<p><a href="https://www.nature.com/articles/s41586-018-0393-7" rel="nofollow noreferrer">This publication</a> used a negative binomial regression model of the counts of cells at each time-point.</p>

<p>From the methods section: <em>"For each cell type, we model the number of lineage-labelled cells detected in each analysed mouse as a random count variable using a negative binomial distribution. The frequency of detection is modelled by using the natural log of the total number of cells of that type profiled in a given mouse as an offset. The time point of each mouse (0, 30 or 60 days post tamoxifen) is provided as a covariate. The negative binomial model was fit using the R command ‘glm.nb’ from the ‘MASS’ package. The P value for the significance of the change in labelled fraction size between time-points was assessed using a likelihood-ratio test, computing using the R function ‘anova’."</em></p>

<p>The method is available <a href="https://github.com/adamh-broad/single_cell_airway/blob/d7741835738ed58a9f93f2febb4dd302d9a9455b/PulseSeq.md#compute-the-p-values-for-increasing-labeled-fraction-using-negative-binomial-regression" rel="nofollow noreferrer">here</a> as an R script.</p>

<p>The input <code>x</code> is a dataframe with the following columns:</p>

<pre><code>x<span class="math-container">$variable # celltype
x$</span>value # cell count
x<span class="math-container">$batch # condition+donor
x$</span>timepoint # condition (e.g. adult, fetal)
</code></pre>

<p>Montoro, D.T., Haber, A.L., Biton, M. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018) doi:10.1038/s41586-018-0393-7</p>

CC BY-SA 4.0
2019-11-20T18:15:51.140Z
2019-11-20T18:15:51.140Z
Peter
<p>I want to test for significance between differences in cell count between conditions.</p>

<p>I have 2 conditions (N, T), 3 donors (samples) each, and four cell types (A-D) as shown in the example below.</p>

<p>I believe this problem is analogous to the differential expression test performed with edgeR or DEseq.</p>

<p>Example data:</p>

<pre><code>Count   Sample  Celltype    Condition
555 P1  D   T
3829    P1  C   T
945 P1  A   T
222 P1  B   T
795 P2  D   T
5464    P2  C   T
839 P2  A   T
438 P2  B   T
629 P3  D   T
6426    P3  C   T
647 P3  A   T
457 P3  B   T
8   P1  D   N
5122    P1  C   N
3204    P1  A   N
1   P1  B   N
20  P2  D   N
6111    P2  C   N
2524    P2  A   N
26  P2  B   N
66  P3  D   N
7490    P3  C   N
5272    P3  A   N
3   P3  B   N
</code></pre>

<scrnaseq><single-cell>
Calculating celltype proportion changes between conditions in single-cell data
# Calculating celltype proportion changes between conditions in single-cell data

(Asked by: username_0 on 2019-11-20)

I want to test for significance between differences in cell count between conditions.
I have 2 conditions (N, T), 3 donors (samples) each, and four cell types (A-D) as shown in the example below.
I believe this problem is analogous to the differential expression test performed with edgeR or DEseq.
Example data:
Count   Sample  Celltype    Condition
555 P1  D   T
3829    P1  C   T
945 P1  A   T
222 P1  B   T
795 P2  D   T
5464    P2  C   T
839 P2  A   T
438 P2  B   T
629 P3  D   T
6426    P3  C   T
647 P3  A   T
457 P3  B   T
8   P1  D   N
5122    P1  C   N
3204    P1  A   N
1   P1  B   N
20  P2  D   N
6111    P2  C   N
2524    P2  A   N
26  P2  B   N
66  P3  D   N
7490    P3  C   N
5272    P3  A   N
3   P3  B   N

**Tags:** scrnaseq, single-cell

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## 1 answers

### Answer by username_0 on 2019-11-20. Score: 1

This publication used a negative binomial regression model of the counts of cells at each time-point.
From the methods section: "For each cell type, we model the number of lineage-labelled cells detected in each analysed mouse as a random count variable using a negative binomial distribution. The frequency of detection is modelled by using the natural log of the total number of cells of that type profiled in a given mouse as an offset. The time point of each mouse (0, 30 or 60 days post tamoxifen) is provided as a covariate. The negative binomial model was fit using the R command ‘glm.nb’ from the ‘MASS’ package. The P value for the significance of the change in labelled fraction size between time-points was assessed using a likelihood-ratio test, computing using the R function ‘anova’."
The method is available here as an R script.
The input x is a dataframe with the following columns:
x$variable # celltype
x$value # cell count
x$batch # condition+donor
x$timepoint # condition (e.g. adult, fetal)

Montoro, D.T., Haber, A.L., Biton, M. et al. A revised airway epithelial hierarchy includes CFTR-expressing ionocytes. Nature 560, 319–324 (2018) doi:10.1038/s41586-018-0393-7


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