Answers.Body: <p>I would recommend reading the following study by <a href="https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-021-02936-w" rel="nofollow noreferrer">Zhao <em>et al</em> (2021)</a>.</p>
  <p>The traditional simplest measure is CPM otherwise known as TPM. This is counts/transcripts per million. FPKM (fragments per kilobase of transcript per million fragments mapped) is used for metagenomics. I don't know its application in your context ... I don't know your context.</p>
  <p>The discussions for the pros- and cons- of FPKM and TPM/CPM are discussed therein.</p>
  <p>The authors in the study favour normalisation - <em>in their context</em> - not in general. The authors use the coefficient of variation, and intraclass correlation coefficient. I would see this as a form of standardisation. It's worth keeping an open mind because its a pivotal step in the calculation but all too easily overlooked area of investigation.</p>
  <p>From RamRS's comments that may have changed already. In general statistics standardisation is a pivotal step, usually followed by the removal of outliers.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-06T23:17:44.430Z
Answers.Text: Unfortunately, neither TPM nor CPM can be used in any context to compare two samples. FPKM is obsolete IMO
Answers.UserDisplayName: Ram RS
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Answers.CreationDate: 2023-06-06T23:27:50.797Z
Answers.Text: FPKM is used in metagenomics (a lot, lot more than one species present). Recovering a full length gene/transcript rarely happens.
Answers.UserDisplayName: M__
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-06-07T13:11:20.917Z
Answers.Text: Thank you, I did not know that.
Answers.UserDisplayName: Ram RS
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Answers.CreationDate: 2023-06-09T17:25:23.890Z
Answers.Text: @M__ Thanks for your response. I agree that in this case TPM normalization may be required when displaying them in a trackplot. I think those types of normalization may not be accurate when performing differential expression analysis through DESeq. However, my particular problem is with the fact that what I see in IGV ends up being different from what I see when I plot the bam file in R through the script written above.
Answers.UserDisplayName: A4747
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Answers.LastActivityDate: 2023-06-06T23:37:31.940Z
Answers.LastEditDate: 2023-06-06T23:37:31.940Z
Answers.UserDisplayName: M__
Body: <p><em>This question has also been asked on <a href="https://www.biostars.org/p/9565667/" rel="nofollow noreferrer">Biostars</a></em></p>
  <p>I have two bam files from single-cell RNA sequencing mapped to the reference genome using CellRanger, I can view them in IGV and I have a particular region where the pattern of reads mapped to the reference genome are different between the two bam files but when I try the following code to plot trackplots with coverage, the resulting plot is not consistent with what I see in IGV. I am not very familiar with this, aren't I supposed to normalize the data as well? please help me.</p>
  <pre class="lang-r prettyprint-override"><code>txdb &lt;-TxDb.Hsapiens.UCSC.hg38.knownGene
  txTr &lt;- GeneRegionTrack(txdb, chromosome = &quot;chr1&quot;, 
                          start = 153561336,  end = 153561564)
  dTrack &lt;- DataTrack(range = bam.file, genome = &quot;hg38&quot;, type = &quot;l&quot;, 
                       name = &quot;Coverage&quot;, window = -1, 
                       chromosome = &quot;chr1&quot;)
  
  dTrack0 &lt;- DataTrack(range = bam.file.0, genome = &quot;hg38&quot;, type = &quot;l&quot;, 
                       name = &quot;Coverage&quot;, window = -1, 
                       chromosome = &quot;chr1&quot;, col=&quot;red&quot;)
  
  plotTracks(c(dTrack,dTrack0, txTr), from = 153561357, to = 153561585)
  
  </code></pre>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2023-06-06T22:58:27.200Z
Comments.Text: Welcome @A4747 its useful to describe what you are dealing with, e.g. humans, a model system and any further project details. Essentially the answer will be project specific. Anyway provided an appropriate summary paper.
Comments.UserDisplayName: M__
Tags: <r><scrnaseq><sam><cellranger><gviz>
Title: Gviz Coverage Plots
ThreadText: # Gviz Coverage Plots
  
  (Asked by: username_0 on 2023-06-06)
  
  *This question has also been asked on [Biostars](https://www.biostars.org/p/9565667/)*
  
  I have two bam files from single-cell RNA sequencing mapped to the reference genome using CellRanger, I can view them in IGV and I have a particular region where the pattern of reads mapped to the reference genome are different between the two bam files but when I try the following code to plot trackplots with coverage, the resulting plot is not consistent with what I see in IGV. I am not very familiar with this, aren't I supposed to normalize the data as well? please help me.
  
  ```r
  txdb <-TxDb.Hsapiens.UCSC.hg38.knownGene
  txTr <- GeneRegionTrack(txdb, chromosome = "chr1", 
                          start = 153561336,  end = 153561564)
  dTrack <- DataTrack(range = bam.file, genome = "hg38", type = "l", 
                       name = "Coverage", window = -1, 
                       chromosome = "chr1")
  
  dTrack0 <- DataTrack(range = bam.file.0, genome = "hg38", type = "l", 
                       name = "Coverage", window = -1, 
                       chromosome = "chr1", col="red")
  
  plotTracks(c(dTrack,dTrack0, txTr), from = 153561357, to = 153561585)
  
  ```
  
  **Tags:** r, scrnaseq, sam, cellranger, gviz
  
  > **Comments:**
  > **username_1**: Welcome @username_0 its useful to describe what you are dealing with, e.g. humans, a model system and any further project details. Essentially the answer will be project specific. Anyway provided an appropriate summary paper.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2023-06-06. Score: 0
  
  I would recommend reading the following study by [Zhao *et al* (2021)](https://translational-medicine.biomedcentral.com/articles/10.1186/s12967-021-02936-w).
  
  The traditional simplest measure is CPM otherwise known as TPM. This is counts/transcripts per million. FPKM (fragments per kilobase of transcript per million fragments mapped) is used for metagenomics. I don't know its application in your context ... I don't know your context.
  
  The discussions for the pros- and cons- of FPKM and TPM/CPM are discussed therein.
  
  The authors in the study favour normalisation - *in their context* - not in general. The authors use the coefficient of variation, and intraclass correlation coefficient. I would see this as a form of standardisation. It's worth keeping an open mind because its a pivotal step in the calculation but all too easily overlooked area of investigation.
  
  From username_2's comments that may have changed already. In general statistics standardisation is a pivotal step, usually followed by the removal of outliers.
  
  > **Comments:**
  > **username_2**: Unfortunately, neither TPM nor CPM can be used in any context to compare two samples. FPKM is obsolete IMO  
  > **username_1**: FPKM is used in metagenomics (a lot, lot more than one species present). Recovering a full length gene/transcript rarely happens.  
  > **username_2**: Thank you, I did not know that.  
  > **username_0**: @username_1 Thanks for your response. I agree that in this case TPM normalization may be required when displaying them in a trackplot. I think those types of normalization may not be accurate when performing differential expression analysis through DESeq. However, my particular problem is with the fact that what I see in IGV ends up being different from what I see when I plot the bam file in R through the script written above.  
  
  ---
  
  