text: user208: I'm looking for single-cell specific methods to construct (using gene expression data) new features that express pathway "level" or "activity", and then use these for clustering cells.
  One example for bulk RNA-seq is PLAGE, implemented in the GSVA R package.
  I have found only one method for single-cell RNA-seq that comes close: pagoda in the scde R package. This performs PCAs for each pathway, using its genes.
  A very similar question is Classification (supervised learning) of expression data on pathway level, on bulk/microarray data.
  
  user208: The AUCell R package identifies enriched gene sets within cells. It uses the Area Under the Curve (AUC) to calculate whether a gene set is enriched within the expressed genes for each cell.
  
  user3723: Package Hipathia allows you to compute a value of "activity" for each pathway and each cell, so that the matrix of gene expression is transformed into a matrix of pathway activity. You can use this matrix for further analysis, cell differentiation and clustering. You can also compute functional activity matrices in the same way. 
  
original_text: 208: <p>I'm looking for single-cell specific methods to construct (using gene expression data) new features that express pathway "level" or "activity", and then use these for clustering cells.</p>
  
  <p>One example for bulk RNA-seq is <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-6-225" rel="noreferrer">PLAGE</a>, implemented in the <a href="https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7" rel="noreferrer">GSVA</a> R package.</p>
  
  <p>I have found only one method for single-cell RNA-seq that comes close: <a href="http://hms-dbmi.github.io/scde/pagoda.html" rel="noreferrer">pagoda in the scde R package</a>. This performs PCAs for each pathway, using its genes.</p>
  
  <p>A very similar question is <a href="https://bioinformatics.stackexchange.com/questions/3645/classification-supervised-learning-of-expression-data-on-pathway-level">Classification (supervised learning) of expression data on pathway level</a>, on bulk/microarray data.</p>
   208: <p>The <a href="https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html" rel="nofollow noreferrer">AUCell R package</a> identifies enriched gene sets within cells. It uses the Area Under the Curve (AUC) to calculate whether a gene set is enriched within the expressed genes for each cell.</p>
   3723: <p>Package <a href="https://bioconductor.org/packages/release/bioc/html/hipathia.html" rel="nofollow noreferrer">Hipathia</a> allows you to compute a value of "activity" for each pathway and each cell, so that the matrix of gene expression is transformed into a matrix of pathway activity. You can use this matrix for further analysis, cell differentiation and clustering. You can also compute functional activity matrices in the same way. </p>
  