This post provides an in-depth understanding of False Discovery Rate (FDR) and the Benjamini-Hochberg Method, crucial in statistical analysis with large datasets like genomics. It explains FDR's role in identifying false positives in multiple hypothesis testing and the Benjamini-Hochberg Method's effectiveness in controlling FDR. The post compares various p-value adjustment methods, discussing their advantages, limitations, and suitability for different data types. It emphasizes the BH method's balance in statistical power and error control, and its integration in software like R, highlighting its applicability across scientific fields.

The Fruchterman-Reingold layout is a force-directed layout algorithm, which treats edges like springs that move vertices closer or further from each other in an attempt to find an equilibrium that minimizes the 'energy' of the system. © Bernie Hogan

When we compare our control with another group, the t-test could fit our goal very well. But when we need to compare it into more than 1 group, 3 groups for example, the t-test could only give the random false positive independently. That means three false positives in three comparisons. This would cause a problem. Because the false positive we need to consider now is at least one false positive in three comparisons. It means we need to adjust the p-value and apply a rigorous method to achieve a more reliable result. For doing that, ANOVA was introduced and applied.

NMF: Non-negtive matrix factorization

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Unsupervised Machine Learning in Python (DBSCAN; UMAP, t-SNE, etc)

A quick guide for seaborn plot in python

What is Negative binomial and binomial distribution. $b(x; r, P) = C_{x-1}^{r-1} imes P^r imes (1 - P)^{x - r}$

What is binomial and binomial distribution. $b(x; n, P) = C_{n}^{x} * P^ x (1 - P)^{(n - x)}$

Plot the density distribution of your data by ggplot

Odds of an event happening is defined as the likelihood that an event will occur, expressed as a proportion of the like...

Correlation is a statistical measure that shows how closely two variables are related to each other. We are interested in correlation because it helps us understand the relationship between two variables, make better decisions and predictions, and is used in various fields like finance, economics, psychology, and biology. Who said this?

Tricks for Data cleaning in R

Few ways to normalizing your date, reducing batch effect...