text: user3968: I have single cell data which I have analysed for differential expression. In my experiment,  I subjected two groups of mice (a control and a treatment group) to treatment that will lead to the proliferation of my cells of interest in the treated mice. Other scientists have said that the increase of cell numbers caused by this treatment results from increased recruitment from precursor cells from the bone marrow while other scientists argue that the increase in cell number is due to local proliferation.  
  I wish to address this question computationally, based on my single cell rna seq data using R or any other method. I have googled but I can find no suitable tool. Does it suffice to test the expression of known cell migration markers, produce a migration score  and judge based on the migration scores of my proliferating cells?
  If not, I will be very grateful for suggestion of a computational tool, method or package that can help me address this question even indirectly.
  Thank you in advance.
  
  user9039: This may not be a viable answer for all experimental contexts, but you can compare expression profiles of individual cells against datasets that contain known reference cell types (migratory and not). We have recently published a web-based tool called CIPR (cluster identity predictor) that helps with cluster annotations in scRNAseq experiments. There are various reference datasets available, but you can also provide a custom reference by preparing data from databases like GEO.
  You can read the manuscript for more information about how the algorithm works, but in a nutshell, it compares differentially expressed genes or global expression patterns within your data to known reference datasets and creates quick visual outputs. If your reference contains both migratory and non-migratory cell types, maybe CIPR can help differentiate them. For other identity prediction tools, check out this article is a good resource that compares different cluster calling algorithms.
  
original_text: 3968: <p>I have single cell data which I have analysed for differential expression. In my experiment,  I subjected two groups of mice (a control and a treatment group) to treatment that will lead to the proliferation of my cells of interest in the treated mice. Other scientists have said that the increase of cell numbers caused by this treatment results from increased recruitment from precursor cells from the bone marrow while other scientists argue that the increase in cell number is due to local proliferation.  </p>
  
  <p>I wish to address this question computationally, based on my single cell rna seq data using R or any other method. I have googled but I can find no suitable tool. Does it suffice to test the expression of known cell migration markers, produce a migration score  and judge based on the migration scores of my proliferating cells?</p>
  
  <p>If not, I will be very grateful for suggestion of a computational tool, method or package that can help me address this question even indirectly.</p>
  
  <p>Thank you in advance.</p>
   9039: <p>This may not be a viable answer for all experimental contexts, but you can compare expression profiles of individual cells against datasets that contain known reference cell types (migratory and not). We have recently published a web-based tool called <a href="https://aekiz.shinyapps.io/CIPR/" rel="nofollow noreferrer">CIPR</a> (cluster identity predictor) that helps with cluster annotations in scRNAseq experiments. There are various reference datasets available, but you can also provide a custom reference by preparing data from databases like <a href="https://www.ncbi.nlm.nih.gov/geo/browse/" rel="nofollow noreferrer">GEO</a>.</p>
  <p>You can read the <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3538-2" rel="nofollow noreferrer">manuscript</a> for more information about how the algorithm works, but in a nutshell, it compares differentially expressed genes or global expression patterns within your data to known reference datasets and creates quick visual outputs. If your reference contains both migratory and non-migratory cell types, maybe CIPR can help differentiate them. For other identity prediction tools, check out <a href="https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1795-z" rel="nofollow noreferrer">this article</a> is a good resource that compares different cluster calling algorithms.</p>
  