[TEMPLATE] Time course experiment

SRP298264 Time course transcriptomic analysis of human melanoma A375 cells dying of immunogenic or non immunogenic apoptosis

IMPORTANT

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Introduction

This notebook demonstrates time course experiments following the workflow developed by Michael I. Love and colleagues. This notebook has been generalized to fit most time course bulk RNASeq datasets.

Data Summary

We can search and select a time course dataset by going to "Edit Data" and creating a single sample group with all relevant samples. Since datasets have varying column labels, we need to select the column denoting the intervention and the time series.

Action needed!

If you have selected different data, update the following code to select your time series and intervention columns as seen in the sample table. Also update the units variable to the proper units of time.

Note that since "recovery time" is separated by a space, we replace the space with "."

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Time course experiments

DESeq2 can be used to analyze time course experiments, for example to find those genes that react in a condition-specific manner over time, compared to a set of baseline samples. Here we demonstrate a basic time course analysis given the chosen time course dataset.

In this case, we need to pre-process our time variable to convert it from text like "4 hours" into a numeric variable.

Depending on the contents of your time_series_column, you may need to update the extract_numeric_time() function to properly extract just the numerical values.

The below chunk pre-processes the data and runs DESeq2. It may take a few minutes.

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Normalized counts for a gene with condition-specific changes over time

We can plot the counts for the groups over time using ggplot2, for the gene with the smallest adjusted p value, testing for condition-dependent time profile.

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Plot the log2 fold changes in a heatmap

Heatmap of log2 fold changes for genes with smallest adjusted p value.

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References

Love MI, Anders S, Kim V and Huber W. RNA-Seq workflow: gene-level exploratory analysis and differential expression [version 2; peer review: 2 approved]. F1000Research 2016, 4:1070 https://doi.org/10.12688/f1000research.7035.2