Answers.Body: <p>If you're interested in an unbiased approach you could have a look at <a href="https://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/Rpackages/WGCNA/" rel="nofollow noreferrer">Gene Correlation Network Analysis</a>. WGCNA is a fairly old R package, with a somewhat confusing workflow and results that can be difficult to interpret. It also needs a large sample set, preferably from fairly different biological conditions, to produce meaningful results.</p>
  
  <p>Asking how "gene expression signatures" differ from GSEA is like asking how surface textures differ from trademarks. Gene set enrichment analysis is for looking at gene rankings and seeing how they compare to a known / existing gene set. A gene set is considered "enriched" if the majority of the gene set appears in high-ranked genes (or alternatively in low-ranked genes), rather than a more even distribution of the set across the ranked gene list. Most commonly people will rank genes based on expression (or fold change), but the method is agnostic to the nature of the ranking.</p>
  
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-04-04T14:32:19.307Z
Answers.Text: WGCNA does need a large sample set but from the same biological condition in order to find the meaningful correlations between genes, otherwise the correlations are not good enough to infer that there is a relationship between genes
Answers.UserDisplayName: llrs
Answers.ContentLicense: CC BY-SA 3.0
Answers.CreationDate: 2018-04-04T16:25:44.210Z
Answers.Text: What does large sample set mean here? how many samples you mean?
Answers.UserDisplayName: stack_learner
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Answers.CreationDate: 2018-04-04T20:47:35.510Z
Answers.Text: I'll start the ball rolling with a suggestion of 40 samples: 40 cancer subtypes that are a good representation of the biological variation that you'll be testing on
Answers.UserDisplayName: gringer
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Answers.CreationDate: 2018-04-04T14:25:13.167Z
Answers.LastActivityDate: 2018-04-04T14:25:13.167Z
Answers.UserDisplayName: gringer
Answers.Body: <p>Gene Signatures are sets of genes to report a consensus signal or function. They are not defined in a particular way but can be defined by any chosen group depending on the biological function that you are interested in. Typically these are defined by differential gene expression analysis or principal component analysis, although other approaches such as network analysis or time-course data could also be used.</p>
  
  <p>There are plenty of examples of sets of genes published as gene signatures (especially in the field of cancer research). Those that have been cross-validated in other datasets or replicated by other research groups are ideal. If you wish to generate such a “signature” for a particular biological function, you could follow the approaches demonstrated in these published signatures (especially those that have been widely used and cited).</p>
  
  <p>A gene expression signature is used to generate a consensus expression signal for these groups of genes (representing the given biological function). A widely accepted way to do this across samples of bulk RNASeq (or microarray data) is a “meta-gene” from the first eigenvector (v[,1]) of the singular value decomposition (SVD). This dimension reduction identifies a major contributor to variation across samples (in these genes) without negatively correlated genes averaging out. Dimension reduction can also be achieved by PCA and tSNE, depending on the requirements of your analysis.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-05T13:48:57.893Z
Answers.Text: Hi- I'm interested in the “meta-gene” approach using SVD. Do you have any recommended reference at hand describing it? With the occasion, another approach to identify "meta-genes" is based on [non-nagative matrix factorization](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization).
Answers.UserDisplayName: dariober
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-06-05T14:03:51.183Z
Answers.Text: It’s described rather well here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261155/#!po=65.1786
Answers.UserDisplayName: Tom Kelly ケリー・トム
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Answers.UserDisplayName: Tom Kelly ケリー・トム
Answers.Body: <p>There is no golden/standard way to define a gene signature but they are completely different from a gene set enrichment analysis (GSEA). I will start with how to obtain a <strong>gene signature</strong>:</p>
  
  <p>Usually this kind of signatures are defined by comparing a group against another ie doing a differential gene expression analysis. One then selects the most up or down regulated genes and defines them as the signature of the group of cells. That's how the gene sets of the <a href="http://software.broadinstitute.org/gsea/msigdb/index.jsp" rel="noreferrer">MSigDB</a> which are not from (or derived from) pathway databases or gene ontologies are defined. There is no gold threshold of a fold change or a specific number of genes, it (highly) depends on the experiment you run and how good are the isolation of the cells.<br>
  Tools: DESeq2, limma, edgeR,...</p>
  
  <p>As suggested by gringer, another way is to find which genes are expressed together (independently of how do they relate to other groups). This method relays on having a good enough number of samples from the same condition to be able to identify which genes are expressed the same way in those cells. If these genes are not expressed that way in other cell lines, then you have your signature for this group. Again, there isn't a golden method, different types of co-expression networks can be build and depends on how specific do you want to be or which is the data provided.<br>
  Tools: WGCNA, diffcoexp, TReNA,...</p>
  
  <p>This is different from a <strong>gene <em>set</em> enrichment analysis</strong><sup>1</sup>, which consists on evaluating if a group of genes (usually a gene set, a pathway or genes from a specific gene ontology) are not randomly sorted in a sorted list of genes (usually this sorted list is the fold-change of a differential analysis).  </p>
  
  <p>There are several methods developed depending on the null hypothesis, the type of sub-method and what do they take into account...<br>
  Tools: GSEA, PLAGUE, GSVA, limma, fgsea,... </p>
  
  <p>TLDR: To perform a gene set enrichment you don't need a gene signature but a gene set (that <em>can</em> be a gene signature or a pathway or a random group).</p>
  
  <p>1: Note that gene set enrichment analyisis is different from gene enrichment analysis. For a good review on the methods I recommend this <a href="http://dx.plos.org/10.1371/journal.pcbi.1002375" rel="noreferrer">article</a></p>
  
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Answers.UserDisplayName: llrs
Body: <p>I have TCGA gene expression data. I'm interested in identifying gene expression signatures using the data. </p>
  
  <p>I would like to know whether there are any tools or R packages for identifying gene signatures.</p>
  
  <p>How are gene signatures different from GSEA?</p>
  
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T10:28:06.247Z
Comments.Text: @gringer, usually these is the wording used to denote genes that their expression is characteristic of a specific group of cells (cell lines or not), I read it also as marker genes (not in the sense that are used because one can mark them in the surface or in a specific region of the cell but because the expression pattern is specific of these cells). But user335123 Could you confirm that we are talking about the same meaning?
Comments.UserDisplayName: llrs
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Comments.CreationDate: 2018-04-04T11:13:41.203Z
Comments.Text: @Llopis yes what said was right !!
Comments.UserDisplayName: stack_learner
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Comments.CreationDate: 2018-04-04T14:15:05.370Z
Comments.Text: Why do you want to do this? I have never seen any reason to generate new gene signatures/gene sets from TCGA, since they already exist for humans. Or are you trying to create signatures for specific cancer types?
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:16:48.697Z
Comments.Text: BTW, if the (more standard) definition of a gene signature from @Llopis is correct then please edit your post accordingly.
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:20:08.823Z
Comments.Text: @Devon, do you mean that exists other gene signatures for humans (in a database)? Could you please provide me a link or a reference? User I'will answer later
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:22:43.443Z
Comments.Text: @Llopis The question needs to be appropriately clarified by user3351523 first, since whether a database exists is dependent upon that (and anyway I'm not putting it in a comment that I'm going to delete).
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:29:04.170Z
Comments.Text: I don't understand: my comment got deleted and now the conformation that what I rephrased is isolated (you where refering to that comment that could be deleted?). Are you looking for clarification on if it either for human or if it is what I had written in my first comment?
Comments.UserDisplayName: llrs
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:34:23.237Z
Comments.Text: @Llopis Oops, restored the comments. They got flagged for deletion and I didn't check thoroughly enough. OP needs to correctly edit into their question what the exact goal is, since it seems that they didn't even define gene signature properly in the original post.
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T14:34:59.723Z
Comments.Text: GAH, the incorrect definition wasn't from OP!
Comments.UserDisplayName: Devon Ryan
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T16:23:07.693Z
Comments.Text: @DevonRyan I'm interested in generating new signatures for lung cancer histological subtypes.
Comments.UserDisplayName: stack_learner
Comments.ContentLicense: CC BY-SA 3.0
Comments.CreationDate: 2018-04-04T20:41:55.317Z
Comments.Text: It was a comment from OP, which I flagged for deletion after adding it to the question. The question is not clarified enough as it is to give a useful answer.
Comments.UserDisplayName: gringer
Tags: <rna-seq><r><gene><bioconductor>
Title: How to identify gene expression signatures from gene expression data?
ThreadText: # How to identify gene expression signatures from gene expression data?
  
  (Asked by: username_0 on 2018-04-04)
  
  I have TCGA gene expression data. I'm interested in identifying gene expression signatures using the data.
  
  I would like to know whether there are any tools or R packages for identifying gene signatures.
  
  How are gene signatures different from GSEA?
  
  **Tags:** rna-seq, r, gene, bioconductor
  
  > **Comments:**
  > **username_1**: @username_3, usually these is the wording used to denote genes that their expression is characteristic of a specific group of cells (cell lines or not), I read it also as marker genes (not in the sense that are used because one can mark them in the surface or in a specific region of the cell but because the expression pattern is specific of these cells). But user335123 Could you confirm that we are talking about the same meaning?  
  > **username_0**: @Llopis yes what said was right !!  
  > **username_2**: Why do you want to do this? I have never seen any reason to generate new gene signatures/gene sets from TCGA, since they already exist for humans. Or are you trying to create signatures for specific cancer types?  
  > **username_2**: BTW, if the (more standard) definition of a gene signature from @Llopis is correct then please edit your post accordingly.  
  > **username_1**: @username_2, do you mean that exists other gene signatures for humans (in a database)? Could you please provide me a link or a reference? User I'will answer later  
  > **username_2**: @Llopis The question needs to be appropriately clarified by user3351523 first, since whether a database exists is dependent upon that (and anyway I'm not putting it in a comment that I'm going to delete).  
  > **username_1**: I don't understand: my comment got deleted and now the conformation that what I rephrased is isolated (you where refering to that comment that could be deleted?). Are you looking for clarification on if it either for human or if it is what I had written in my first comment?  
  > **username_2**: @Llopis Oops, restored the comments. They got flagged for deletion and I didn't check thoroughly enough. OP needs to correctly edit into their question what the exact goal is, since it seems that they didn't even define gene signature properly in the original post.  
  > **username_2**: GAH, the incorrect definition wasn't from OP!  
  > **username_0**: @username_2 I'm interested in generating new signatures for lung cancer histological subtypes.  
  > **username_3**: It was a comment from OP, which I flagged for deletion after adding it to the question. The question is not clarified enough as it is to give a useful answer.  
  
  ---
  
  ## 3 answers
  
  ### Answer by username_1 on 2018-04-04. Score: 7 | Accepted answer
  
  There is no golden/standard way to define a gene signature but they are completely different from a gene set enrichment analysis (GSEA). I will start with how to obtain a **gene signature**:
  
  Usually this kind of signatures are defined by comparing a group against another ie doing a differential gene expression analysis. One then selects the most up or down regulated genes and defines them as the signature of the group of cells. That's how the gene sets of the [MSigDB](http://software.broadinstitute.org/gsea/msigdb/index.jsp) which are not from (or derived from) pathway databases or gene ontologies are defined. There is no gold threshold of a fold change or a specific number of genes, it (highly) depends on the experiment you run and how good are the isolation of the cells.  
  Tools: DESeq2, limma, edgeR,...
  
  As suggested by username_3, another way is to find which genes are expressed together (independently of how do they relate to other groups). This method relays on having a good enough number of samples from the same condition to be able to identify which genes are expressed the same way in those cells. If these genes are not expressed that way in other cell lines, then you have your signature for this group. Again, there isn't a golden method, different types of co-expression networks can be build and depends on how specific do you want to be or which is the data provided.  
  Tools: WGCNA, diffcoexp, TReNA,...
  
  This is different from a **gene *set* enrichment analysis**1, which consists on evaluating if a group of genes (usually a gene set, a pathway or genes from a specific gene ontology) are not randomly sorted in a sorted list of genes (usually this sorted list is the fold-change of a differential analysis).
  
  There are several methods developed depending on the null hypothesis, the type of sub-method and what do they take into account...  
  Tools: GSEA, PLAGUE, GSVA, limma, fgsea,...
  
  TLDR: To perform a gene set enrichment you don't need a gene signature but a gene set (that *can* be a gene signature or a pathway or a random group).
  
  1: Note that gene set enrichment analyisis is different from gene enrichment analysis. For a good review on the methods I recommend this [article](http://dx.plos.org/10.1371/journal.pcbi.1002375)
  
  
  ---
  
  ### Answer by username_3 on 2018-04-04. Score: 4
  
  If you're interested in an unbiased approach you could have a look at [Gene Correlation Network Analysis](https://labs.genetics.ucla.edu/horvath/htdocs/CoexpressionNetwork/Rpackages/WGCNA/). WGCNA is a fairly old R package, with a somewhat confusing workflow and results that can be difficult to interpret. It also needs a large sample set, preferably from fairly different biological conditions, to produce meaningful results.
  
  Asking how "gene expression signatures" differ from GSEA is like asking how surface textures differ from trademarks. Gene set enrichment analysis is for looking at gene rankings and seeing how they compare to a known / existing gene set. A gene set is considered "enriched" if the majority of the gene set appears in high-ranked genes (or alternatively in low-ranked genes), rather than a more even distribution of the set across the ranked gene list. Most commonly people will rank genes based on expression (or fold change), but the method is agnostic to the nature of the ranking.
  
  > **Comments:**
  > **username_1**: WGCNA does need a large sample set but from the same biological condition in order to find the meaningful correlations between genes, otherwise the correlations are not good enough to infer that there is a relationship between genes  
  > **username_0**: What does large sample set mean here? how many samples you mean?  
  > **username_3**: I'll start the ball rolling with a suggestion of 40 samples: 40 cancer subtypes that are a good representation of the biological variation that you'll be testing on  
  
  ---
  
  ### Answer by username_4 on 2018-06-02. Score: 2
  
  Gene Signatures are sets of genes to report a consensus signal or function. They are not defined in a particular way but can be defined by any chosen group depending on the biological function that you are interested in. Typically these are defined by differential gene expression analysis or principal component analysis, although other approaches such as network analysis or time-course data could also be used.
  
  There are plenty of examples of sets of genes published as gene signatures (especially in the field of cancer research). Those that have been cross-validated in other datasets or replicated by other research groups are ideal. If you wish to generate such a “signature” for a particular biological function, you could follow the approaches demonstrated in these published signatures (especially those that have been widely used and cited).
  
  A gene expression signature is used to generate a consensus expression signal for these groups of genes (representing the given biological function). A widely accepted way to do this across samples of bulk RNASeq (or microarray data) is a “meta-gene” from the first eigenvector (v[,1]) of the singular value decomposition (SVD). This dimension reduction identifies a major contributor to variation across samples (in these genes) without negatively correlated genes averaging out. Dimension reduction can also be achieved by PCA and tSNE, depending on the requirements of your analysis.
  
  > **Comments:**
  > **username_5**: Hi- I'm interested in the “meta-gene” approach using SVD. Do you have any recommended reference at hand describing it? With the occasion, another approach to identify "meta-genes" is based on [non-nagative matrix factorization](https://en.wikipedia.org/wiki/Non-negative_matrix_factorization).  
  > **username_4**: It’s described rather well here: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261155/#!po=65.1786  
  
  ---
  
  