User: Gordon Smyth

gravatar for Gordon Smyth
Gordon Smyth2.1k
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Australia
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http://www.statsci.org...
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9 hours ago
Joined:
6 years, 4 months ago
Email:
s****@wehi.edu.au

Joint Head of the Bioinformatics Division at the WEHI, Melbourne, Australia.

My research group created the limma, edgeR, goseq, Rsubread, csaw and diffHic packages, all part of the Bioconductor project.

Posts by Gordon Smyth

<prev • 157 results • page 1 of 16 • next >
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Answer: A: How to extract genes from goana
... This has to be done for each GO term individually because there might be 100s or 1000s of genes associated with the term. First, store the DE results: ``` tt <- topTable(fit, n=Inf) ``` Then see DE genes for a specifed GO term. Let's suppose you want to see the genes that `goana` has used for ...
written 5 days ago by Gordon Smyth2.1k
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Answer: A: How to get gene level of differential expression from 2 color channel array with
... It is not possible to consolidate microarray probes into genes. If you insist on having only one probe for each gene then you have to choose one probe to keep for each gene (e.g., the most highly expressed) and discard the others. For most purposes (e.g., pathway analysis) a few duplicate gene symbo ...
written 5 days ago by Gordon Smyth2.1k
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Answer: A: Microarray analysis in limma
... The original paper used a fold-change cutoff of 1.7, whereas in limma you are using a log2-fold-change cutoff of 1.7. Not the same thing. Setting lfc=1.7 corresponds to a fold-change of `2^1.7 = 3.25`, so naturally you will find fewer genes satisfying this much higher threshold. ...
written 8 days ago by Gordon Smyth2.1k
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Comment: C: How to analysis 2 color microarray data from GEO with limma?
... You can convert log2 if you want, it is optional. Makes no difference to the DE results (t-statistics, p-values, FDR etc). Only difference is that the logFC and AveExpr values will be on the log10 scale if you input log10. ...
written 8 days ago by Gordon Smyth2.1k
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Answer: A: How to analysis 2 color microarray data from GEO with limma?
... Yes, you can do either. You can analyse the matrix of normalized log-ratios that you get from GEO_query in limma. limma will accept the GEOquery object directly. Or you can read the raw data files into limma using `read.images`. Either way, the most important thing will be to setup the two-color des ...
written 8 days ago by Gordon Smyth2.1k
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Answer: A: Conversion of gene id to Refseq/gene symbol
... This is the method I use. First download ``` https://ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Mus_musculus.gene_info.gz ``` Then ``` > library(limma) > Aliases <- c("0610005C13Rik", "0610007P14Rik") > GeneAnnotation <- alias2SymbolUsingNCBI(Aliases, "Mus_musculus.gene_info. ...
written 6 weeks ago by Gordon Smyth2.1k
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Comment: C: Using ComBat-seq on transcript counts
... The ComBat-seq paper only mentions gene-level counts. I would guess that mapping uncertaintly would be a major issue for transcript level counts, and that ComBat-seq is only designed for gene level counts, but the ComBat-seq authors would have to confirm. It isn't clear to me whether OP has gene le ...
written 10 weeks ago by Gordon Smyth2.1k
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Answer: A: Using ComBat-seq on transcript counts
... ComBat-seq uses edgeR under the hood, which is able to handle fractional counts such as those from RSEM. You need to use the RSEM expected counts. There is no need to round them to exact integers. You absolutely cannot use TPM or FPKM. ...
written 10 weeks ago by Gordon Smyth2.1k
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Answer: A: glmQLFTest coef for multiple comparisons
... You can keep the design matrix as it is now. To compare 5 vs 2 for example, use ``` qlf5vs2 <- glmQLFTest(fit, contrast=c(0,-1,0,0,1,0,0)) ``` Why does this work? As you have correctly understood, the coefficients are all relative to level 1. So coef5=group5-group1 and coef2=group2-group1. So c ...
written 10 weeks ago by Gordon Smyth2.1k
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Comment: C: EdgeR exactTest help
... You omitted a crucial part of the edgeR example. I suggest you read further to understand what library sizes are and how they enter into measures of gene expression. Simulating 1 gene for 10,000 samples is a very strange thing to do, but is easy enough. Once you set the library sizes, you get an as ...
written 11 weeks ago by Gordon Smyth2.1k

Latest awards to Gordon Smyth

Scholar 5 days ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 8 days ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 8 days ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 9 weeks ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Teacher 3 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 3 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 4 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Good Answer 4 months ago, created an answer that was upvoted at least 5 times. For A: Differential expression: replicates in one condition, no replicates in the other
Scholar 4 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 4 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 5 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 5 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 7 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Teacher 8 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 8 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Supporter 8 months ago, voted at least 25 times.
Commentator 8 months ago, created a comment with at least 3 up-votes. For C: Trying to understand the maths behind one Limma function
Good Answer 10 months ago, created an answer that was upvoted at least 5 times. For A: Differential expression: replicates in one condition, no replicates in the other
Teacher 10 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Centurion 10 months ago, created 100 posts.
Teacher 11 months ago, created an answer with at least 3 up-votes. For A: published array data not replicable with limma analysis
Scholar 11 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In
Scholar 13 months ago, created an answer that has been accepted. For A: DESeq and Limma+Voom Normalization for Rna-Seq Data Using Ercc Spike-In

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