Download Project - MSCBIO 2025

RNA-seq analysis is a valuable tool for investigating gene expression levels. After the
analysis is done you need to filter the information for genes that have significant
differences from a control (wild-type). Here you will take a .csv file containing a
gene list and their statistics from the analysis and filter it for the important genes
with statistical significance and get an idea of how the fold changes trend positively
or negatively.
60% >> assign1.py Diff_gene_genes3.csv (any of the three files)
Here are three files of differentially expressed genes between mutant and wild-type
zebrafish, provided by Michael Tsang’s developmental/regenerative lab. Your code
needs to take a single argument to read the file in using the Pandas package. Print
the number of total genes in the file and the column names only of the file (not a list
of the names). Hint: familiarize yourself with the column names it will help with the
rest of the assignment.
70% >> assign1.py Diff_gene_genes3.csv (any of the three files)
Next drop the columns named logCPM and LR as they will not be used in your
filtering. Now you need to focus on genes that have a pvalue less than 0.05 and
produce a table that only contains those genes. Print the number of genes from the
original table and after the Pvalue filter and the columns names. Notice a difference
from the total genes and less columns.
80% >> assign1.py Diff_gene_genes3.csv (any of the three files)
Next you want to look at genes with high fold changes. You will want a table that
contains all genes with a fold change (logFC) greater than 2. Print the number of
genes you have in this table which should all have pvalues < 0.05 and logFC > 2.
90% >> assign1.py Diff_gene_genes3.csv (any of the three files)
Now filter by an FDR (false discovery rate)<0.05 using the original file without
unwanted columns and a logFC > 1.5 this time. Print the total genes in this table and
the top ten genes in the table sorted so the most significant is on top. Hint: you will
no longer need to have the pvalue limit the FDR is a better stat to use in research
reporting.
100% >> assign1.py Diff_gene_genes3.csv (any of the three files)
Finally, you will use matplotlib to produce a histogram representing the fold change
in the experiment. This will give you an idea of how the fold change is distributed
positively or negatively. Set the bins to 30 to get a better idea of the fold change
distribution.