Body: <p>I have expression matrices for different cell types, representing the expression of individual cells of that type. They were learned through a generative model, so I am confident they represent the approximate expression patterns of specific cell types. Now, I want to implement a bulk sequencing deconvolution using the aforementioned expression patterns. The pseudo-bulk I used is the summation of a large number of single-cell sequencing data (sc-seq). That's the background.</p>
  <p>My first approach is to design an optimization process to optimize a series of weights, so that the product of the weights and the cell type expression approximates the pseudo-bulk. I was advised to use Poisson loss as the loss function because it aligns with the biological characteristics of RNA-seq. However, I couldn't add non-negativity constraints to the weights during optimization, resulting in negative values in the optimization results, which is meaningless. Then I found the optimiza.nnls method in the scipy package, which implements non-negativity constraints, but it uses Euclidean distance to compare the pseudo-bulk and the sc-seq combination. I obtained some good results using this method, but I have the following questions.</p>
  <ol>
  <li>Can I use Euclidean distance to compare the differences between two sequencing methods? To me, this problem seems to become a linear regression problem, i.e., combining sc-seq to approximate pseudo-bulk. At this stage, there don't seem to be any biological distribution assumptions, so I guess it's feasible.</li>
  <li>If the answer to the previous question is no, what biological assumptions does using Poisson loss follow, and what am I ignoring when using Euclidean distance for comparison?</li>
  <li>If I want to continue using Poisson loss to optimize weights, how should I set the non-negativity constraints on the weights? I have tried methods such as ReLU and softmax in machine learning, but the results are not good.</li>
  </ol>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-18T15:35:43.457Z
Comments.Text: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.
Comments.UserDisplayName: Community
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2024-03-27T22:49:16.460Z
Comments.Text: I do not know the specific answers to your questions but I know there are several published papers out there about single cell deconvolution that might help.
Comments.UserDisplayName: Sarah
Tags: <scrnaseq><machine-learning><deconvolution>
Title: Comparing sc-seq combination and bulk-sequencing
ThreadText: # Comparing sc-seq combination and bulk-sequencing
  
  (Asked by: username_0 on 2024-03-17)
  
  I have expression matrices for different cell types, representing the expression of individual cells of that type. They were learned through a generative model, so I am confident they represent the approximate expression patterns of specific cell types. Now, I want to implement a bulk sequencing deconvolution using the aforementioned expression patterns. The pseudo-bulk I used is the summation of a large number of single-cell sequencing data (sc-seq). That's the background.
  
  My first approach is to design an optimization process to optimize a series of weights, so that the product of the weights and the cell type expression approximates the pseudo-bulk. I was advised to use Poisson loss as the loss function because it aligns with the biological characteristics of RNA-seq. However, I couldn't add non-negativity constraints to the weights during optimization, resulting in negative values in the optimization results, which is meaningless. Then I found the optimiza.nnls method in the scipy package, which implements non-negativity constraints, but it uses Euclidean distance to compare the pseudo-bulk and the sc-seq combination. I obtained some good results using this method, but I have the following questions.
  
  1. Can I use Euclidean distance to compare the differences between two sequencing methods? To me, this problem seems to become a linear regression problem, i.e., combining sc-seq to approximate pseudo-bulk. At this stage, there don't seem to be any biological distribution assumptions, so I guess it's feasible.
  2. If the answer to the previous question is no, what biological assumptions does using Poisson loss follow, and what am I ignoring when using Euclidean distance for comparison?
  3. If I want to continue using Poisson loss to optimize weights, how should I set the non-negativity constraints on the weights? I have tried methods such as ReLU and softmax in machine learning, but the results are not good.
  
  **Tags:** scrnaseq, machine-learning, deconvolution
  
  > **Comments:**
  > **username_1**: Please edit the question to limit it to a specific problem with enough detail to identify an adequate answer.  
  > **username_2**: I do not know the specific answers to your questions but I know there are several published papers out there about single cell deconvolution that might help.  
  
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  ## 0 answers
  
  