Body: <p>I received a large <em>sc-RNA-seq</em> data matrix, as of the nature of <em>sc-RNA-seq</em> compared to <em>bulk-RNA-seq</em> the data matrix is very sparse.
  Moreover, due to the fact that this is single cell data the cell progress stages become more dominant and might be dominant in finding the PC's.</p>
  
  <p>I want to apply a dimensionality reduction method, such as PCA or tSNE, in order to enter it to some working algorithm to find principle curve for pathway analysis.</p>
  
  <p>Ive encountered Bailey paper: "<a href="https://arxiv.org/abs/1208.4122" rel="nofollow noreferrer">Principal Component Analysis with Noisy and/or Missing Data</a>", but not sure if it will help me in the case of <em>sc-RNA-seq</em>.</p>
  
  <p>Would appreciate some reference link or other help
  Thanks</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-04T09:37:04.633Z
Comments.Text: Welcome to BioinfoSE. This is a fairly standard question, and its fairly easy as everything is in Seurat v3. Beyond that you will need to give information on the data set shape and the field (human genetics?).
Comments.UserDisplayName: M__
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-04T14:44:51.663Z
Comments.Text: @MichaelG. I'm not familiar with Seurat v3, is it a package that handle sparse data? regarding the data, its expression dataset of tumors it is size ~(20000, 500) but with a lot of empty entries; sorry if I'm not that clear I'm just getting into the field.
Comments.UserDisplayName: David
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2019-08-04T21:02:07.463Z
Comments.Text: This tutorial would be a good start: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html
Comments.UserDisplayName: Mack123456
Tags: <scrnaseq><pca>
Title: PCA on large sparse matrix of single cell RNA-seq
ThreadText: # PCA on large sparse matrix of single cell RNA-seq
  
  (Asked by: username_0 on 2019-08-04)
  
  I received a large *sc-RNA-seq* data matrix, as of the nature of *sc-RNA-seq* compared to *bulk-RNA-seq* the data matrix is very sparse.
  Moreover, due to the fact that this is single cell data the cell progress stages become more dominant and might be dominant in finding the PC's.
  
  I want to apply a dimensionality reduction method, such as PCA or tSNE, in order to enter it to some working algorithm to find principle curve for pathway analysis.
  
  Ive encountered Bailey paper: "[Principal Component Analysis with Noisy and/or Missing Data](https://arxiv.org/abs/1208.4122)", but not sure if it will help me in the case of *sc-RNA-seq*.
  
  Would appreciate some reference link or other help
  Thanks
  
  **Tags:** scrnaseq, pca
  
  > **Comments:**
  > **username_1**: Welcome to BioinfoSE. This is a fairly standard question, and its fairly easy as everything is in Seurat v3. Beyond that you will need to give information on the data set shape and the field (human genetics?).  
  > **username_0**: @MichaelG. I'm not familiar with Seurat v3, is it a package that handle sparse data? regarding the data, its expression dataset of tumors it is size ~(20000, 500) but with a lot of empty entries; sorry if I'm not that clear I'm just getting into the field.  
  > **username_2**: This tutorial would be a good start: https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html  
  
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  ## 0 answers
  
  