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I would suggest looking at some of the documentation. As far as I can tell, the intention of the package is to visualize and organize correlation coefficient estimates. It does not seem that the author of the package has any interest in p-value estimation. If you are interested in computing p-values, I would suggest using a different tool, such as the ...

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This is non-parametric statistics, the mean requires confirmation to the normal distribution (but there are exceptions), or perhaps better put it requires a fixed relationship between mean and standard deviation. Non-parametric stats always use the median and are distribution free. Wilcoxon test/ Mann-Whitney U test are used to replace t-test. The Kruscal-...

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The first approach only address the question of how likely are you to end up with the observed over-representation given the MAF distribution. My suggestion is to use the second approach, but I am not sure if you would call it bootstrap. Bootstrap in general means sampling with replacement to estimate the uncertainty of a parameter, in this case, OR. So even ...

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If we look at documentation of DESEQ2 and search "adjusted p-value", we find the section "Multiple test correction". In this section they discuss Benjamini-Hochberg ("BH") false discovery rate (FDR) correction procedures: FDR/Benjamini-Hochberg: Benjamini and Hochberg (1995) defined the concept of FDR and created an algorithm ...

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Another viewpoint could be : statistically significant implies use of sampling theory to establish presence of significant difference between case and control groups. The "clinical " significance seems to test for correctness or validity of measures of variables. Apparenty, the measurement theory is meant for checking validity of data - the difference ...

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I found that using the Wald test instead of the Likelihood Ratio Test produced very different results with respect to the p-values. sleuth_obj = sleuth_prep( metadata, extra_bootstrap_summary = TRUE, read_bootstrap_tpm = TRUE, num_cores = 1, target_mapping = transcripts_genes, aggregation_column = "ens_gene" ) # same model of ...

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Try the protocol here for generating a different (smaller) dispersion estimate https://www.huber.embl.de/users/klaus/Teaching/DESeq2Predoc2014.html#inspection-and-correction-of-pvalues

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What is the proper method to do this (if possible)? Please use dedicated software for RNA-seq and microarray analysis and not naive tests such as the t-test. For microarray one commonly uses limma and for RNA-seq either DESeq2, edgeR or limma-voom, all are available as Bioconductor packages in R.

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Well, here my thoughts: The equation $s$ somehow tries to represent the ratio $\frac{equality}{equality+inequality}$. But $s$ returns only a value. And to assing the p-value to a single value... is complicated. How could you approach this? Using some kind of Hamming distance for each base pair at the position $n$ ($1 \leqslant n \leqslant len$) between the ...

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Something like this book chapter might help on understanding FDR. The packages or functions you call differ in their estimation of pi0 or the proportion of null hypothesis. Basically, the p.adjust(..method = "BH") is a more conservative method with Benjamini-Hochberg, you are assuming the proportion of null features to be 1. When you use fdrtool, ...

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Here is a suggested workaround: Generate a p-value table using one of these tools: RcmdrMisc::rcorr.adjust() psych::corr.test() Then use corrr:as_cordf() to generate the tidy dataframe with the same structure as a regular corrr correlation table. Personally, I have used the Hmisc::rcorr() package, and it works well: Hmisc::rcorr() %>% [[('P') %>% ...

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The p-values are not very very significant, so the adj. p-value. You need to plot the gene counts and see why it is the case. It could be because they are captured/expressed only in very very few cells. VlnPlot or FeaturePlot functions should help. You haven't shown the TSNE/UMAP plots of the two clusters, so its hard to comment more.

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I would start by re-formulating the question in this post: Your end-goal seems to the differential expression analysis (DEA) of your samples (and you have to have to levels for this like treatment-control, mutant-WT, etc ...). P-values are context-dependent and should be calculated based on your research question/hypothesis. DEA has been addressed by many ...

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