But for technical replicates, normalization factors across several libraries can be calculated by selecting one sample as a reference and calculating the IMM factor for each non-reference library, and then obtain a scale vector IMM 0 = (IMM 01 r, IMM 02 r, …, IMM 0 k r ).
Technical replication and normalization of data are well-validated approaches to deal with variability in other genomic methodologies, such as microarray transcript expression profiling[20].
Following normalization, technical outlier samples were identified and excluded using a model-based quality control assessment that included relative log2 expression (RLE) measures, normalized un-scaled standard errors (NUSE), array pseudo-images, and the percent of probe sets called "present".
Given that mass spectra are prone to variability in ionization efficiency, we applied a standardized method of analyzing serum directly with MALDI-TOF MS, using averaging of technical replicates and normalization to a measure of total ionization efficiency by the laser.
The table demonstrates the degree of variability between technical replicates after normalization.
The qPCR and microarray analysis have inherent technical and data normalization challenges that can lead to variability in results.
The relative expression levels were calculated from three biological replicates, each measured in technical quadruplicate, after normalization to GAPC2 [ 51].
TSPs have two main advantages over many standard classifiers used in gene expression studies: (i) a TSP is based on only two genes, which leads to easily interpretable and inexpensive diagnostic tests and (ii) TSP classifiers are based on gene rankings, so they are more robust to variation in technical factors or normalization than classifiers based on expression levels of individual genes.
Major technical comment: The normalization of datasets across different samples (here embryonic stages and wildtype versus Zelda-depleted embryos) is an important issue, especially when the samples are expected to strongly differ in the strengths or even the presence of the studied features.
We normalized this technical effect using lowess normalization (Fig EV1B).
