# Interpreting this PCA plot for RNA-seq

I have RNA-seq from two sequencing batches; Lab technician says that he has run the RNA expression quantification two times in bathes 1 and 2 for example tumor 1 in batch 1 and tumor 1 in batch 2 , normal 2 in batch1 and normal 2 in batch 2. This is my design for DESeq2

> head(mycols)
condition batch
N_1_305         N     1
N_1_310         N     1
N_1_337         N     1
N_1_353         N     1
T_1_305         T     1
T_1_310         T     1
> tail(mycols)
condition batch
T_2_337         T     2
T_2_338         T     2
T_2_344         T     2
T_2_346         T     2
T_2_349         T     2
T_2_353         T     2
>


I got this PCA plot

And this is biplot of samples

In PCA plot I am seeing for instance , T_1_337 (batch1) has been placed too close to T_2_337 (batch2)

Then I used svd for detecting hidden batch

Does this mean that there is no big batch effect between experimental runs and I can concatenate the fastqs from both batches for each sample (technical replication) or collapse technical replicates afterwards?

Please help me to interpret these Thank you

EDITED

Sorry @Devon, I have 4 lanes for each samples (paired end) in each of experimental runs; For concatenating fastq files can I do like this ?

cat fastq1_lane1_batch1 fastq1_lane1_batch2 fastq1_lane2_batch1   fastq1_lane2_batch2  fastq1_lane3_batch1 fastq1_lane3_batch2 fastq1_lane4_batch1 fastq1_lane4_batch2  > fastq1

cat fastq2_lane1_batch1 fastq2_lane1_batch2 fastq2_lane2_batch1   fastq2_lane2_batch2  fastq2_lane3_batch1 fastq2_lane3_batch2 fastq2_lane4_batch1 fastq2_lane4_batch2 > fastq2

• Why do you want to collapse replicates? It's really good that you have replicates. Treat them as such. Sep 15 '19 at 20:34
• She's talking about technical replicates. Sep 15 '19 at 21:36

## 2 Answers

Yes, you can safely concatenate the technical replicates. Odds are good that these are even the same libraries just sequenced twice, so even labeling them as replicates is a bit of a stretch. As an aside, it would be surprising if you actually had a batch effect in a situation like this. You will commonly see sequencing facilities just sequence a given sample a second time if it didn't get the number of reads requested by the client to begin with. They then just have the client concatenate the files.

• Sorry @Devon, I have 4 lanes for each samples (paired end) in each of experimental runs; For concatenating fastq files can I do like this ? cat fastq1_lane1_batch1 fastq1_lane1_batch2 fastq1_lane2_batch1 fastq1_lane2_batch2 fastq1_lane3_batch1 fastq1_lane3_batch2 fastq1_lane4_batch1 fastq1_lane4_batch2 fastq2_lane1_batch1 fastq2_lane1_batch2 fastq2_lane2_batch1 fastq2_lane2_batch2 fastq2_lane3_batch1 fastq2_lane3_batch2 fastq2_lane4_batch1 fastq2_lane4_batch2 Sep 15 '19 at 23:40
• @Fereshteh That's a different question, and one you can find answers to by searching this site or biostars. Please do not stretch people offering to help by piling on more questions. Sep 15 '19 at 23:50
• Sorry @Devon I think this question relates to this post; Can I use technical replicates for gene differential expression analysis? For example I am only interested in tumour A and normal A , can I compare them by using batch1 and batch2 as my replications? Sep 16 '19 at 16:35
• I think you know the answer to that is "no". Sep 16 '19 at 18:15

Prepping the RNA on different days, or making Illumina libraries on different days, or having different technicians handle different samples; that can lead to batch effects. Running samples on two different days does not cause a significant batch effect, as you can plainly see in your PCA. You should just combine the fastqs.

• Thank you @swbarnes2, now my question is can I compare only one tumour with its own matched normal sample to get differentially expressed genes for this pair by using experimental runs (technical replicates )? Design would be Tumour1_batch1 Tumour1_batch2 versus Normal1_batch1 Normal1_bacth2 Sep 16 '19 at 16:48
• @Fereshteh As you're well aware, new questions should be posted as such. Sep 16 '19 at 18:14