<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>

CC BY-SA 4.0
2018-11-23T15:41:29.863Z
2018-11-23T18:47:41.463Z
2018-11-23T18:47:41.463Z
Marta R. Hidalgo
<p>Check our recent <a href="https://academic.oup.com/narcancer/article/2/2/zcaa011/5862620" rel="nofollow noreferrer">paper</a> where we, not only characterized pathway activities in scRNAseq experiments, but also took advantage of mechanistic models in order to make some in silico prediction of drug effects.</p>

CC BY-SA 4.0
2020-09-03T15:34:44.757Z
2020-09-03T15:34:44.757Z
mfalco
<p><a href="https://www.biorxiv.org/content/10.1101/621805v1.full" rel="nofollow noreferrer">Single-Cell_Signature_Explorer paper</a> compared several such methods including: AUCell, Seurat CellCycleScore, and GSVA/ssGSEA.</p>

CC BY-SA 4.0
2019-10-28T06:58:02.533Z
2019-10-28T06:58:02.533Z
niuyw
<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>

CC BY-SA 4.0
2018-11-01T17:32:37.097Z
2018-11-01T17:32:37.097Z
Peter
<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>

CC BY-SA 4.0
2018-05-24T07:09:50.317Z
Hi, I find a bit unclear your question, could you clarify if you want to find (new?) pathways in scRNA-seq or how to analyze scRNA-seq using pathways? If the later, what is the problem with the methods you mention?
llrs
CC BY-SA 4.0
2018-05-25T10:43:19.080Z
I'm not trying to find new pathways, I want to use pathway- or biological function-level information to classify or understand cell diversity. GSVA is designed for bulk data, so I don't yet know how applicable it is to single cells. scde might be good.
Peter
CC BY-SA 4.0
2018-05-25T10:54:28.943Z
1)Pathways might (most probably do) change for each cell line (and in fact if it is not from a isolation, you don't know if this is a different cell line or not unless you traced it somehow) 2) We don't know enough about pathways and how they work (ie, there is few reliable information available, different databases define different what a pathway is, either by interactions, with kinetic constants an thus by contact (but then in which conditions are these measured?))
llrs
CC BY-SA 4.0
2018-05-25T10:59:10.243Z
Thanks for pointing me to `scde`. I [found](https://hms-dbmi.github.io/scde/pagoda.html) that it identifies "known pathways or novel gene sets"
llrs
<r><scrnaseq><pathway>
Pathway level analysis of single-cell gene expression
# Pathway level analysis of single-cell gene expression

(Asked by: username_0 on 2018-05-23)

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](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-6-225), implemented in the [GSVA](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-14-7) R package.

I have found only one method for single-cell RNA-seq that comes close: [pagoda in the scde R package](http://hms-dbmi.github.io/scde/pagoda.html). This performs PCAs for each pathway, using its genes.

A very similar question is [Classification (supervised learning) of expression data on pathway level](https://bioinformatics.stackexchange.com/questions/3645/classification-supervised-learning-of-expression-data-on-pathway-level), on bulk/microarray data.

**Tags:** r, scrnaseq, pathway
> **Comments:**
> **username_1**: Hi, I find a bit unclear your question, could you clarify if you want to find (new?) pathways in scRNA-seq or how to analyze scRNA-seq using pathways? If the later, what is the problem with the methods you mention?  
> **username_0**: I'm not trying to find new pathways, I want to use pathway- or biological function-level information to classify or understand cell diversity. GSVA is designed for bulk data, so I don't yet know how applicable it is to single cells. scde might be good.  
> **username_1**: 1)Pathways might (most probably do) change for each cell line (and in fact if it is not from a isolation, you don't know if this is a different cell line or not unless you traced it somehow) 2) We don't know enough about pathways and how they work (ie, there is few reliable information available, different databases define different what a pathway is, either by interactions, with kinetic constants an thus by contact (but then in which conditions are these measured?))  
> **username_1**: Thanks for pointing me to `scde`. I [found](https://hms-dbmi.github.io/scde/pagoda.html) that it identifies "known pathways or novel gene sets"  

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## 4 answers

### Answer by username_2 on 2018-11-23. Score: 2

Package [Hipathia](https://bioconductor.org/packages/release/bioc/html/hipathia.html) 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.


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### Answer by username_0 on 2018-11-01. Score: 1

The [AUCell R package](https://bioconductor.org/packages/release/bioc/vignettes/AUCell/inst/doc/AUCell.html) 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.


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### Answer by username_3 on 2020-09-03. Score: 0

Check our recent [paper](https://academic.oup.com/narcancer/article/2/2/zcaa011/5862620) where we, not only characterized pathway activities in scRNAseq experiments, but also took advantage of mechanistic models in order to make some in silico prediction of drug effects.


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### Answer by username_4 on 2019-10-28. Score: 0

[Single-Cell\_Signature\_Explorer paper](https://www.biorxiv.org/content/10.1101/621805v1.full) compared several such methods including: AUCell, Seurat CellCycleScore, and GSVA/ssGSEA.


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