diff --git a/.~lock.Schrick-Noah_CS-6643_Lab-4.docx# b/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
index dc2814b..20caa52 100644
--- a/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
+++ b/.~lock.Schrick-Noah_CS-6643_Lab-4.docx#
@@ -1 +1 @@
-,noah,NovaArchSys,30.09.2022 11:57,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
+,noah,NovaArchSys,30.09.2022 12:12,file:///home/noah/.config/libreoffice/4;
\ No newline at end of file
diff --git a/Schrick-Noah_CS-6643_Lab-4.R b/Schrick-Noah_CS-6643_Lab-4.R
index 5038cf7..51cab6c 100644
--- a/Schrick-Noah_CS-6643_Lab-4.R
+++ b/Schrick-Noah_CS-6643_Lab-4.R
@@ -81,3 +81,52 @@ p <- ggplot(mygene.data.df, aes(x=phenotype, y=gene, fill=phenotype)) +
           stat_boxplot(geom ='errorbar') + geom_boxplot()
 p <- p + xlab("MDD versus HC") + ylab(mygene)
 p
+
+
+#### Part D: t-test for all filtered genes
+
+# Put it all together into a function to run in loop. 
+# First write a function that computes t-test for one gene.
+# i is the data row for the gene
+ttest_fn <- function(i){
+  mygene <- rownames(GxS.covfilter)[i]
+  t.result <- t.test(GxS.covfilter[i,] ~ pheno.factor)
+  tstat <- t.result$statistic
+  pval <- t.result$p.value
+  # return vector of three things for each gene    
+  c(mygene, tstat, pval) 
+} 
+
+# Testing on the second gene
+ttest_fn(2)
+
+## Testing on the rest:
+# initialize an empty matrix to store the results
+ttest_allgene.mat <- matrix(0,nrow=nrow(GxS.covfilter), ncol=3)
+# run analysis on all gene rows
+for (i in 1:nrow(GxS.covfilter)){
+  ttest_allgene.mat[i,] <- ttest_fn(i) 
+}
+# convert matrix to data frame and colnames
+ttest_allgene.df <- data.frame(ttest_allgene.mat)
+colnames(ttest_allgene.df) <- c("gene ", "t.stat", "p.val")
+
+# sort based on p-value
+ttest_allgene.sorted <- ttest_allgene.df                 %>% 
+  mutate_at("p.val", as.character) %>%
+  mutate_at("p.val", as.numeric)   %>%
+  arrange(p.val) # sort 
+ttest_allgene.sorted[1:10,] # look at top 10
+
+## Plot the result of the top gene
+# create data frame for gene
+myrow <- which(ttest_allgene.df$gene==ttest_allgene.sorted[1,1])
+mygene<-rownames(GxS.covfilter)[myrow]
+mytopgene.data.df <- data.frame(mygene=GxS.covfilter[myrow,],
+                                phenotype=pheno.factor)
+
+# boxplot
+p <- ggplot(mytopgene.data.df, aes(x=phenotype, y=mygene, fill=phenotype)) +
+  stat_boxplot(geom ='errorbar') + geom_boxplot()
+p <- p + xlab("MDD versus HC") + ylab(mygene)
+p
diff --git a/Schrick-Noah_CS-6643_Lab-4.docx b/Schrick-Noah_CS-6643_Lab-4.docx
index 0c5c630..78c7611 100644
Binary files a/Schrick-Noah_CS-6643_Lab-4.docx and b/Schrick-Noah_CS-6643_Lab-4.docx differ