(c) Other categories can be left unchanged.
featureCounts will generate two output files for each
input: ‘Summary’ and ‘Counts’. Viewing the ‘Counts’ file
should show a list of GeneIDs and the number of aligned
reads to each GeneID. The ‘Summary’ file will show
statistics about the reads that were ‘Assigned’ vs ‘Unassigned’ with some information about why if they were not
assigned to a specific transcript.
3.11 Differential
Expression Analysis
3.11.1 Tools: DESeq2
To compare expression levels of genes systematically across multiple
samples and assess whether there are significant differences between
experimental conditions, the expression count data for each sample
need to be normalized and statistical tests performed. One advantage of the Galaxy environment is that it provides access to a
number of different tools for determining differential gene expression. The DESeq2 tool that we describe here will apply size factor
normalization by computing the geometric mean of read counts,
dividing the gene count by the geometric mean, and using the
median of these ratios for normalization.
1. Run DESeq2 to normalize and compare gene expression.
DESeq2 can be accessed on Galaxy Main under Genomics
Analysis ! RNA-seq ! DESeq2.
2. Depending on the experimental setup, multiple factors or factor levels may need to be added (see Note 32):
(a) For a comparison of two conditions (e.g.,
Drug vs. Control), we would only need one Factor
(effects_drug_x).
(b) The Factor level corresponds to the various conditions, so
for two conditions (Control or Drug), we would specify
two factor levels and the designated count files from
featureCounts.
DESeq2 takes count tables as input and generates a tabular file
containing (1) gene identifier, (2) mean normalized counts, averaged over all samples from both conditions, (3) logarithm (base 2)
of the fold change, (4) the standard error estimate for the log2 fold
change estimate, (5) Wald test statistic, (6) p-value for the statistical
significance of this change, and (7) p-value adjusted for multiple. In
addition to the list of genes, DESeq2 outputs a graphical summary
of results in .pdf format.
4 Notes
1. If you are new to Galaxy, we recommend you read through the
‘Galaxy 101’ tutorial and other relevant tutorials at https://
galaxyproject.org/learn/. Also consider the NGS and QC
tutorials.
RNAseq Workflow
507
featureCounts will generate two output files for each
input: ‘Summary’ and ‘Counts’. Viewing the ‘Counts’ file
should show a list of GeneIDs and the number of aligned
reads to each GeneID. The ‘Summary’ file will show
statistics about the reads that were ‘Assigned’ vs ‘Unassigned’ with some information about why if they were not
assigned to a specific transcript.
3.11 Differential
Expression Analysis
3.11.1 Tools: DESeq2
To compare expression levels of genes systematically across multiple
samples and assess whether there are significant differences between
experimental conditions, the expression count data for each sample
need to be normalized and statistical tests performed. One advantage of the Galaxy environment is that it provides access to a
number of different tools for determining differential gene expression. The DESeq2 tool that we describe here will apply size factor
normalization by computing the geometric mean of read counts,
dividing the gene count by the geometric mean, and using the
median of these ratios for normalization.
1. Run DESeq2 to normalize and compare gene expression.
DESeq2 can be accessed on Galaxy Main under Genomics
Analysis ! RNA-seq ! DESeq2.
2. Depending on the experimental setup, multiple factors or factor levels may need to be added (see Note 32):
(a) For a comparison of two conditions (e.g.,
Drug vs. Control), we would only need one Factor
(effects_drug_x).
(b) The Factor level corresponds to the various conditions, so
for two conditions (Control or Drug), we would specify
two factor levels and the designated count files from
featureCounts.
DESeq2 takes count tables as input and generates a tabular file
containing (1) gene identifier, (2) mean normalized counts, averaged over all samples from both conditions, (3) logarithm (base 2)
of the fold change, (4) the standard error estimate for the log2 fold
change estimate, (5) Wald test statistic, (6) p-value for the statistical
significance of this change, and (7) p-value adjusted for multiple. In
addition to the list of genes, DESeq2 outputs a graphical summary
of results in .pdf format.
4 Notes
1. If you are new to Galaxy, we recommend you read through the
‘Galaxy 101’ tutorial and other relevant tutorials at https://
galaxyproject.org/learn/. Also consider the NGS and QC
tutorials.
RNAseq Workflow
507