Thursday, May 15, 2014

In situ, in vitro and other confusing expressions: explained with examples

Ok, biologists love using these Latin expressions, but somehow I keep forgetting what they mean and wikipedia entries only add more confusion. Additionally they can also mean different things depending on the context. In my humble opinion, scientific language should be explicit and clear, but not ambiguous. But who am I to judge, right? ;)

Here are the explanations with examples in the area of genomics:

in vivo
In a living thing. Usually refers to an experiment which is performed in an animal with analysis of the observed phenotypes. For example, survival outcome of a gene knockout in a genetically modified mouse.

ex vivo
Out of a living thing. We assume extracting some tissues or cells from a living organism. Example: tumor samples extracted from a cancer patient.

in situ
In position, in a system. Analysis of a biological system without disrupting it. For example, we can analyse gene expression in a cell without extracting the RNA by using hybridization probes and mircoscopy.

in vitro
In a lab tube. An artificial extraction and analysis of biological material. The most common example: DNA extracted from cell cultures.

in silico
In a computer simulation. At least this one is clear :)

Luckily, in my lab I have some nice biologists to help me understand these things. Anyway, corrections and suggestions are welcome!

Update 1:

Hey, there is one I missed - ex situ. It means "out of natural system". An example from ecology/conservation biology: an animal moved from the natural habitat to the zoo/nature reserve. Never encountered this in molecular biology, but one can guess as example some cells transplanted from one organism to another. Thanks to Arkady Ukrop for this one.

Tuesday, May 6, 2014

Learning statistics: Simpsons paradox

Although I had a couple of nice courses in the university, I still feel myself not very confident when going through the statistics analysis part in a genomics paper. Apparently, it is not enough to know algorithms and biology to become a bionformatician :) After realizing (finally!) that statistics is super important for science and especially for data analysis, I started taking some MOOCs (this,this and this for example ) to refresh and improve my knowledge. As a result I am learning a lot of cool new things now! :)

Today I came across something interesting in the Exploratory Data Analysis course: Simpson's paradox.

Somehow I never heard about it before or just did not keep attention to it. The idea is the following: main trend or correlation is different for distinct groups in the dataset compared to the dataset as a whole. Such effect is usually introduced by some unrecognized confounding factor in the data.

Nice visualization and explanation can be found here.