ejc043 / nash_rnaseq

The purpose of this project was to determine differential gene expression with proinflammatory biomarkers with control v.s. diet-induced NASH mouse models. The paper can be found here: https://pubmed.ncbi.nlm.nih.gov/31398325/

NASH_RNASEQ

SAMPLES: https://www.ncbi.nlm.nih.gov/gds/?term=GSE129516[ACCN]%20AND%20gsm[ETYP] each barcode, matrix, and features file for controls1-3 and nash1-3 were found here: https://www.ncbi.nlm.nih.gov/gds/?term=GSE129516[ACCN]%20AND%20gsm[ETYP] http://128.197.168.11/kkarri/NASH/data/

Because the purpose of this project was to determine differential gene expression of proinflammatory biomarkers, I decided to use processed matrix data over raw FASTQ files. All processed files can be found above. I used many resources to determine differential gene expression, including the folloowing:

https://www.youtube.com/watch?v=aZIauqUp0oQ&ab_channel=RebeccaJCarlson https://pachterlab.github.io/kallistobustools/tutorials/kb_kite/python/kb_kite/ https://chanzuckerberg.github.io/scRNA-python-workshop/preprocessing/00-tabula-muris.html

The output of this code are multiple graphs as png files:

1. violin plot for n_counts of gene and log of n_counts
2. histogram for cell quality controls
3. scatter plot where x axis is mean expression of genes and y axis is dispersion of genes (normalized), where second graph has nonnormalized values on y axis
4. PCA plot of Tnfsf18
5. UMAP of Tnfsf4, Tnfsf18, Tnfsf10 (similar gene expression)
6. clustering by partitioning N cells into k clusters
7. violin plot of Prex2 and Il17a which exhibit differential expression when computing by leiden algorithm