Thank you for your response, and sorry for being late as we had vacations here.

I am working with TCGA-LUAD data set, and the samples are processed/normalized using DESeq2. The steps are given below:

ld_dds <- DESeqDataSetFromMatrix(countData = ld_dataPrep2,
                              colData = ld_sampleTypes,
                              design = ~sType)
ld_dds <- DESeq(ld_dds)

# extract normalized count for Clustering and Immune infiltration analysis
ld_normCount <- counts(ld_dds, normalized = TRUE)

For PCA:

ld_normCount <- ld_normCount[rowSums(ld_normCount) > 100 , ]
pca.obj = prcomp(t(ld_normCount),
                 scale. = TRUE)

pcr.objx <- as.data.frame(pca.obj$x)

dtp <- data.frame('titles' = rownames(pcr.objx),
                  pcr.objx[, c(1:3)]) # the first three components are selected

dtp2 = merge(dtp, ld_sampleTypes, by.x = "titles", by.y = 0)

#print(head(dtp2))
ggplot(data = dtp2) +
    geom_point(aes(x = PC1, y = PC2, col = sType)) + # type needs to added
    theme_minimal() +
    labs(title = "LUAD PCA")

The samples >200 along PC1 are considered as outliers (as suggested by literature).

The PCA figure is attached. NT are normals, while TP are tumor samples.