text: user6498: I'm working with some large Seurat objects (MOCA, MCA, Tabula Muris) studying gene coexpression, and I'm running into memory issues. 
  Is it possible to remove all genes in a Seurat object that are not in a specified gene array? Ideally I'd like to be working with a small R matrix with say, 250k rows (cells) and 10-50 columns (gene expression data) which will enable fast processing and comprehensive analysis using other packages.
  
  user252: Is your memory usage low enough that you can initially read everything in and then filter later? If so you could read the Seurat object in like normal and then use subset against a vector of your gene names.
  subsample <- subset(seurat_obj, features = my_genes)
  
  
  user3631: A different approach if you are using Seurat3, is DietSeurat(). It allows you to diet the object by removing the components that you don't need. For example you can keep the normalised/scaled matrix and remove the raw counts. This approach could reduce space and memory usage, while keeping all your genes in place.
  
original_text: 6498: <p>I'm working with some large Seurat objects (MOCA, MCA, Tabula Muris) studying gene coexpression, and I'm running into memory issues. </p>
  
  <p>Is it possible to remove all genes in a Seurat object that are not in a specified gene array? Ideally I'd like to be working with a small R matrix with say, 250k rows (cells) and 10-50 columns (gene expression data) which will enable fast processing and comprehensive analysis using other packages.</p>
   252: <p>Is your memory usage low enough that you can initially read everything in and then filter later? If so you could read the Seurat object in like normal and then use <code>subset</code> against a vector of your gene names.</p>
  
  <pre><code>subsample &lt;- subset(seurat_obj, features = my_genes)
  </code></pre>
   3631: <p>A different approach if you are using <code>Seurat3</code>, is <code>DietSeurat()</code>. It allows you to diet the object by removing the components that you don't need. For example you can keep the normalised/scaled matrix and remove the raw counts. This approach could reduce space and memory usage, while keeping all your genes in place.</p>
  