README file

README.html

@@ -440,6 +440,18 @@ <h2>DEG analysis</h2>
 <p><strong>Differential Expressed Genes</strong> are employed for
 detecting hub genes based on significant differential expression between
 two groups.</p>
+<ul>
+<li>Using <code>edgeR</code> and <code>limma</code> package, we can
+analyze differential expressions of <em>microarray</em> datasets with
+<strong>TMM (trimmed mean of M-values)</strong> method of normalization
+for microarray. Besides, <em>Adj.P.Value</em> is added identifying the
+significance of differential expression, and <strong>logFC (log Fold
+Change)</strong> will be added to describe the counts of differential
+exprssion by <em>positive or negative</em> values.</li>
+</ul>
+<p>Here, I normalize and filter the raw data first using limma and edgeR
+package based on value distribution by TMM normalization method.</p>
+<p><code>{r} dge &lt;- DGEList(finalnormalize) keep &lt;- filterByExpr(dge, design = design) filt &lt;- dge[keep,,keep.lib.sizes=F] norm &lt;- calcNormFactors.DGEList(filt, method = &quot;TMM&quot;)</code></p>
 </div>
 </div>
 

README.md

@@ -101,6 +101,16 @@ names(feature)
 
 **Differential Expressed Genes** are employed for detecting hub genes based on significant differential expression between two groups.
 
+* Using `edgeR` and `limma` package, we can analyze differential expressions of *microarray* datasets with **TMM (trimmed mean of M-values)** method of normalization for microarray. Besides, *Adj.P.Value* is added identifying the significance of differential expression, and **logFC (log Fold Change)** will be added to describe the counts of differential exprssion by *positive or negative* values. 
+
+Here, I normalize and filter the raw data first using limma and edgeR package based on value distribution by TMM normalization method.
+
+```{r}
+dge <- DGEList(finalnormalize)
+keep <- filterByExpr(dge, design = design)
+filt <- dge[keep,,keep.lib.sizes=F]
+norm <- calcNormFactors.DGEList(filt, method = "TMM")
+```