Identifying differential and equivalent gene expression between wildtype and FOXC2-deficient melanoma cells
Poster Number
36
Research or Creativity Area
Engineering & Computer Science
Abstract
Title: Identifying differential and equivalent gene expression between wildtype and FOXC2-deficient melanoma cells
This research aims to find the statistical equivalence between the RNA sequence results from a given dataset and gene expressions from the chosen research paper conducted by Haradon and Williams (2020). The dataset that is used in the academic paper comprises of RNA-seq data from both wild-type B16-F1 murine melanoma cells and a CRISPR-Cas9 engineered variant of these cells that is deficient in the FOXC2 transcription factor. This aims to explore FOXC2's role in regulating multiple oncogenic pathways in melanoma. By examining the equivalent and different p values from both the gene expression using equivalence testing and t test I will be able to determine which of the RNA genes are statistically significant while also checking if the data from the paper matches findings from my dataset. A p value is a measure used to help quantify whether the results are significant. In other words, how likely the data would randomly occur. When running the tests I utilize Jupyter notebooks(Python) in order to quantify the genes that we find to be different. I am also utilizing gene ontology analysis where I input the genes that are significantly differentially expressed from both the paper and my data set to determine which gene types are enriched. This research project has taught me how to run equivalent testing with different sets of data and ways to clean up data. As for the next steps of the research I plan to run multiple hypothesis correction for the different and equivalent p values, while investigating to whether there are any overlaps in the genes.
Location
Don and Karen DeRosa University Center (DUC) Poster Hall
Start Date
27-4-2024 10:30 AM
End Date
27-4-2024 12:30 PM
Identifying differential and equivalent gene expression between wildtype and FOXC2-deficient melanoma cells
Don and Karen DeRosa University Center (DUC) Poster Hall
Title: Identifying differential and equivalent gene expression between wildtype and FOXC2-deficient melanoma cells
This research aims to find the statistical equivalence between the RNA sequence results from a given dataset and gene expressions from the chosen research paper conducted by Haradon and Williams (2020). The dataset that is used in the academic paper comprises of RNA-seq data from both wild-type B16-F1 murine melanoma cells and a CRISPR-Cas9 engineered variant of these cells that is deficient in the FOXC2 transcription factor. This aims to explore FOXC2's role in regulating multiple oncogenic pathways in melanoma. By examining the equivalent and different p values from both the gene expression using equivalence testing and t test I will be able to determine which of the RNA genes are statistically significant while also checking if the data from the paper matches findings from my dataset. A p value is a measure used to help quantify whether the results are significant. In other words, how likely the data would randomly occur. When running the tests I utilize Jupyter notebooks(Python) in order to quantify the genes that we find to be different. I am also utilizing gene ontology analysis where I input the genes that are significantly differentially expressed from both the paper and my data set to determine which gene types are enriched. This research project has taught me how to run equivalent testing with different sets of data and ways to clean up data. As for the next steps of the research I plan to run multiple hypothesis correction for the different and equivalent p values, while investigating to whether there are any overlaps in the genes.
