**10**wrote:

Hello, I have the following question: I have an RNAseq dataset (DESeq dataset) that I want to do PCA analysis on. This is how I made the dataset:

```
dds_norm <- estimateSizeFactors(dds)
dds_norm_vst <- vst(dds_norm)
counts_norm_vst <- assay(dds_norm_vst, normalized = TRUE)
```

I made a PCA bi-plot, screeplot and pairsplot with the package PCAtools, with the following code:

```
dds_vst_pca_.1 <- pca(counts_norm_vst, metadata = xp_design_pca, removeVar = .1)
#make scree plot (90% top genes)
scree_all_.1 <- data.frame(percentVar_all_.1)
scree_all_.1[,2] <- c(1:46)
colnames(scree_all_.1) <- c("variance","component_nr")
ggplot(scree_all_.1, mapping=aes(x=component_nr, y=variance)) +
geom_bar(stat="identity") +
ggtitle("screeplot for all timepoints")
#make bi plot (90% top) with PCAtools
biplot(dds_vst_pca_.1,
colby = "time",
shape = "treatment",
pointSize = 5,
legendPosition = 'right')
#find PC's to keep
elbow <- findElbowPoint(dds_vst_pca_.1$variance)
#make pairs plot (90% top)
pairsplot(dds_vst_pca_.1,
components = getComponents(dds_vst_pca_.1, c(1:10)),
colby = "treatment",
colkey = c("e" = "gray",
"m" = "darkgreen",
"n" = "lightgreen"))
```

Then, I tried to check myself and made another biplot with prcomp() according to the following code:

```
dds_vst_prc <- prcomp(counts_norm_vst, scale = TRUE)
ntop_.1 <- nrow(counts)*.9
ntop_.1 <- as.integer(ntop_.1)
#do prcomp with lower 10% of variable genes removed
select_.1 <- order(rv, decreasing = TRUE)[seq_len(min(ntop_.1, length(rv)))]
mat_.1 <- t( assay(dds_norm_vst)[select_.1, ] )
dds_norm_vst_prc_.1 <-prcomp(mat_.1)
dds_norm_vst_prc_.1_df <- as.data.frame(dds_norm_vst_prc_.1$x)
percentVar_all_.1 <- dds_norm_vst_prc_.1$sdev^2 / sum(dds_norm_vst_prc_.1$sdev^2)
ggplot(dds_norm_vst_prc_.1_df, aes(PC1, PC2, color = time, shape = treatment)) +
geom_point(size = 4) +
xlab(paste0("PC1: ",percentVar_all_.1[1]*100,"% variance")) +
ylab(paste0("PC2: ",percentVar_all_.1[2]*100,"% variance")) +
coord_fixed() +
ggtitle("PCA plot")
```

Indeed, these two give exactly the same biplot (same percentages of PC's and same position of the samples). However, then I looked into scaling, and I see that although for the prcomp() I put scaling to TRUE, for the pcatools method i did not specify, assuming scaling default would be TRUE. However, I found out the default is FALSE! Indeed, when I put scaling to TRUE, I get a different outcome. How is this possible? What am I overlooking? Thanks very much for any input!