R Tutorial: Differential expression data

Key Takeaways

next I'll describe the data that you will explore for the videos in this first chapter I will be using a data from a study of 344 patients with breast cancer specifically I'll analyze the differences between patients that are positive or negative for expression of the estrogen receptor an important clinical indicator in the exercises you will analyze data from a study of chronic lymphocytic leukemia or CLL for short the study measured gene expression in 22 patients with CLL 8 who were stable and 14 whose disease was progressing it's okay if you are unfamiliar with cancer biology the most important thing to note is then each of these experiments you are testing for differences between two groups of samples for every experiment you analyze in this course you will focus on three main data sets the first is the expression matrix which contains the expression measurements the second is the feature data which describes each of the measured features usually genes or proteins the third is the phenotype data which describes each of the samples in the study you will refer to these as X F and P to facilitate potentially small screens but I of course would recommend using more informative variable names in your real analysis code for the expression matrix each row is a feature that was measured and each column is one of the samples in the breast cancer experiment 22,000 283 genes were measured for 344 samples the feature data is a data frame with one row per feature that's its thus it's numbers of rows is equal to the number of rows of the expression matrix the columns describe the features in this case jeans here the columns correspond to the gene symbol the database identifier and the chromosomal location in the genome the phenotype data is a data frame with one row per sample thus it's number of rows is equal to the number of columns and the expression matrix the columns describe the samples in this case the sample identifier the age of the subject and whether or not the tumor sample was positive or negative for the estrogen receptor to both practice interacting with these three data sets and to become more familiar with the data you will create a box plot for a single gene the function box plot accepts a formula as its first argument you list the variables to be plotted on the y-axis to the left of the tilde and on the right the variable for the x-axis an R will create one box plot for each value of the x-axis variable you can add a title with the argument main to create box plots of a single gene you insert the gene expression to the left of the tilde the phenotype variable to the right and use the feature data to label the title of the plot specifically to plot the first gene of the breast cancer data I subset the first row of the expression matrix for the y-axis and I select the column er from the phenotype data frame for the x-axis for the title I use the feature data column symbol remember to subset to only include the first row corresponding to the first gene this gene appears to be similar in both groups now it's your turn to create a box plot for a gene in a leukemia study