4

Permutation as suggested by @StupidWolf's comment is essential to understand what's going on. If permutation makes this pattern go away, then you have a problem with your model specification, there's something uncorrected. If your data are weird, well, that's just how they are. But this argues to me that something else is going on confounding your ...


3

To answer the first part of your question, the dependent and independent variable of X-WAS is kind of arbitrary and dependent on the question you asked. But it gradually becomes a convention in the field after the initial name and concept are accepted by the community. For example, GWAS from the very beginning is written as condition ~ SNP, and there is no ...


3

I don't know if this question has been solved already, but what they try to do is equalize the depth of sequencing for each cell. Therefore, they scale for the total number of reads. If you regress out (via linear or negative binomial regression) the differences in the number of reads per cell, you end up with cells that have been sequenced with the same ...


3

In general, survival analysis can be said to be composed of two steps; Cox regression, with which you calculate the "hazard ratio" based on your variables, and a "Kaplan-Meier (KM) estimate", which is used to visuazlize the data. Here is a nice tutorial for doing survival analysis with the survival and survminer packages. The latter includes ggplot2 kind ...


2

Seurat has as part of its protocol a step where you filter based on UMI counts and percent mitochondrial http://satijalab.org/seurat/pbmc3k_tutorial.html


2

Cleaning data before doing analysis can be more important than the method of data analysis. For UMIs, there's an obvious cleaning step that can be done: it would be better to filter out duplicates (prior to generating counts) than to try to incorporate that information in the count analysis. I'm not familiar with single cell analysis, but I am aware that ...


2

You can use either, but lmFit has the benefit of returning an object that can be used with eBayes() so you can pool information across genes/probes/whatever. lm() is a base R function applicable basically everywhere. lmFit() if from the limma package, so originally intended for microarray data, though these days pretty much everything omics is analyzed with ...


1

I do not know much about statistics but I will try my best to explain. First, random effects are defined as the factors (categories) in the population that we are not aware of (not observed), so we are randomly sampling levels of those factors when we sample the population. Practically speaking, random effects can be found when there are hierarchical ...


1

The hypothesis can be examined by t-test. You can group the data (number of catches) into dimmed light, and fully lit street lights and proceed with t-test to assess whether dimmed light results in less en-catchment or higher en-catchment or equivalent encatchment. Alternatively, a linear model can be applied subject to certain conditions here. PCA is ...


1

The residual is the level of error in a regression model, the lower the residual the better the model. Residuals cannot equate to a phenotype, it is actually the opposite if your regression model is trying to investigate a given genotype/phenotype(s) model. The residual in this case is the amount of infomation which the genotype/phentype does not account for,...


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