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The other day, I saw this tweet:
Machine learning and bioinformatics tutorials these days pic.twitter.com/0FhWWG09TB — Ramon Massoni Badosa (@rmassonix) May 15, 2024 Many of the bioinformatics tutorials are like that. I am not saying the tutorial is not good. For beginners, we need something basic first to understand it.

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The problem df<- data.frame(id = c(1,2,3), value = c('x,y', 'z,w', 'a')) df #> id value #> 1 1 x,y #> 2 2 z,w #> 3 3 a we want to put x,y in the first row into two rows:
1, x
1, y
and put z,w into two rows too.
solution with R There is a neat function separate_rows that does exactly this in tidyr package:

In R, S3 and S4 objects are related to object-oriented programming (OOP), which allows you to create custom data structures with associated behaviors and methods. Let me explain them using simple language and metaphors, along with practical examples.
S3 Objects Imagine you have a collection of toys, like cars, dolls, and action figures. Each toy has its own set of properties (color, size, material) and behaviors (move, make sounds, etc.

In this post, we are going to try a CITE-seq normalization method called dsb published in Normalizing and denoising protein expression data from droplet-based single cell profiling
two major components of protein expression noise in droplet-based single cell experiments: (1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately estimated via the level of “ambient” ADT counts in empty droplets,
and (2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell.

I asked this question on Twitter:
what test to test if two distributions are different? I am aware of KS test. When n is large (which is common in genomic studies), the p-value is always significant. better to test against an effect size? how to do it in this context?
In genomics studies, it is very common to have large N (e.g., the number of introns, promoters in the genome, number of cells in the single-cell studies).

I asked this question on twitter.
load the package library(tidyverse) make some dummy data The dummy example: We have two groups of samples: disease and health. We treat those cells in vitro with different dosages (0, 1, 5) of a chemical X and count the cell number after 3 hours.
x <- tibble( '0' = c(8.66, 11.50, 7.01, 13.40, 11.30, 8.13, 5.92, 7.54), '1' = c(22.10, 23.00, 22.00, 35.70, 32.

I am taking this STATE-80 course from Harvard Extension School. This course teaches commonly used distributions and probability theory. The instructor Hatch is a really good teacher and he uses simulation for all the demonstrations along with the formulas.
In week 6, we revisited the Monty Hall problem which we played on the first day of class.
If you have not heard about it, I quoted from the wiki:
Suppose you’re on a game show, and you’re given the choice of three doors: Behind one door is a car; behind the others, goats.

I used to use cowplot to align multiple ggplot2 plots but when the x-axis are of different ranges, some extra work is needed to align the axis as well.
The other day I was reading a blog post by GuangChuang Yu and he exactly tackled this problem. His packages such as ChIPseeker, ClusterProfiler, ggtree are quite popular among the users.
Some dummy example from his post:
library(dplyr) library(ggplot2) library(ggstance) library(cowplot) # devtools::install_github("YuLab-SMU/treeio") # devtools::install_github("YuLab-SMU/ggtree") library(tidytree) library(ggtree) no_legend=theme(legend.

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