<p>DESeq2 does not require replicates for its noralization. You are probably running the <code>DESeq()</code> function which is a wrapper around normalization <strong>and</strong> testing. Use either the normalized counts, or the output of the <code>vst</code> function which on top of normalization does variance stabilization. Assuming you have a DESeqDataSet:</p>
<pre class="lang-r prettyprint-override"><code>dds &lt;- estimateSizeFactors(dds)

# normalized counts
normCounts &lt;- counts(dds, normalized = TRUE)

# or vst
vsd &lt;- vst(dds)
varianceStabilized &lt;- assay(vsd)
</code></pre>

CC BY-SA 4.0
2024-08-26T10:14:57.440Z
Thank you, your suggestion worked. I'm also considering RPKM normalization. Do you think that would be a sensible approach?
dim
CC BY-SA 4.0
2024-08-25T17:22:02.777Z
2024-08-25T17:22:02.777Z
ATpoint
<p>Following up from <a href="https://bioinformatics.stackexchange.com/questions/22819/how-can-i-normalize-dnaseq-nanopore-metagenomics-data">my previous question</a>, I am currently conducting antimicrobial resistance (AMR) surveillance in water samples using long-read metagenomics (Nanopore sequencing). After analyzing the data with a k-mer read alignment tool, I obtained two sets of AMR gene tables corresponding to two conditions: &quot;before&quot; and &quot;after&quot; treatment.</p>
<p>Each table includes:</p>
<ul>
<li><strong>readCount</strong>: The number of reads mapped to a given gene template.</li>
<li><strong>Template_length</strong>: The length of the gene template sequence.</li>
</ul>
<p>My goal is to normalize these data to compare the AMR gene abundance between the two conditions. However, I'm facing challenges due to the lack of replicates and spike-in controls. Initially, I attempted to use DESeq2 for normalization, but it requires replicates, leading to the following error:</p>
<pre><code>Error in checkForExperimentalReplicates(object, modelMatrix) : 

  The design matrix has the same number of samples and coefficients to fit,
  so estimation of dispersion is not possible. Treating samples
  as replicates was deprecated in v1.20 and no longer supported since v1.22.
</code></pre>
<p>Given these constraints, I’m considering using RPKM (Reads Per Kilobase of transcript per Million mapped reads) for normalization, as suggested by a recent study (see <a href="https://journals.asm.org/doi/10.1128/mra.00644-22" rel="nofollow noreferrer">this paper</a>).</p>
<p>Could anyone provide advice on whether RPKM is a suitable method for normalizing my data under these conditions? Are there alternative approaches I should consider given the absence of replicates and controls?</p>
<p>I want to clarify that my intention is not to spam; I am earnestly seeking a logical solution to my issue. Unfortunately, I lack a support network in my immediate environment to assist with this matter, which is why I am reaching out here. I am close to a conclusion and value your continued support and understanding.</p>
<p>Thank you for your insights!</p>

<deseq2><normalization><long-reads>
Normalizing AMR Gene Read Counts in Metagenomics Data Without Replicates: Is RPKM the Best Approach?
# Normalizing AMR Gene Read Counts in Metagenomics Data Without Replicates: Is RPKM the Best Approach?

(Asked by: username_0 on 2024-08-23)

Following up from my previous question, I am currently conducting antimicrobial resistance (AMR) surveillance in water samples using long-read metagenomics (Nanopore sequencing). After analyzing the data with a k-mer read alignment tool, I obtained two sets of AMR gene tables corresponding to two conditions: "before" and "after" treatment.
Each table includes:

readCount: The number of reads mapped to a given gene template.
Template_length: The length of the gene template sequence.

My goal is to normalize these data to compare the AMR gene abundance between the two conditions. However, I'm facing challenges due to the lack of replicates and spike-in controls. Initially, I attempted to use DESeq2 for normalization, but it requires replicates, leading to the following error:
Error in checkForExperimentalReplicates(object, modelMatrix) : 

  The design matrix has the same number of samples and coefficients to fit,
  so estimation of dispersion is not possible. Treating samples
  as replicates was deprecated in v1.20 and no longer supported since v1.22.

Given these constraints, I’m considering using RPKM (Reads Per Kilobase of transcript per Million mapped reads) for normalization, as suggested by a recent study (see this paper).
Could anyone provide advice on whether RPKM is a suitable method for normalizing my data under these conditions? Are there alternative approaches I should consider given the absence of replicates and controls?
I want to clarify that my intention is not to spam; I am earnestly seeking a logical solution to my issue. Unfortunately, I lack a support network in my immediate environment to assist with this matter, which is why I am reaching out here. I am close to a conclusion and value your continued support and understanding.
Thank you for your insights!

**Tags:** deseq2, normalization, long-reads

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## 1 answers

### Answer by username_1 on 2024-08-25. Score: 1 | Accepted answer

DESeq2 does not require replicates for its noralization. You are probably running the DESeq() function which is a wrapper around normalization and testing. Use either the normalized counts, or the output of the vst function which on top of normalization does variance stabilization. Assuming you have a DESeqDataSet:
dds <- estimateSizeFactors(dds)

# normalized counts
normCounts <- counts(dds, normalized = TRUE)

# or vst
vsd <- vst(dds)
varianceStabilized <- assay(vsd)

> **Comments:**
> **username_0**: Thank you, your suggestion worked. I'm also considering RPKM normalization. Do you think that would be a sensible approach?  

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