![]() Mann-Whitney for comparing two unpaired groups) and parametric test (e.g. Choose a proper statistical test before you start your analysis Choosing between a rank based (non-parametric) (e.g. If not, there is a big chance that you may miss a significant effect, as the pairing will help to cancel out sample specific differences. If there is pairing information in your data set, then you should use a statistical test that takes this information into account. Typical examples are gene expression levels measured on different cell lines treated with a compound or vehicle control, or measurements on mice before and after an intervention. Consider pairing Paired information means that values in one group are related to the values in the other group. Log transformation makes your data more symmetrical and therefore, a parametric statistical test will provide you with a more accurate and relevant answer. Upon log transformation (I use base 10 here, but any base will do), the distance between A and B, and between B and C becomes equal (1 log10 unit, as the log10 values of A, B, and C are -1, 0 and 1) (figure above, right panel). A parametric statistical test will therefore be biased and not appreciate that A and C are equally different from B. However, in linear scale A and B are much closer (similar) to each other than B and C (0.9 units versus 9 units). Intuitively, we understand that sample A has a ten-fold lower expression compared to sample B, and that C has a ten-fold higher expression compared to B. Consider the case where the normalized expression levels are 0.1 (A), 1 (B) and 10 (C) for 3 samples (A-C) under study (figure above, left panel). Always log transform your gene expression data Gene expression levels are heavily skewed in linear scale: half of the data-point (the lower expressed genes) are between 0 and 1 (with 1 meaning no change), and the other half (the higher expressed genes) between 1 and positive infinity. #Clc sequence viewer color code selected residue full#The full text pre-print of the book chapter qPCR data analysis – unlocking the secret to successful results is available on the qbase+ website. Some of this information is also available in a book chapter that Jan Hellemans and I wrote for "PCR Troubleshooting and Optimization: The Essential Guide”. Instead, I would like to focus on seven very simple but fundamental principles for doing bio-statistics yourself, especially when you’re handling gene expression data. Obviously, this blog does not aim to serve as a crash course on statistics. The corresponding article on Nature Method’s methagora blog holds a continuously updated list of the Points of Significance articles. In August of 2013, Nature Methods has initiated a new column 'Points of Significance’ devoted to statistics. If you always felt the need to sharpen your basic bio-statistics skills, then this book may be really something for you. I particularly appreciate the book as it really is intuitive it almost reads like a novel, and you could read it in bed, next to the fireplace with a glass of your favorite wine, or even when you’re on holidays. This excellent book is written by an author who graduated from medical school this probably explains why it contains only the most pertinent formulas. Later, I almost really fell in love with statistics after reading Intuitive biostatistics by Harvey Motulsky. ![]() Only when I generated my first data during my PhD research, I started realizing the necessity and power of bio-statistics. Too theoretical, didn’t see the utility of it. Personally, I didn’t like statistics (at all) during my masters degree education.
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