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Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes

Normalization methods with RNA-Seq ERCC spike in?Confirm success or failure RNA-Seq normalizationWhat are the ways to process a list of differentially expressed genes?What methods are available to find a cutoff value for non-expressed genes in RNA-seq?qPCR: Why is fold change and standard deviation calculated after transformation?Order of batch effects removal, data imputation and library size normalization in scRNA-seq dataDetecting differentially expressed genes with foldchange >= 2 and FDR < 0.05Selection of differential expressed genesK mean clustering issueHow to quantile normalization on RNA seq counts

2

$begingroup$

I have two bulk RNA-Seq samples, already tpm-normalized.

I would like to know what is a reasonable normalization procedure to enable downstream log fold-change estimation.

The distribution of the two samples using the common set of genes looks similar:

However, the two samples have only been tpm-normalized, is it enough to guarantee reliable fold-change estimation? Should I use another normalization procedure, e.g. Quantile Normalization, before comparison?

My objective is to define a signature using the genes that are up-regulated in Sample1 with respect to Sample0, and vice versa. I'm using log fold-changes, but I'm concerned that their value may be affected by each sample distribution.

Do you also have suggestions for the definition of up-regulated genes with these data?

rna-seq normalization fold-change

share|improve this question

asked 10 hours ago

gc5gc5

721216

$endgroup$

add a comment | 

2

$begingroup$

I have two bulk RNA-Seq samples, already tpm-normalized.

I would like to know what is a reasonable normalization procedure to enable downstream log fold-change estimation.

The distribution of the two samples using the common set of genes looks similar:

However, the two samples have only been tpm-normalized, is it enough to guarantee reliable fold-change estimation? Should I use another normalization procedure, e.g. Quantile Normalization, before comparison?

My objective is to define a signature using the genes that are up-regulated in Sample1 with respect to Sample0, and vice versa. I'm using log fold-changes, but I'm concerned that their value may be affected by each sample distribution.

Do you also have suggestions for the definition of up-regulated genes with these data?

rna-seq normalization fold-change

share|improve this question

asked 10 hours ago

gc5gc5

721216

$endgroup$

add a comment | 

$begingroup$

I have two bulk RNA-Seq samples, already tpm-normalized.

I would like to know what is a reasonable normalization procedure to enable downstream log fold-change estimation.

The distribution of the two samples using the common set of genes looks similar:

However, the two samples have only been tpm-normalized, is it enough to guarantee reliable fold-change estimation? Should I use another normalization procedure, e.g. Quantile Normalization, before comparison?

My objective is to define a signature using the genes that are up-regulated in Sample1 with respect to Sample0, and vice versa. I'm using log fold-changes, but I'm concerned that their value may be affected by each sample distribution.

Do you also have suggestions for the definition of up-regulated genes with these data?

rna-seq normalization fold-change

share|improve this question

asked 10 hours ago

gc5gc5

721216

$endgroup$

share|improve this question

asked 10 hours ago

gc5gc5

721216

share|improve this question

asked 10 hours ago

gc5gc5

721216

asked 10 hours ago

gc5gc5

721216

asked 10 hours ago

gc5gc5

721216

3 Answers
3

active

oldest

votes

2

$begingroup$

It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. DESeq2 includes read normalisation as part of its methods for differential expression analysis.

share|improve this answer

answered 9 hours ago

gringergringer

7,79221049

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with TPM for a lot of reasons, unfortunately the data was already in TPM. Can you explain more about how read counts are useful to determine shot noise and statistical significance? Thanks
  $endgroup$
  – gc5
  8 hours ago
  

  
  
  
  

add a comment | 

2

$begingroup$

What I have generally done in the past is to process the data using voom in the limma package for bulk RNASeq. Inside voom you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.

voom will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.

Check out this link for more info on the package as well as normalization methods: https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html
It is a very thorough introduction to the package and all of its capabilities.

Good luck!

share|improve this answer

answered 9 hours ago

h3ab74h3ab74

836

$endgroup$

add a comment | 

1

$begingroup$

You have only two samples?

You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.

share|improve this answer

answered 7 hours ago

swbarnes2swbarnes2

48114

$endgroup$

add a comment | 

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2

$begingroup$

It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. DESeq2 includes read normalisation as part of its methods for differential expression analysis.

share|improve this answer

answered 9 hours ago

gringergringer

7,79221049

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with TPM for a lot of reasons, unfortunately the data was already in TPM. Can you explain more about how read counts are useful to determine shot noise and statistical significance? Thanks
  $endgroup$
  – gc5
  8 hours ago
  

  
  
  
  

add a comment | 

2

$begingroup$

It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. DESeq2 includes read normalisation as part of its methods for differential expression analysis.

share|improve this answer

answered 9 hours ago

gringergringer

7,79221049

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  I agree with TPM for a lot of reasons, unfortunately the data was already in TPM. Can you explain more about how read counts are useful to determine shot noise and statistical significance? Thanks
  $endgroup$
  – gc5
  8 hours ago
  

  
  
  
  

add a comment | 

$begingroup$

It's not a good idea to do tpm normalisation prior to differential expression analysis, because the actual read counts are useful to determine shot noise and statistical significance. DESeq2 includes read normalisation as part of its methods for differential expression analysis.

share|improve this answer

answered 9 hours ago

gringergringer

7,79221049

$endgroup$

share|improve this answer

answered 9 hours ago

gringergringer

7,79221049

answered 9 hours ago

gringergringer

7,79221049

answered 9 hours ago

gringergringer

7,79221049

2

$begingroup$

What I have generally done in the past is to process the data using voom in the limma package for bulk RNASeq. Inside voom you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.

voom will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.

Check out this link for more info on the package as well as normalization methods: https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html
It is a very thorough introduction to the package and all of its capabilities.

Good luck!

share|improve this answer

answered 9 hours ago

h3ab74h3ab74

836

$endgroup$

add a comment | 

2

$begingroup$

What I have generally done in the past is to process the data using voom in the limma package for bulk RNASeq. Inside voom you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.

voom will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.

Check out this link for more info on the package as well as normalization methods: https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html
It is a very thorough introduction to the package and all of its capabilities.

Good luck!

share|improve this answer

answered 9 hours ago

h3ab74h3ab74

836

$endgroup$

add a comment | 

$begingroup$

What I have generally done in the past is to process the data using voom in the limma package for bulk RNASeq. Inside voom you can call for different normalization methods to be used - "TMM" works fine for me and, is advocated by many in the field.

voom will output an object containing the normalized expression values in a log2 scale, which, also in my experience, has worked out just fine for calculating log fold changes.

Check out this link for more info on the package as well as normalization methods: https://www.bioconductor.org/packages/devel/workflows/vignettes/RNAseq123/inst/doc/limmaWorkflow.html
It is a very thorough introduction to the package and all of its capabilities.

Good luck!

share|improve this answer

answered 9 hours ago

h3ab74h3ab74

836

$endgroup$

share|improve this answer

answered 9 hours ago

h3ab74h3ab74

836

answered 9 hours ago

h3ab74h3ab74

836

answered 9 hours ago

h3ab74h3ab74

836

1

$begingroup$

You have only two samples?

You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.

share|improve this answer

answered 7 hours ago

swbarnes2swbarnes2

48114

$endgroup$

add a comment | 

1

$begingroup$

You have only two samples?

You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.

share|improve this answer

answered 7 hours ago

swbarnes2swbarnes2

48114

$endgroup$

add a comment | 

$begingroup$

You have only two samples?

You aren't going to be able to draw strong conclusions from that no matter what you do. Clever statistics don't work without replicates.

share|improve this answer

answered 7 hours ago

swbarnes2swbarnes2

48114

$endgroup$

share|improve this answer

answered 7 hours ago

swbarnes2swbarnes2

48114

answered 7 hours ago

swbarnes2swbarnes2

48114

answered 7 hours ago

swbarnes2swbarnes2

48114