<p>If the problem concerns spatial clustering of cells then the ML algorithm is without question <a href="https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html" rel="nofollow noreferrer">DBSCAN</a>.</p>
<p>The problem with deep learning and CNN (convolution) not &quot;deconvolution&quot;, i.e. the MBE paper cited, is:</p>
<ul>
<li>The model is an <em>ad hoc</em> construction, which can be also a strength;</li>
<li>Under-training, is resolved by adding more layers;</li>
<li>Overtraining, can only be assessed post-analysis, in ML its algorithm specific;</li>
<li>CNN approaches require large datasets for training of 2 million+.</li>
</ul>
<p>The issue with ML is its performance tapers at around the 1 or 2 million data set size mark, whereas deep learning <em>can</em> (stress <em>can</em>) continue to use massive data sets to enhance performance (thats its strength).</p>
<p>Whether, DBSCAN has received publications in bioinformatics - I'm not sure. However, what is present in the public domain isn't always the cutting edge work: a lesson I've very recently learnt. DBSCAN is very fashionable for spatial problems at present and in context personally would rank it above CNN.</p>
<p><strong>CNN vs ML</strong> The thing is most of us - unless you're working for Google - don't have access to data sets in 2 million+ range. This can be circumvented by <em>transfer learning</em> but it is not sufficiently developed outside Google and a few major players in computer vision. Has Google performed CNN on one million+ cell images - I doubt it. If the problem is ecological species identification via remote cameras - Googles CNN image libraries would be just the ticket (lots of dogs, cats and bear photos) but for microscopy studies I seriously doubt it.</p>
<p><strong>Conclusion</strong>
What I am 100% certain on is DBSCAN is VERY fashionable right now and certain ML algorithms repeatedly outperform deep learning in certain situations. What I am trying to say is ML is not necessarily just a low data size version of deep learning: in some instances it surpasses it and I think DBSCAN has that potential. The broader challenges of deep learning hinge around 'transfer learning'. If biology manages to resolve that <em>then</em> deep learning will start surpassing machine learning, but firstly it's about sharing, secondly in e.g. RNN this is not technically trivial.</p>
<hr />
<p>What you can do is pose a detailed problem in a later question and I could explain how training would be used ... generally in complex analytical problems it is difficult to assess precisely how a solution would be implemented.</p>

CC BY-SA 4.0
2023-01-13T12:43:00.667Z
2023-01-13T15:29:12.267Z
2023-01-13T15:29:12.267Z
M__
<p>I am currently looking into some of the published methods for deconvolution of spatial transcriptomics data where each spot does not have single-cell resolution. These methods all rely on cell type annotated scRNA-seq data to build a reference of expected expression for each cell population. So in essence, because ground truth is more or less impossible to obtain for complete cell typing of a spatially intact tissue section, another type of data (scRNA) is used instead. This lets you infer something about the spatial organization of the tissue despite no spatial information being present in the training data, but in the end no ground truth is available for validating the results.</p>
<p>I was trying to think of an analogous machine learning problem, biological or not, where something similar is done to deal with unattainable ground truch. Are there any obvious/good examples of something like this? Maybe some inspiration can be drawn for how such results are validated.</p>

CC BY-SA 4.0
2023-01-13T12:23:49.983Z
Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.
Community
CC BY-SA 4.0
2023-01-13T21:20:11.347Z
Hi, discussion and opinion-based questions are too generic to usefully fit the Stack Exchange format. Please adjust your question to relate to a specific issue that you're having. If you want to ask general questions for discussion, it would be better to do that on [reddit](https://reddit.com/r/bioinformatics)
gringer
<rna-seq><scrnaseq>
Examples of machine learning approaches to validate results where ground truth is lacking?
# Examples of machine learning approaches to validate results where ground truth is lacking?

(Asked by: username_0 on 2023-01-13)

I am currently looking into some of the published methods for deconvolution of spatial transcriptomics data where each spot does not have single-cell resolution. These methods all rely on cell type annotated scRNA-seq data to build a reference of expected expression for each cell population. So in essence, because ground truth is more or less impossible to obtain for complete cell typing of a spatially intact tissue section, another type of data (scRNA) is used instead. This lets you infer something about the spatial organization of the tissue despite no spatial information being present in the training data, but in the end no ground truth is available for validating the results.

I was trying to think of an analogous machine learning problem, biological or not, where something similar is done to deal with unattainable ground truch. Are there any obvious/good examples of something like this? Maybe some inspiration can be drawn for how such results are validated.

**Tags:** rna-seq, scrnaseq
> **Comments:**
> **username_1**: Please clarify your specific problem or provide additional details to highlight exactly what you need. As it's currently written, it's hard to tell exactly what you're asking.  
> **username_2**: Hi, discussion and opinion-based questions are too generic to usefully fit the Stack Exchange format. Please adjust your question to relate to a specific issue that you're having. If you want to ask general questions for discussion, it would be better to do that on [reddit](https://reddit.com/r/bioinformatics)  

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## 1 answers

### Answer by username_3 on 2023-01-13. Score: 1

If the problem concerns spatial clustering of cells then the ML algorithm is without question [DBSCAN](https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html).

The problem with deep learning and CNN (convolution) not "deconvolution", i.e. the MBE paper cited, is:

* The model is an *ad hoc* construction, which can be also a strength;
* Under-training, is resolved by adding more layers;
* Overtraining, can only be assessed post-analysis, in ML its algorithm specific;
* CNN approaches require large datasets for training of 2 million+.

The issue with ML is its performance tapers at around the 1 or 2 million data set size mark, whereas deep learning *can* (stress *can*) continue to use massive data sets to enhance performance (thats its strength).

Whether, DBSCAN has received publications in bioinformatics - I'm not sure. However, what is present in the public domain isn't always the cutting edge work: a lesson I've very recently learnt. DBSCAN is very fashionable for spatial problems at present and in context personally would rank it above CNN.

**CNN vs ML** The thing is most of us - unless you're working for Google - don't have access to data sets in 2 million+ range. This can be circumvented by *transfer learning* but it is not sufficiently developed outside Google and a few major players in computer vision. Has Google performed CNN on one million+ cell images - I doubt it. If the problem is ecological species identification via remote cameras - Googles CNN image libraries would be just the ticket (lots of dogs, cats and bear photos) but for microscopy studies I seriously doubt it.

**Conclusion**
What I am 100% certain on is DBSCAN is VERY fashionable right now and certain ML algorithms repeatedly outperform deep learning in certain situations. What I am trying to say is ML is not necessarily just a low data size version of deep learning: in some instances it surpasses it and I think DBSCAN has that potential. The broader challenges of deep learning hinge around 'transfer learning'. If biology manages to resolve that *then* deep learning will start surpassing machine learning, but firstly it's about sharing, secondly in e.g. RNN this is not technically trivial.

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What you can do is pose a detailed problem in a later question and I could explain how training would be used ... generally in complex analytical problems it is difficult to assess precisely how a solution would be implemented.


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