User: Beginner

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Beginner50
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Posts by Beginner

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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... Hi Klenk, The one which you posted above  write.csv(as.data.frame(res[!is.na(res$padj) & res$padj < 0.1 & res$log2FoldChange > log2(1.5),]), file="DEGs.csv") is not the right one to select DEG's based on FC > 1.5 and FDR < 0.1. From this [https://support.bioconductor.org/p/867 ...
written 6 days ago by Beginner50
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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... Yes, this worked. A question about the PCA plot I posted. Is the sample behaviour is due to batch effect? If yes how can I remove that and proceed to differential analysis? ...
written 6 days ago by Beginner50
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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... But this gave an error. Error: logical subscript contains NAs​ ...
written 6 days ago by Beginner50
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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... So, you mean to select candidates for wet lab experiments going with fold change > 1.5 and p.value < 0.05 not a bad idea? In my analysis I would also like to select candidates for experiments so I feel like following their way. ...
written 6 days ago by Beginner50
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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... @Michael Love A small question.  I selected DEGs with res <- results(dds, lfcThreshold = log2(1.5), alpha = 0.1) summary(res) out of 11949 with nonzero total read count adjusted p-value < 0.1 LFC > 0.58 (up) : 15, 0.13% LFC < -0.58 (down) : 5, 0.042% outliers [1] : 81, 0.68% ...
written 6 days ago by Beginner50
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Comment: C: Possible ways to select differential expressed genes using DeSeq2 or edgeR
... Thanks a lot for the answers. But in one of the nature paper I see that they have selected differential genes based on foldchange > 5 and p.value < 0.05. Check Figure1a legend in this paper [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4927085/] ...
written 7 days ago by Beginner50
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Possible ways to select differential expressed genes using DeSeq2 or edgeR
... I'm working with RNA-seq data. I have 40 tumor samples and 5 Normal samples. Differential analysis with Deseq2 based on Fold change > 1.2 and alpha < 0.05 gave very low number of differentially expressed genes. Only 2 upregulated genes.      res <- results(dds, lfcThreshold = log2(1.2), al ...
edger deseq2 R differential gene expression fold change written 7 days ago by Beginner50 • updated 7 days ago by Axel Klenk920
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Comment: C: Differential analysis based on selection of random samples
... Thanks. And could you please tell when subsampling can be applied for differential analysis? ...
written 11 days ago by Beginner50
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Comment: C: Differential analysis based on selection of random samples
... Thanks for the reply. Basically with full analysis (35 tumor vs 4 normal samples) I got only 4 Upregulated genes using results function [results(dds, lfcThreshold = log2(1.2), alpha = 0.05)]. I felt random-subsampling can be applied to get more Upregulated genes from different analysis and then merg ...
written 12 days ago by Beginner50
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Differential analysis based on selection of random samples
... I have 35 tumor and 4 normal samples. I'm using DESeq2 for differential analysis. Differential analysis between tumor and normal gave only two upregulated genes which could be due to statistical power. So, I'm interested in selection of random samples from tumor condition and do differential analysi ...
rnaseq deseq2 R bioconductor differential gene expression written 13 days ago by Beginner50 • updated 12 days ago by Michael Love18k

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