Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between genes
$begingroup$
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
asked 3 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
asked 3 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
asked 3 hours ago
gc5gc5
721216
$endgroup$
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
rna-seq normalization fold-change
asked 3 hours ago
gc5gc5
721216
asked 3 hours ago
gc5gc5
721216
asked 3 hours ago
gc5gc5
721216
asked 3 hours ago
gc5gc5
721216
asked 3 hours ago
gc5gc5
721216
721216
add a comment |
add a comment |
3 Answers
3
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oldest
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$begingroup$
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.
answered 2 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
1 hour ago
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!
answered 2 hours ago
h3ab74h3ab74
836
$endgroup$
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.
answered 1 hour ago
swbarnes2swbarnes2
47114
$endgroup$
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
$begingroup$
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.
answered 2 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
1 hour 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.
answered 2 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
1 hour 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.
answered 2 hours ago
gringergringer
7,79221049
$endgroup$
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.
answered 2 hours ago
gringergringer
7,79221049
answered 2 hours ago
gringergringer
7,79221049
answered 2 hours ago
gringergringer
7,79221049
answered 2 hours ago
gringergringer
7,79221049
7,79221049
$begingroup$
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
1 hour ago
add a comment |
$begingroup$
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
1 hour ago
$begingroup$
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
1 hour ago
$begingroup$
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
1 hour ago
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!
answered 2 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!
answered 2 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!
answered 2 hours ago
h3ab74h3ab74
836
$endgroup$
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!
answered 2 hours ago
h3ab74h3ab74
836
answered 2 hours ago
h3ab74h3ab74
836
answered 2 hours ago
h3ab74h3ab74
836
answered 2 hours ago
h3ab74h3ab74
836
836
add a comment |
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.
answered 1 hour ago
swbarnes2swbarnes2
47114
$endgroup$
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.
answered 1 hour ago
swbarnes2swbarnes2
47114
$endgroup$
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.
answered 1 hour ago
swbarnes2swbarnes2
47114
$endgroup$
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.
answered 1 hour ago
swbarnes2swbarnes2
47114
answered 1 hour ago
swbarnes2swbarnes2
47114
answered 1 hour ago
swbarnes2swbarnes2
47114
answered 1 hour ago
swbarnes2swbarnes2
47114
47114
add a comment |
add a comment |
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