User: CE

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CE10
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United States
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10 months ago
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1 year, 3 months ago
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Posts by CE

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Time Series Analysis
... I have a question about analyzing time series data. If you use the formula design(dds) <- ~ time + treat + time:treat dds <- DESeq(dds, test="LRT", reduced = ~ time) I understand that you are seeing if treatment causes a change in gene expression at any time point, but for the treated term ...
timecourse deseq2 written 10 months ago by CE10 • updated 10 months ago by Michael Love24k
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Comment: C: DEseq2 time-series design.
... I know this is an old post, but I have a question regarding the comment "to generate p-values which test for any difference due to treatment including time=0 "  If you use the formula design(dds) <- ~ time + treat + time:treat dds <- DESeq(dds, test="LRT", reduced = ~ time) I understand th ...
written 10 months ago by CE10
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Comment: C: skewed differentially expressed gene results - DESeq2
... You are exactly right, when I plot a heatmap of the top differentially expressed genes, most of them appear to be significantly different in these same samples. They show a very similar pattern to this gene when I plotCounts(). I have almost 600 genes with padj < 0.05 which is a lot to sift throu ...
written 10 months ago by CE10
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Comment: C: skewed differentially expressed gene results - DESeq2
...   Thanks for the quick reply! Here is an example...   3 samples from the 'yes' group are clearly skewing the results. This gene has a padj of 0.001, a LFC of 1.22 before shrinking and 1.17 LFC after shrinking.  Most of our DE genes do not have very large fold changes and we are dealing with v ...
written 10 months ago by CE10
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skewed differentially expressed gene results - DESeq2
... Hi,  I'm not sure if this is a question of outliers, since it is happening with more than one sample in a group, but I am seeing genes coming back as being differentially expressed when they are only obviously different in 3-4 samples out of 16 total samples in a group compared to 18 samples in a c ...
deseq2 differential gene expression outliers written 10 months ago by CE10 • updated 10 months ago by Ryan C. Thompson7.3k
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Comment: C: DESeq2 vs Ballgown results
... Ok thanks I’ll try that. But so far, the genes we are getting back with DESeq2 make sense. The main issue is trying to understand why I’m not getting close to the same results with ballgown if what I’m seeing is real.  ...
written 15 months ago by CE10
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Comment: C: DESeq2 vs Ballgown results
... I may be doing something very wrong, because there is very little correlation. Here is how I made the graph:   des <- res_subset_df bg <- results_genes_subset des <- des[des$baseMean > 0,] des <- tibble::rownames_to_column(des, var = 'id') des <- des[rank(des$pvalue)<500 ...
written 15 months ago by CE10
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Comment: C: DESeq2 vs Ballgown results
... I believe this might be what you are asking for... I've plotted the p-value vs the rank of the p value for my deseq2 and ballgown results.    ...
written 15 months ago by CE10
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Comment: C: DESeq2 vs Ballgown results
... I am using a gene count matrix generated by stringtie as input for DESeq2. This is generated with the prepDE.py script here https://github.com/gpertea/stringtie/blob/master/prepDE.py. Can you give me a little more info on how to plot the rank?    Thanks! ...
written 15 months ago by CE10
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DESeq2 vs Ballgown results
... I am using Hisat2 and Stringtie for alignment and assembly of human samples. When I run ballgown on my data, I am getting 29 genes that are significantly differentially expressed, however when I use DESeq2 for the analysis, I get 930 genes that are significantly different (q<0.05). I also compare ...
deseq2 ballgown written 15 months ago by CE10

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