text: user4595: Sorry I just got totally confused conceptually.
  If this is my raw count data
  Likely, Seurat divides each value by sum of the column afterward times by 10000. 
  which gives so 
                     s1.1        s1.2 s1.3       s1.4 s1.5
  DDB_G0267178 0.00000000 0.009263254    0 0.01286397    0
  DDB_G0267180 0.00000000 0.000000000    0 0.00000000    0
  DDB_G0267182 0.00000000 0.000000000    0 0.03810585    0
  DDB_G0267184 0.00000000 0.000000000    0 0.00000000    0
  DDB_G0267188 0.02640801 0.000000000    0 0.01286397    0
  
  Literally Seurat claims that final step would be taking natural log of the above matrix as I tested with 
  library(seurat)
  mat <- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
  mat
  
      [,1] [,2] [,3] [,4] [,5]
  [1,]    1    0    1    1    1
  [2,]    1    0    2    2    3
  [3,]    1    1    1    1    2
  [4,]    1    1    0    1    0
  [5,]    0    0    0    1    3
  mat_norm <- LogNormalize(data = mat)
  mat_norm
  [1,] 7.824446 .        7.824446 7.419181 7.014015
  [2,] 7.824446 .        8.517393 8.112028 8.112028
  [3,] 7.824446 8.517393 7.824446 7.419181 7.706713
  [4,] 7.824446 8.517393 .        7.419181 .       
  [5,] .        .        .        7.419181 8.112028
  
  My confusion is: seurat@data dose not give me natural log transformed data rather returns only data divided by column sum and scalled by 10000.
  So when plotting a violin plot if data are log normalised plot should not look as below picture 
  When I used 
  VlnPlot(object = y, features.plot = "DDB_G0277853", x.lab.rot = FALSE, y.log = TRUE)
  
  plot changed totally. Could someone please help me in getting idea about seurat@data that is not really log transformed and by which data violin plot is being produced??? So if a gene has 10000 read counts, without log transformation violin plot should be so
  This is my seurat object
  
  user2388: Those two graphs don't look radically different.   The y axis is definitely not log scaled on the top one, and it looks like it is scaled in the second one (y.log = TRUE)
  
  user208: The normalized and log-transformed values are used for the violin plot.
  The argument y.log changes only the display of the data (scaling of the y axis).
  Seurat has very good documentation. Section 7 in the FAQ explains what data is stored in the object:
  
  
  How is data stored within the Seurat object? What is the difference between raw.data, data, and scale.data?
  
  raw.data The raw data slot
  (object@raw.data) represents the original expression matrix, input
  when creating the Seurat object, and prior to any preprocessing by
  Seurat.
  data The data slot
  (object@data) stores normalized and log-transformed single cell
  expression. This maintains the relative abundance levels of all genes,
  and contains only zeros or positive values. See ?NormalizeData for
  more information. This data is used for visualizations, such as violin
  and feature plots, most differential expression tests, finding
  high-variance genes, and as input to ScaleData (see below).
  scale.data
  The scale.data slot (object@scale.data) represents a cell’s relative
  expression of each gene, in comparison to all other cells. Therefore
  this matrix contains both positive and negative values. See ?ScaleData
  for more information.
  
  
original_text: 4595: <p>Sorry I just got totally confused conceptually.</p>
  
  <p>If this is my raw count data</p>
  
  <p>Likely, Seurat divides each value by sum of the column afterward times by 10000. </p>
  
  <p>which gives so </p>
  
  <pre><code>                   s1.1        s1.2 s1.3       s1.4 s1.5
  DDB_G0267178 0.00000000 0.009263254    0 0.01286397    0
  DDB_G0267180 0.00000000 0.000000000    0 0.00000000    0
  DDB_G0267182 0.00000000 0.000000000    0 0.03810585    0
  DDB_G0267184 0.00000000 0.000000000    0 0.00000000    0
  DDB_G0267188 0.02640801 0.000000000    0 0.01286397    0
  </code></pre>
  
  <p>Literally Seurat claims that final step would be taking natural log of the above matrix as I tested with </p>
  
  <pre><code>library(seurat)
  mat &lt;- matrix(data = rbinom(n = 25, size = 5, prob = 0.2), nrow = 5)
  mat
  
      [,1] [,2] [,3] [,4] [,5]
  [1,]    1    0    1    1    1
  [2,]    1    0    2    2    3
  [3,]    1    1    1    1    2
  [4,]    1    1    0    1    0
  [5,]    0    0    0    1    3
  mat_norm &lt;- LogNormalize(data = mat)
  mat_norm
  [1,] 7.824446 .        7.824446 7.419181 7.014015
  [2,] 7.824446 .        8.517393 8.112028 8.112028
  [3,] 7.824446 8.517393 7.824446 7.419181 7.706713
  [4,] 7.824446 8.517393 .        7.419181 .       
  [5,] .        .        .        7.419181 8.112028
  </code></pre>
  
  <p>My confusion is: seurat@data dose not give me natural log transformed data rather returns only data divided by column sum and scalled by 10000.
  So when plotting a violin plot if data are log normalised plot should not look as below picture </p>
  
  <p>When I used </p>
  
  <pre><code>VlnPlot(object = y, features.plot = "DDB_G0277853", x.lab.rot = FALSE, y.log = TRUE)
  </code></pre>
  
  <p>plot changed totally. Could someone please help me in getting idea about seurat@data that is not really log transformed and by which data violin plot is being produced??? So if a gene has 10000 read counts, without log transformation violin plot should be so</p>
  
  <p>This is my seurat object</p>
   2388: <p>Those two graphs don't look radically different.   The y axis is definitely not log scaled on the top one, and it looks like it is scaled in the second one (y.log = TRUE)</p>
   208: <p>The normalized and log-transformed values are used for the violin plot.
  The argument <code>y.log</code> changes only the display of the data (scaling of the y axis).</p>
  <p>Seurat has very good documentation. <a href="https://satijalab.org/seurat/faq" rel="nofollow noreferrer">Section 7 in the FAQ</a> explains what data is stored in the object:</p>
  <blockquote>
  <ol start="7">
  <li>How is data stored within the Seurat object? What is the difference between raw.data, data, and scale.data?</li>
  </ol>
  <p><strong>raw.data</strong> The raw data slot
  (object@raw.data) represents the original expression matrix, input
  when creating the Seurat object, and prior to any preprocessing by
  Seurat.</p>
  <p><strong>data</strong> The data slot
  (object@data) stores normalized and log-transformed single cell
  expression. This maintains the relative abundance levels of all genes,
  and contains only zeros or positive values. See ?NormalizeData for
  more information. This data is used for visualizations, such as violin
  and feature plots, most differential expression tests, finding
  high-variance genes, and as input to ScaleData (see below).</p>
  <p><strong>scale.data</strong>
  The scale.data slot (object@scale.data) represents a cell’s relative
  expression of each gene, in comparison to all other cells. Therefore
  this matrix contains both positive and negative values. See ?ScaleData
  for more information.</p>
  </blockquote>
  