When performing LC-MS quantitation, there are numerous sources of experimental variance that can confound the quality of your results (variation in the starting amount of sample, variation in the LC-MS measurements, etc.). Having a robust normalization strategy that...
In an LC-MS experiment there are multiple sources of variance that can confound the quality of your results. This variation can be biological e.g. differences between treated and control groups, but can also be non-biological, usually from small variations in...
Did you know that MarkerView software can pull information directly from the metadata of your sample datafiles that can help with downstream data processing? To facilitate this, when building your batch in Analyst software, make sure to define your experimental groups...
A t-test (sometimes called Student’s t-test) is used to determine if the means of two sample groups are significantly different. So, for LC-MS data, we would typically use a t-test to find out if the amount of our variable of interest (protein, peptide, small...
In our previous post we discussed how to interpret the Scores and Loadings Plots produced by PCA. In many cases there are several dimensions that are found to have substantial influence over a specific principal component, and these dimensions are often correlated in...
Principal component analysis (PCA) is a data transformation technique that can be used to reduce the complexity of high-dimensional data sets (such as mass spectrometry data) while grouping samples based on common features. REMINDER! Principal components are linear...
When we measure anything, the resulting measurements are often referred to as variables or dimensions. For example, a cube has three dimensions that can be measured: length, width and height. When we measure a complex sample using mass spectrometry (like a proteome,...