If you need a fancy but elegant calendar live in your terminal, khal would be an awesome choice.

It would be easy to count the result when we have only a few cells in an image. But once you got thousands of cells in an image and/or you got hundreds of repeats, the work would be tedious and laboring. But with the help of python, we can do more than sample counts and gray intensity calculation. We can apply more complicated techniques like Vironoi spacial calculation and Delaunay triangulation. I'll show how can we apply these two algorithms to finally determine whether cells may share boundaries or be physically contacted.

Make BioStatistics easy with R or Python

Python GAM to fit

Binomial and Poisson Distribution; Confidence intervals for proportions and counts; Contingency Tables; Survival Curve; Finally, Power and Sample Size

Basic knowledge and concepts for Artificial Neural Network

Tricks of data calculating

A quick way to 2ind the outline (edge) of the 2D points

R: ggplot, ggkaboom, barplot

geometry calculation: areas, distance, etc

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.

Quick Guide of settel your Pop OS!

Normal Distribution; CI calculate; t-table; p-value; T test; Independence and rejection of data; Propagation of errors

Notes for Genetics

Classes 13, Protein Translocation