<p>I need to perform differential expression analysis using the <a href="https://rdrr.io/bioc/scDD/f/inst/doc/scDD.pdf" rel="nofollow noreferrer">scDD package</a> from R, but I am not able to since I miss the <strong>normcounts</strong> assay in my SCE object (of course in the example they show, the assay is already there).</p>
<p>As I found out in the preprocessing steps, one can use <a href="https://rdrr.io/bioc/scDD/man/preprocess.html" rel="nofollow noreferrer">this function</a> to generate the missing assay, except that it does not work:</p>
<pre><code># this is the SCE object
&gt; cTSubSet
class: SingleCellExperiment 
dim: 19767 149 
metadata(2): Samples Samples
assays(2): counts logcounts
rownames(19767): Xkr4 Gm19938 ... CAAA01147332.1 AC149090.1
rowData names(4): ID Symbol Type chrLoc
colnames(149): AAAGGATGTTCACGAT-1 AACCATGTCTTTCGAT-1 ...
  TGTGGCGTCTCTTAAC-1 TTTGGTTAGACGCTCC-1
colData names(17): Sample Barcode ... slm_1 slm_1.5
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

# setting parameters as suggested in the vignette linked above
&gt; paR &lt;- list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01)
# setting zero.thresh to zero since I already removed unwanted genes
&gt; cTSubSet &lt;- preprocess(cTSubSet, zero.thresh=0, scran_norm=TRUE)

# the not so useful error:
Performing scran Normalization
Error in t.default(Data) : argument is not a matrix
</code></pre>
<p>I was also looking for other possible ways of obtaining such <strong>normcounts</strong> assay and I found <a href="https://bioconductor.org/packages/devel/bioc/vignettes/SingleCellExperiment/inst/doc/intro.html" rel="nofollow noreferrer">this description</a>:</p>
<blockquote>
<p>normcounts: Normalized values on the same scale as the original
counts. For example, counts divided by cell-specific size factors that
are centred at unity.</p>
</blockquote>
<p>Still, I can't figure out what this may mean. Is there a way of obtaining such <strong>normcounts</strong>?</p>

CC BY-SA 4.0
2021-06-17T10:31:48.990Z
You might as well try [monocle3::normalized_count](https://rdrr.io/github/cole-trapnell-lab/monocle3/man/normalized_counts.html).
It takes SingleCellExperiment object.
littleworth
CC BY-SA 4.0
2021-06-18T12:01:01.447Z
yep that is a possibility. I actually found another one using edgeR, i.e.
`y <- DGEList(counts=(counts(cTSubSet)), group=cTSubSet$condition)`
`y <- calcNormFactors(y)`
however, I am still working on this
gbt
<r><rna-seq><scrnaseq>
how to get normcounts for singlecellexperiment object?
# how to get normcounts for singlecellexperiment object?

(Asked by: username_0 on 2021-06-17)

I need to perform differential expression analysis using the scDD package from R, but I am not able to since I miss the normcounts assay in my SCE object (of course in the example they show, the assay is already there).
As I found out in the preprocessing steps, one can use this function to generate the missing assay, except that it does not work:
# this is the SCE object
> cTSubSet
class: SingleCellExperiment 
dim: 19767 149 
metadata(2): Samples Samples
assays(2): counts logcounts
rownames(19767): Xkr4 Gm19938 ... CAAA01147332.1 AC149090.1
rowData names(4): ID Symbol Type chrLoc
colnames(149): AAAGGATGTTCACGAT-1 AACCATGTCTTTCGAT-1 ...
  TGTGGCGTCTCTTAAC-1 TTTGGTTAGACGCTCC-1
colData names(17): Sample Barcode ... slm_1 slm_1.5
reducedDimNames(0):
mainExpName: NULL
altExpNames(0):

# setting parameters as suggested in the vignette linked above
> paR <- list(alpha=0.01, mu0=0, s0=0.01, a0=0.01, b0=0.01)
# setting zero.thresh to zero since I already removed unwanted genes
> cTSubSet <- preprocess(cTSubSet, zero.thresh=0, scran_norm=TRUE)

# the not so useful error:
Performing scran Normalization
Error in t.default(Data) : argument is not a matrix

I was also looking for other possible ways of obtaining such normcounts assay and I found this description:

normcounts: Normalized values on the same scale as the original
counts. For example, counts divided by cell-specific size factors that
are centred at unity.

Still, I can't figure out what this may mean. Is there a way of obtaining such normcounts?

**Tags:** r, rna-seq, scrnaseq
> **Comments:**
> **username_1**: You might as well try [monocle3::normalized_count](https://rdrr.io/github/cole-trapnell-lab/monocle3/man/normalized_counts.html). It takes SingleCellExperiment object.  
> **username_0**: yep that is a possibility. I actually found another one using edgeR, i.e. `y <- DGEList(counts=(counts(cTSubSet)), group=cTSubSet$condition)` `y <- calcNormFactors(y)` however, I am still working on this  

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## 0 answers

