<p>TLDR: Harmony can work on 106~ cell samples but has less versatility then methods like BATMAN. BATMAN is useful if you need your data for differential gene expression and single-cell eQTL and can work on ~200 cell data.</p>
<p>Well their certainly is a minimum which comes down to several factors. As many of these rely on algorithms like KNN and other clustering algorithms for integration the threshold is dependent on the quality of the data and the  algorithms abilities to predict reasonable clusters from that data. As well as the downstream uses of the data...</p>
<p><a href="https://www.sciencedirect.com/science/article/pii/S2589004220303709" rel="nofollow noreferrer">BATMAN highest performance on simulated set 200 cells and 1000 cells</a><br />
<strong>With 200 cells from one batch and and 1000 in the other only BATMAN succeeded in clustering the high dimensionality simulated data well. If you need your data for differential gene expression and single-cell eQTL, BATMAN seems to be the correct choice, for small sets.</strong> &quot;The downside of these methods is that they operate in latent space, which limits their interpretability and use in downstream analyses such as differential gene expression and single-cell eQTL analyses.&quot;
&quot;A shows that the two original datasets become better mixed with each other. BATMAN has the highest performance not only when considering the top two principal components but also when considering more top principal components. It is the only method that manages to efficiently maintain a high iLISI score in a larger number of dimensions.&quot;</p>
<p><a href="https://www.researchgate.net/publication/328742713_Fast_sensitive_and_flexible_integration_of_single_cell_data_with_Harmony" rel="nofollow noreferrer">Harmony is best if... differential gene expression and single-cell eQTL is not necessary</a>
<strong>&quot;Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of ~106 cells feasible on a personal computer.&quot;</strong></p>
<p><a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338326/" rel="nofollow noreferrer">Computing time benchmarks-section</a>
It seems as the runtime of these algorithms is less than 2 minutes, using a set with less than ~2000 cells will not benefit your computational time much.
&quot;When processing a small data set of ~ 2000 cells, all four methods took less than 2 min.&quot;
&quot;To obtain such datasets, we downsampled the MCA and TM datasets to obtain a total of 9 sets of data containing between ~ 2000 and ~ 140,000 cells, while the number of highly variable genes (HVGs) was controlled in a range from ~ 2000 to ~ 3000 (Table S1).&quot;</p>

CC BY-SA 4.0
2024-11-11T09:42:07.143Z
Old post, but since I landed here: The harmony paper quotes 10^6 cells not 106. I imagine there was a copy and paste issue here. Since the OP asks for minimum number of cells the current answer is a bit misleading.
dariober
CC BY-SA 4.0
2021-04-12T15:31:45.030Z
2021-04-12T15:31:45.030Z
Michael Hearn
<p>There have been a few methods proposed for integration (or batch correction) of scRNA-seq datasets, such as Seurat CCA, MNN Correct, Scanorama, and Harmony. The concern is generally about the maximum number of cells that they handle, but I haven't seen any discussion about the minimum number of cells. I am confident they can all handle 10k cells reasonably well and will fail with 10 cells, but where do you draw the line? Is there a method that works best for small datasets?</p>

<p>For example, with plate-based platforms like Fluidigm, many experiments only have 96 cells and potentially much less after quality filtering. How can those be used?</p>

CC BY-SA 4.0
2021-04-12T15:32:38.833Z
I hope my answer is sufficient speculation as not many papers have been written specifically on this topic.
Michael Hearn
<scrnaseq>
scRNA-seq multi-dataset integration for small datasets
# scRNA-seq multi-dataset integration for small datasets

(Asked by: username_0 on 2019-03-27)

There have been a few methods proposed for integration (or batch correction) of scRNA-seq datasets, such as Seurat CCA, MNN Correct, Scanorama, and Harmony. The concern is generally about the maximum number of cells that they handle, but I haven't seen any discussion about the minimum number of cells. I am confident they can all handle 10k cells reasonably well and will fail with 10 cells, but where do you draw the line? Is there a method that works best for small datasets?

For example, with plate-based platforms like Fluidigm, many experiments only have 96 cells and potentially much less after quality filtering. How can those be used?

**Tags:** scrnaseq
> **Comments:**
> **username_1**: I hope my answer is sufficient speculation as not many papers have been written specifically on this topic.  

---

## 1 answers

### Answer by username_1 on 2021-04-12. Score: 1

TLDR: Harmony can work on 106~ cell samples but has less versatility then methods like BATMAN. BATMAN is useful if you need your data for differential gene expression and single-cell eQTL and can work on ~200 cell data.

Well their certainly is a minimum which comes down to several factors. As many of these rely on algorithms like KNN and other clustering algorithms for integration the threshold is dependent on the quality of the data and the algorithms abilities to predict reasonable clusters from that data. As well as the downstream uses of the data...

[BATMAN highest performance on simulated set 200 cells and 1000 cells](https://www.sciencedirect.com/science/article/pii/S2589004220303709)  
**With 200 cells from one batch and and 1000 in the other only BATMAN succeeded in clustering the high dimensionality simulated data well. If you need your data for differential gene expression and single-cell eQTL, BATMAN seems to be the correct choice, for small sets.** "The downside of these methods is that they operate in latent space, which limits their interpretability and use in downstream analyses such as differential gene expression and single-cell eQTL analyses."
"A shows that the two original datasets become better mixed with each other. BATMAN has the highest performance not only when considering the top two principal components but also when considering more top principal components. It is the only method that manages to efficiently maintain a high iLISI score in a larger number of dimensions."

[Harmony is best if... differential gene expression and single-cell eQTL is not necessary](https://www.researchgate.net/publication/328742713_Fast_sensitive_and_flexible_integration_of_single_cell_data_with_Harmony)
**"Moreover, we show that Harmony requires dramatically fewer computational resources. It is the only available algorithm that makes the integration of ~106 cells feasible on a personal computer."**

[Computing time benchmarks-section](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7338326/)
It seems as the runtime of these algorithms is less than 2 minutes, using a set with less than ~2000 cells will not benefit your computational time much.
"When processing a small data set of ~ 2000 cells, all four methods took less than 2 min."
"To obtain such datasets, we downsampled the MCA and TM datasets to obtain a total of 9 sets of data containing between ~ 2000 and ~ 140,000 cells, while the number of highly variable genes (HVGs) was controlled in a range from ~ 2000 to ~ 3000 (Table S1)."

> **Comments:**
> **username_2**: Old post, but since I landed here: The harmony paper quotes 10^6 cells not 106. I imagine there was a copy and paste issue here. Since the OP asks for minimum number of cells the current answer is a bit misleading.  

---

