Data Analysis

In this stage, we loaded, analyzed, and plotted our RNAseq data to determine if there were correlations between certain genes and gestational age.

Gene Matrix

In our project, we loaded our RNAseq data in the form of a gene matrix. A gene matrix or count matrix represents the number of mRNA fragment reads mapped to a particular gene (it basically represents gene expression levels).

^ Our Gene Matrix ^

The row that is indicated by the red arrow contains the gene IDs, and the left column in blue are the patient IDs. The numbers in the matrix represent the number of mRNA fragment reads that map to each gene.

Plotting Graphs

To summarize and better understand our data, we used the ggplot2 library to plot different variables together in order to understand their correlations with one another.

plot a) displays the gene expression levels and shows that few genes are highly expressed within the samples

plot b) displays the levels of variance for the genes and shows that the level of variance is low for most genes

plot c) displays levels of correlation (Pearson Correlation) between the genes and gestational age, and shows that many genes show close to no correlation with gestational age. However, there are a select few genes who have positive and negative correlations with GA

plot d) compares the Spearman and Pearson correlation methods, showing that both will produce similar results

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