utilized R to perform comprehensive statistical analyses on gene expression data from six samples. My responsibilities included:

Data Processing & Statistical Modeling: Cleaning and processing raw spectral datasets, performing differential expression analysis, and calculating fold changes with corresponding p-values to identify statistically significant genetic alterations.

Data Visualization: Employing R's ggplot2 library to create a volcano plot, visualizing the relationship between statistical significance (-Log10 P-value) and magnitude of change (Log2 Fold Change) for each gene, making complex results immediately interpretable.

Functional Analysis: Conducting KEGG and GO enrichment analyses using R to identify significantly enriched pathways and biological processes among the differentially expressed genes.

Through these computational approaches, I transformed raw experimental data into meaningful biological insights, demonstrating how statistical analysis and data visualization techniques can reveal underlying patterns in complex biological systems.