text: user9765: Does single cell data typically have the following meta-data: the "coordinates" (e.g. on a tissue, adjacent tissues) saying where each cell in the sample was located relative to other cells? If not, is it possible to reconstruct this with other meta-data on the cells?
  With the ultimate goal of working hands on with such location-tagged data, I am hoping for suggestions on the correct terms to search for, and even references to previous studies and/or easy to use public datasets.
  For visual reference of the idea I have in mind, consider this picture:
  
  I expect expression of any given gene in the skin cells in Condition 1 to be very different according to the location of the cell. For condition 2, I wouldn't expect the expression of a gene in the skin cells to be overly different, if at all. I want to see if I can formalize this idea using data mining techniques that were created for other purposes, but first I need the proper data.
  edit: Here is an example of the format I eventually hope to work with.
  Fake table 1: Each row represents measurements taken for a unique cell, while the last three columns have coordinates for its location.
  \begin{array}{r|lllllllll}
    \hline
   cell & phenotype1 & phenotype2 & \dots & gene1.expr & gene2.expr & \dots & x.loc.coord & y.loc.coord & z.loc.coord \\ 
    \hline
  1 & 0.69 & 1.34 & \dots & 1.91 & 0.21 & \dots & 1.12 & 0.05 & 1.09 \\    2 & 0.34 & 0.92 & \dots & 1.74 & 2.03 & \dots & 0.57 & 0.46 & 0.24 \\ 
     \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots & \vdots \\ 
    n & 1.97 & 1.3 & 0.96 & 0.19 & 0.66 & \dots & 0.25 & 0.02 & 1.27 \\ 
     \hline
  \end{array}
  Alternatively, if the (x,y,z) coordinates of Fake Table 1 are unavailable or not reconstructable, are there datasets which help us construct an adjacency list of cell pairs according to their location, such as Fake Table 2?
  Fake Table 2: An adjacency table with pairs of cells which were next to each other when the measurement was taken.
  \begin{array}{r|cc}
    \hline
  pair & cell1 & cell2 \\ 
    \hline
  1 & 1 & 2 \\ 
    2 & 1 & 3 \\ 
    3 & 1 & 4 \\ 
    4 & 2 & 3 \\ 
    5 & 2 & 4 \\ 
    \vdots & \vdots & \vdots \\ 
    m-1 & n & n-2 \\ 
    m & n & n-1 \\ 
     \hline
  \end{array}
  I come from a statistics background with a basic understanding of this type of data from talks I've attended, but not hands on experience. I mostly just want to explore this type of data to inform my future work. I am open to any type of scRNA-seq data with cell location "coordinates," if that's the correct thing to ask.
  
  user2388: Is any of this what you want?
  https://support.10xgenomics.com/spatial-gene-expression/datasets
  
  user15353: I would suggest searching with terms such as "spatial transcriptomic" or "spatial proteomics". You could also search for platform-specific data such as "CyCIF", "CODEX", "MIBI", "slideseq", "visium" etc..
  Here is a website that has curated some spatial data (http://www.spatialomics.org/SpatialDB/). We also recently released about 3TB of spatial imaging data that might be helpful. Unlike transcriptomic data that is often submitted in GEO (https://www.ncbi.nlm.nih.gov/geo/) there is still no one place where all of the spatial data is deposited and so you will find them only as supplemental data in published manuscripts. Hope that helps.
  
  user35: 
  Does single cell data typically have the following meta-data: the
  "coordinates" (e.g. on a tissue, adjacent tissues) saying where each
  cell in the sample was located relative to other cells?
  
  No. Typical scRNA-seq is just capturing random cells in a tube with no additional information. The technology you are looking for is spatial transcriptomics where you are measuring RNA levels at a particular location, but that is still not on the single-cell level.
  
  If not, is it possible to reconstruct this with other meta-data on the
  cells?
  
  There have been efforts to reconstruct spatial relationship. For example: Satija et al. 2015. There should be more recent approaches that I am not aware of.
  
original_text: 9765: <p>Does single cell data typically have the following meta-data: the &quot;coordinates&quot; (e.g. on a tissue, adjacent tissues) saying where each cell in the sample was located relative to other cells? If not, is it possible to reconstruct this with other meta-data on the cells?</p>
  <p>With the ultimate goal of working hands on with such location-tagged data, I am hoping for suggestions on the correct terms to search for, and even references to previous studies and/or easy to use public datasets.</p>
  <p>For visual reference of the idea I have in mind, consider this picture:</p>
  <p><a href="https://i.stack.imgur.com/ZxBUJ.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/ZxBUJ.png" alt="enter image description here" /></a></p>
  <p>I expect expression of any given gene in the skin cells in Condition 1 to be very different according to the location of the cell. For condition 2, I wouldn't expect the expression of a gene in the skin cells to be overly different, if at all. I want to see if I can formalize this idea using data mining techniques that were created for other purposes, but first I need the proper data.</p>
  <p><strong>edit</strong>: Here is an example of the format I eventually hope to work with.</p>
  <p><strong>Fake table 1</strong>: Each row represents measurements taken for a unique cell, while the last three columns have coordinates for its location.
  <span class="math-container">\begin{array}{r|lllllllll}
    \hline
   cell &amp; phenotype1 &amp; phenotype2 &amp; \dots &amp; gene1.expr &amp; gene2.expr &amp; \dots &amp; x.loc.coord &amp; y.loc.coord &amp; z.loc.coord \\ 
    \hline
  1 &amp; 0.69 &amp; 1.34 &amp; \dots &amp; 1.91 &amp; 0.21 &amp; \dots &amp; 1.12 &amp; 0.05 &amp; 1.09 \\    2 &amp; 0.34 &amp; 0.92 &amp; \dots &amp; 1.74 &amp; 2.03 &amp; \dots &amp; 0.57 &amp; 0.46 &amp; 0.24 \\ 
     \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots &amp; \vdots \\ 
    n &amp; 1.97 &amp; 1.3 &amp; 0.96 &amp; 0.19 &amp; 0.66 &amp; \dots &amp; 0.25 &amp; 0.02 &amp; 1.27 \\ 
     \hline
  \end{array}</span></p>
  <p>Alternatively, if the (x,y,z) coordinates of <strong>Fake Table 1</strong> are unavailable or not reconstructable, are there datasets which help us construct an adjacency list of cell pairs according to their location, such as <strong>Fake Table 2</strong>?</p>
  <p><strong>Fake Table 2:</strong> An adjacency table with pairs of cells which were next to each other when the measurement was taken.
  <span class="math-container">\begin{array}{r|cc}
    \hline
  pair &amp; cell1 &amp; cell2 \\ 
    \hline
  1 &amp; 1 &amp; 2 \\ 
    2 &amp; 1 &amp; 3 \\ 
    3 &amp; 1 &amp; 4 \\ 
    4 &amp; 2 &amp; 3 \\ 
    5 &amp; 2 &amp; 4 \\ 
    \vdots &amp; \vdots &amp; \vdots \\ 
    m-1 &amp; n &amp; n-2 \\ 
    m &amp; n &amp; n-1 \\ 
     \hline
  \end{array}</span></p>
  <p>I come from a statistics background with a basic understanding of this type of data from talks I've attended, but not hands on experience. I mostly just want to explore this type of data to inform my future work. I am open to any type of scRNA-seq data with cell location &quot;coordinates,&quot; if that's the correct thing to ask.</p>
   2388: <p>Is any of this what you want?</p>
  <p><a href="https://support.10xgenomics.com/spatial-gene-expression/datasets" rel="nofollow noreferrer">https://support.10xgenomics.com/spatial-gene-expression/datasets</a></p>
   15353: <p>I would suggest searching with terms such as &quot;spatial transcriptomic&quot; or &quot;spatial proteomics&quot;. You could also search for platform-specific data such as &quot;CyCIF&quot;, &quot;CODEX&quot;, &quot;MIBI&quot;, &quot;slideseq&quot;, &quot;visium&quot; etc..</p>
  <p>Here is a website that has curated some spatial data (<a href="http://www.spatialomics.org/SpatialDB/" rel="nofollow noreferrer">http://www.spatialomics.org/SpatialDB/</a>). We also recently released about <a href="https://aacrjournals.org/cancerdiscovery/article/doi/10.1158/2159-8290.CD-21-1357/694189/The-spatial-landscape-of-progression-and" rel="nofollow noreferrer">3TB of spatial imaging data</a> that might be helpful. Unlike transcriptomic data that is often submitted in GEO (<a href="https://www.ncbi.nlm.nih.gov/geo/" rel="nofollow noreferrer">https://www.ncbi.nlm.nih.gov/geo/</a>) there is still no one place where all of the spatial data is deposited and so you will find them only as supplemental data in published manuscripts. Hope that helps.</p>
   35: <blockquote>
  <p>Does single cell data typically have the following meta-data: the
  &quot;coordinates&quot; (e.g. on a tissue, adjacent tissues) saying where each
  cell in the sample was located relative to other cells?</p>
  </blockquote>
  <p>No. Typical scRNA-seq is just capturing random cells in a tube with no additional information. The technology you are looking for is spatial transcriptomics where you are measuring RNA levels at a particular location, but that is still not on the single-cell level.</p>
  <blockquote>
  <p>If not, is it possible to reconstruct this with other meta-data on the
  cells?</p>
  </blockquote>
  <p>There have been efforts to reconstruct spatial relationship. For example: <a href="https://doi.org/10.1038/nbt.3192" rel="nofollow noreferrer">Satija et al. 2015</a>. There should be more recent approaches that I am not aware of.</p>
  