Skip to content

Update smallRNA-lab.rst #6

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
orzechoj wants to merge 1 commit into
base: master
Choose a base branch
from
Open

Update smallRNA-lab.rst #6

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
4 changes: 2 additions & 2 deletions source/smallRNA-lab.rst
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -139,7 +139,7 @@ Since the log transformation we will do later cannot handle cases with zero read

exp.data <- exp.data + 1

To compare expression lkevels from different libraries, the read counts have to be normalized to compensate for different sequencing depths etc. For this we will use the TMM normalization. This normalization method uses a trimmed mean of M- values (TMM) between each pair of samples to find a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes (if you are interested in the details, see http://genomebiology.com/2010/11/3/r25). To use this method we need to load the edgeR module. edgeR is an R module with many useful functions for normalizing RNA-seq data and finding differentially expressed genes. Here we will only use one of the normalization functions. ::
To compare expression levels from different libraries, the read counts have to be normalized to compensate for different sequencing depths etc. For this we will use the TMM normalization. This normalization method uses a trimmed mean of M- values (TMM) between each pair of samples to find a set of scaling factors for the library sizes that minimize the log-fold changes between the samples for most genes (if you are interested in the details, see http://genomebiology.com/2010/11/3/r25). To use this method we need to load the edgeR module. edgeR is an R module with many useful functions for normalizing RNA-seq data and finding differentially expressed genes. Here we will only use one of the normalization functions. ::

library(edgeR)

Expand Down Expand Up @@ -181,7 +181,7 @@ To see which microRNAs are highly expressed in samples with low PC1, type: ::

Another way to get a global overview of the data is to use clustering and plot heatmaps. You can do this with the following command: ::

heatmap(norm.data, scale="none", cexCol=0.2)
heatmap(norm.data, scale="none", cexCol=0.7, cexRow=0.2)

In the resulting plot each library is a column and each microRNA is a row. The color indicates the expression levels, with red being no reads and more yellow indicating higher expression. The dendrogram at the top shows how the libraries cluster together. What can you learn from looking at this plot?

Expand Down