Normalization for two bulk RNA-Seq samples to enable reliable fold-change estimation between...
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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
$begingroup$
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 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
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
asked 10 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 10 hours ago
gc5gc5
721216
asked 10 hours ago
gc5gc5
721216
asked 10 hours ago
gc5gc5
721216
asked 10 hours ago
gc5gc5
721216
asked 10 hours ago
gc5gc5
721216
721216
add a comment |
add a comment |
3 Answers
3
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 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 |
$begingroup$
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 9 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 7 hours ago
swbarnes2swbarnes2
48114
$endgroup$
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 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.
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.
answered 9 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 9 hours ago
gringergringer
7,79221049
answered 9 hours ago
gringergringer
7,79221049
answered 9 hours ago
gringergringer
7,79221049
answered 9 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
8 hours 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
8 hours 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
8 hours 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
8 hours 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 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!
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!
answered 9 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 9 hours ago
h3ab74h3ab74
836
answered 9 hours ago
h3ab74h3ab74
836
answered 9 hours ago
h3ab74h3ab74
836
answered 9 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 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.
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.
answered 7 hours ago
swbarnes2swbarnes2
48114
$endgroup$
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 7 hours ago
swbarnes2swbarnes2
48114
answered 7 hours ago
swbarnes2swbarnes2
48114
answered 7 hours ago
swbarnes2swbarnes2
48114
answered 7 hours ago
swbarnes2swbarnes2
48114
48114
add a comment |
add a comment |
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