In my last blog I described the problem of missing values in discovery omics analysis and how it adversely affects the statistics. Now I’ll describe the Progenesis co-detection solution to this problem.
First, a quick recap: the problem is caused by an inefficient workflow in which the feature ion signals are detected independently on each sample. This creates different detection patterns, even for technical replicates (same sample run multiple times), so that matching the ions to ensure you are comparing ‘like with like’ across all samples becomes very difficult. This leads to the generation of many “missing values” in the ion quantity matrix. Multivariate statistical analysis is then performed on the ion quantity matrix, in order to find the truly significant expression changes. Actually, the impact of having missing values in the ion quantity matrix means that it is not possible to do a ‘like with like’ comparison on many features.
This means the multivariate statistics have to be applied to a restricted number of features, consequently false positives and false negatives are generated through the applied multivariate analysis. We examined the consequences of missing values in more detail in our blog post: Missing Values: The hard truths.
Progenesis however, takes an alternative unique approach to data extraction in which ion signals are essentially “matched” before detection takes place by aligning the pixel patterns of the 2D ion maps (see figure below). This compensates for any retention time differences between samples. The pre-matched ions can then be co-detected so that a single detection pattern is created for all the samples in the experiment, resulting in 100% matching of ions and no missing values!
Here is a schematic of how Progenesis QI works:
How does this approach help?
Well, let’s consider a comparison of two very similar samples from a small discovery omics experiment.
In addition to the above benefits, co-detection also increases sensitivity and reliability of ion detection by increasing the signal to noise ratio. Even with co-detection of just two samples, we can see this in the detection of 25 (=154-129) ions that were not detected in either of the samples individually. As we co-detect from more samples, very faint and/or fragmented signals that cannot be reliably detected on individual samples but are consistently present, will become more distinct and easily detected from the aggregated data.
Progenesis co-detection in action
Finally, let’s take a look at how the Progenesis co-detection workflow helps us to easily extract powerful statistical information from a 3 Vs 3 experiment that includes the two samples we’ve already looked at. The figure below shows quantitative data for two different ions extracted from the experiment, one in which a significant expression change is detected and another in which no change is detected. The figure also illustrates another powerful benefit of the co-detection workflow – the ability to visually confirm expression change results (p-values and fold changes) at the “raw data” level, a great way to increase confidence in your results!
So, there you have it. The unique Progenesis QI workflow really does eliminate missing values at the analysis stage.
Would you like to try Progenesis QI on ALL your data? Download now and complete your analysis with confidence.