<p>I have two samples A and B and I do have an RNAseq for both of them. Inside each file (for A and B) I do have a set of FPKMs that give me a fold change for each gene (sample vs control). It looks somewhat like that:</p>
<pre><code>gen_name grp01 grp02 grp03 grp04 fold_change
gene1    10    10    5     5     2
gene2    11    11    11    11    1
gene3    5     5     10    10    0.5
gene4    ... 
</code></pre>
<p>for sample A
and, say</p>
<pre><code>gen_name grp05 grp06 grp07 grp08 fold_change
gene1    100   100   5     5     20
gene2    110   110   110   110   1
gene3    5     5     100   100   0.05
gene4    ... 
</code></pre>
<p>for sample B</p>
<p>I need to show with PCA that, foe example, gene1 is sample B is much more overexpressed than in sample A, same for gene3, but it looks like gene2 is not very interesting.</p>
<p>What data must be put in PCA analysis?</p>

CC BY-SA 4.0
2023-03-10T01:36:57.697Z
Using a package is the best approach. Scanpy is popular right now. I do this stuff via first principles, so my advice isn't cool here. Anyway, I'd combine all data into one dataframe, minus log-fold change. I assume log fold change is the opposing sample? The data must have a column to identify A genes from B genes, or denoted within the gene name. PCA does not give gene by gene comparisons, it will be cluster by cluster. Interpreting each clusters is done independently (by mapping the log-fold-change), i.e. what is clustering together and that cluster represents a cohesive expression group.
M__
CC BY-SA 4.0
2023-03-10T01:50:29.630Z
The alternative is DEG analysis, gene by gene - which you've already done.
M__
CC BY-SA 4.0
2023-03-10T03:43:54.700Z
Thank you. I want to show that A is different from B based on an expression of gene1 and gene3 (in this test experiment). I want to do this in R but scanpy is a python package. Is there any way to use ```prcomp()``` ?
Lara
<r><differential-expression><pca>
What is the best approach to do a PCA analysis to distinguish between two groups?
# What is the best approach to do a PCA analysis to distinguish between two groups?

(Asked by: username_0 on 2023-03-10)

I have two samples A and B and I do have an RNAseq for both of them. Inside each file (for A and B) I do have a set of FPKMs that give me a fold change for each gene (sample vs control). It looks somewhat like that:
gen_name grp01 grp02 grp03 grp04 fold_change
gene1    10    10    5     5     2
gene2    11    11    11    11    1
gene3    5     5     10    10    0.5
gene4    ... 

for sample A
and, say
gen_name grp05 grp06 grp07 grp08 fold_change
gene1    100   100   5     5     20
gene2    110   110   110   110   1
gene3    5     5     100   100   0.05
gene4    ... 

for sample B
I need to show with PCA that, foe example, gene1 is sample B is much more overexpressed than in sample A, same for gene3, but it looks like gene2 is not very interesting.
What data must be put in PCA analysis?

**Tags:** r, differential-expression, pca
> **Comments:**
> **username_1**: Using a package is the best approach. Scanpy is popular right now. I do this stuff via first principles, so my advice isn't cool here. Anyway, I'd combine all data into one dataframe, minus log-fold change. I assume log fold change is the opposing sample? The data must have a column to identify A genes from B genes, or denoted within the gene name. PCA does not give gene by gene comparisons, it will be cluster by cluster. Interpreting each clusters is done independently (by mapping the log-fold-change), i.e. what is clustering together and that cluster represents a cohesive expression group.  
> **username_1**: The alternative is DEG analysis, gene by gene - which you've already done.  
> **username_0**: Thank you. I want to show that A is different from B based on an expression of gene1 and gene3 (in this test experiment). I want to do this in R but scanpy is a python package. Is there any way to use ```prcomp()``` ?  

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## 0 answers

