diff --git a/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.Rmd b/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.Rmd
index 82bc0ac..339bb3a 100644
--- a/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.Rmd
+++ b/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.Rmd
@@ -36,6 +36,13 @@ txi <- readRDS("RObjects/txi.rds")
sampleinfo <- read_tsv("data/samplesheet_corrected.tsv", col_types = "cccc")
```
+First check the metadata.
+
+```{r}
+sampleinfo
+```
+
+
It is important to be sure that the order of the samples in rows in the sample
meta data table matches the order of the columns in the data matrix - `DESeq2`
will **not** check this. If the order does not match you will not be running the
@@ -261,14 +268,13 @@ The default significance level for independent filtering is `0.1`, however, you
should set this to the FDR cut off you are planning to use. We will use `0.05` -
this was the purpose of the `alpha` argument in the previous command.
-Remember
-in Session 7 we said that there is no need to pre-filter the genes as DESeq2
-will do this through a process it calls 'independent filtering'. The genes
-with `NA` are the ones `DESeq2` has filtered out.
+Earlier we said that there it is not necessary to carefully to pre-filter the
+genes as DESeq2 will do this through a process it calls 'independent filtering'.
+The genes with `NA` are the ones `DESeq2` has filtered out.
## Fitting an additive model
-So far we have fitted a simple model considering just "Status", but in reality
+So far we have fit a simple model considering just "Status", but in reality
we want to model the effects of both "Status" and "Time Point".
Let's start with the model with only main effects - an additive model with no
@@ -288,6 +294,13 @@ results.additive <- results(ddsObj, alpha = 0.05)
### The default contrast of `results`
+Let's look at the results.
+
+```{r}
+results.additive
+```
+
+
The `results` function has returned the results for the contrast "Infected vs
Uninfected". Let's have a look at the model matrix to understand why `DESeq2`
has given us this particular contrast.
@@ -332,7 +345,7 @@ resultsNames(ddsObj)
```{r}
results.d33vd11 <- results(ddsObj, name = "TimePoint_d33_vs_d11", alpha = 0.05)
-
+results.d33vd11
sum(results.d33vd11$padj < 0.05, na.rm = TRUE)
```
diff --git a/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.html b/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.html
index 383ee74..c690b08 100644
--- a/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.html
+++ b/Makerere_Course_Materials/04_DE_analysis_with_DESeq2.html
@@ -13,34 +13,228 @@
Introduction to Bulk RNAseq data analysis
-
-
+
+
-
-
-
-
+
+
+
+
-
-
+h1.title {font-size: 38px;}
+h2 {font-size: 30px;}
+h3 {font-size: 24px;}
+h4 {font-size: 18px;}
+h5 {font-size: 16px;}
+h6 {font-size: 12px;}
+code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+pre:not([class]) { background-color: white }
+
+
+code{white-space: pre-wrap;}
+span.smallcaps{font-variant: small-caps;}
+span.underline{text-decoration: underline;}
+div.column{display: inline-block; vertical-align: top; width: 50%;}
+div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;}
+ul.task-list{list-style: none;}
+