4
votes
Accepted
Inflated p-values in quantitative trait analysis
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 ...
- 4,262
3
votes
Accepted
GWAS, MWAS, EWAS: what are the (in)dependent variables?
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 ...
- 887
3
votes
Accepted
Scaling by linear regression against the number of reads
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 ...
- 200
3
votes
Accepted
Doing plot with this data
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 ...
- 4,202
2
votes
Regressing out unwanted sources of variation in single cell RNA-seq data
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
- 2,061
2
votes
Regressing out unwanted sources of variation in single cell RNA-seq data
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 (...
- 15.1k
2
votes
Can I use a regular liner regression model when I'm working with DNA methylation data?
You can use either, but lmFit has the benefit of returning an object that can be used with eBayes() so you can pool information ...
- 19.8k
1
vote
Statistical approach to link DNA methylation with toxic element exposure and health outcome
Having given some thought to this, I'd still use machine learning. I would attempt to augment the negative controls however. Is there a suitable published data set? R program is caret but it high ...
M__♦
- 13k
1
vote
Accepted
How to get coefficient tables from multiple regression model result!
Well, I found a solution,
...
- 319
1
vote
How to get coefficient tables from multiple regression model result!
To get the coefficients alone
lm_model$coefficients
glm_model$coefficients
Everything including coefficients its,
...
M__♦
- 13k
1
vote
Advise on building an effect ML model for predicting important proteins for drug response
The missing values might be an issue indeed. You might want to use imputation methods, e.g.
...
- 113
1
vote
Determining significance of a variable in a glm model
Second viewing of the question from what I can see -0.22 as a coefficient of origin is a strong negative association, so yeah it has a major impact. Its not how I would have done it, but that looks to ...
M__♦
- 13k
1
vote
What is the difference between fixed effects and random effects in the context of Linear-mixed models?
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 ...
- 887
1
vote
How do I Investigate and test the hypothesis on the effect of street-light regime on insects?
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 ...
- 200
1
vote
Accepted
How are residuals used as new phenotype?
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 ...
M__♦
- 13k
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