<p><em>This question was also asked on <a href="https://www.biostars.org/p/9549650/" rel="nofollow noreferrer">Biostars</a> and <a href="https://support.bioconductor.org/p/9148604/" rel="nofollow noreferrer">Bioconductor Support</a></em></p>
<p>My aim is to perform gene clustering based on RNA seq datasets (raw expression values) from NCBI database. After clustering I realized that it is hard for me to get the biological understanding of the clusters. So, I thought it will be better if I start with data such as expression change in different conditions with respect to control (log fold change) for my later interpretation.</p>
<p>I need to calculate log2 fold change values for lot of different experimental conditions when compared to their corresponding controls. Just to mention, I am not going to use these for differential expression analysis but for some other downstream analysis like clustering and stuff. Traditionally in my field, counts are normalized by TPM method and then fold change values are calculated by log2(TPM_exp+1)-log2(TPM_control+1) [using 1 or 0.5 as pseudo counts for log transformation]. In my case, I realized that TPM is not a good way to normalize the data as I have few samples with lot of reads mapping to only one or two genes [RNA composition bias]. DESeq2 median of ratios nomalization seems to take care of that issue. So, I prefer using DESeq2 normalization. But I cannot use DESeq2 for getting log2 fold change values because I don't have replicates for some of the experimental conditions and DESeq2 needs replicates to estimate log2 fold change values. So, I want to manually calculate log2 fold change values from DESeq2 normalized counts.</p>
<p>So, I am using log2(DESeq2norm_exp+0.5)-log2(DESeq2norm_control+0.5) for calculating log2 fold change values. I am not sure whether it is a good idea or the choice of pseudo-count here is very critical. Any comments or help is really appreciated.</p>
<p><em>[This question was originally posted on StackOverflow, and someone commented suggesting other communities that would be more appropriate, in particular, here and Bioconductor Support]</em></p>

CC BY-SA 4.0
2022-12-31T01:01:52.893Z
Can you please provide more information/detail about your experiment? In particular, how many experimental conditions there are, whether there are any interactions (e.g. condition x genetic variant), and the number of replicates per condition. Depending on the experimental design, it might be possible to use other conditions as replicates to help with normalisation and/or L2FC shrinkage.
gringer
CC BY-SA 4.0
2022-12-31T01:04:14.100Z
A semantic comment: if you're calculating log2 fold change (i.e. the log of the fold change between two conditions), you're doing a differential expression analysis. It's possible that what you actually want are log-normalised expression values, but there's not enough detail to determine that.
gringer
CC BY-SA 4.0
2023-01-01T07:37:28.973Z
Thanks, that's a little bit more information, but what's most important in this case is your experimental design. DESeq2 doesn't need replicates for every condition, but it needs enough biological variation to gat a reasonable idea of variability.
gringer
<deseq2><differential-expression><fold-change>
Manually calculating log2 fold change values from DESeq2 normalized counts
# Manually calculating log2 fold change values from DESeq2 normalized counts

(Asked by: username_0 on 2022-12-29)

*This question was also asked on [Biostars](https://www.biostars.org/p/9549650/) and [Bioconductor Support](https://support.bioconductor.org/p/9148604/)*

My aim is to perform gene clustering based on RNA seq datasets (raw expression values) from NCBI database. After clustering I realized that it is hard for me to get the biological understanding of the clusters. So, I thought it will be better if I start with data such as expression change in different conditions with respect to control (log fold change) for my later interpretation.

I need to calculate log2 fold change values for lot of different experimental conditions when compared to their corresponding controls. Just to mention, I am not going to use these for differential expression analysis but for some other downstream analysis like clustering and stuff. Traditionally in my field, counts are normalized by TPM method and then fold change values are calculated by log2(TPM\_exp+1)-log2(TPM\_control+1) [using 1 or 0.5 as pseudo counts for log transformation]. In my case, I realized that TPM is not a good way to normalize the data as I have few samples with lot of reads mapping to only one or two genes [RNA composition bias]. DESeq2 median of ratios nomalization seems to take care of that issue. So, I prefer using DESeq2 normalization. But I cannot use DESeq2 for getting log2 fold change values because I don't have replicates for some of the experimental conditions and DESeq2 needs replicates to estimate log2 fold change values. So, I want to manually calculate log2 fold change values from DESeq2 normalized counts.

So, I am using log2(DESeq2norm\_exp+0.5)-log2(DESeq2norm\_control+0.5) for calculating log2 fold change values. I am not sure whether it is a good idea or the choice of pseudo-count here is very critical. Any comments or help is really appreciated.

*[This question was originally posted on StackOverflow, and someone commented suggesting other communities that would be more appropriate, in particular, here and Bioconductor Support]*

**Tags:** deseq2, differential-expression, fold-change
> **Comments:**
> **username_1**: Can you please provide more information/detail about your experiment? In particular, how many experimental conditions there are, whether there are any interactions (e.g. condition x genetic variant), and the number of replicates per condition. Depending on the experimental design, it might be possible to use other conditions as replicates to help with normalisation and/or L2FC shrinkage.  
> **username_1**: A semantic comment: if you're calculating log2 fold change (i.e. the log of the fold change between two conditions), you're doing a differential expression analysis. It's possible that what you actually want are log-normalised expression values, but there's not enough detail to determine that.  
> **username_1**: Thanks, that's a little bit more information, but what's most important in this case is your experimental design. DESeq2 doesn't need replicates for every condition, but it needs enough biological variation to gat a reasonable idea of variability.  

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## 0 answers

