Progenesis qi

The transcriptomic datasets were processed using Expression and Transcriptome Analysis Consoles (v.4.0.1.36; both from Affymetrix) and SigmaStat (v.3.5, from Systat Software, Inc., San Jose, CA, USA). The default (and currently the industry standard) filter criteria: (1) (+2) < LFC < (−2), and (2) ANOVA p-value (condition pair) ≤0.05, were used to analyze the data. A tighter LFC of >(+1.2) and <(−1.2), as proposed in [34 (link)], was also tested, but deemed impractical because of an unrealistically high number of samples needed to satisfy statistical criteria (see Discussion).
The RP-UPLC/MS data were analyzed using MassLynx (v.4.1), MSe Data Viewer (v.1.4), and Progenesis QI software packages (from Waters). A Supplemental Table S1 lists major lipids of human meibum relevant to this study, and their corresponding m/z values. SigmaStat and SigmaPlot software packages from Systat Software, Inc. were used to conduct statistical evaluation of the data.
The transcriptomic and lipidomic data for two genders were compared gender-wide using Student’s t-test for the two groups. Tests with p-values ≤ 0.05 were considered statistically significant. Principal component analyses were performed using Transcriptome Analysis Console, Progenesis QI, and EZInfo (v.3.0.3.0 from Umetrics AB, Umeå, Sweden).

Data acquisition was carried out with MassLynx (V4.1) and DriftScope (V2.8) software. The total arrival time distribution (ATD) files classified as “Aβ40” and “Aβ40 plus Porph” were thus exported from DriftScope (V2.8) to Progenesis QI (64-bit, Nonlinear Dynamics). The Progenesis QI data analysis software is a small molecule discovery tool predominantly used to identify the significantly changing compounds in your dataset. In this particular case, the software was used for drift time alignment, peak picking, and normalization using total ion intensity. We obtained three data matrices, one for each of the investigated data set. Multiple features with same drift time and different m/z may belong to the same compound due to the fragmentation, adduct formation, or clustering. The three data matrices were then exported from Progenesis QI to the statistical package EZinfo (V3.0.1.0, Umetrics). This was used to build 2-class orthogonal projection to latent structure-discriminant (OPLS-DA) models and S-plots for each sample set under investigation. Protein Prospector V5.22.1 (UCSF Mass Spectrometry Facility) and Fragment Ion Calculator (ISB Data Access Server) were used to analyze the MS/MS fragmentation ions from peptides.

Statistical analysis was performed using Progenesis QI and the EZ info plugin for Progenesis QI developed by Umetrics. p-values, %CV and fold-changes associated with the statistical comparison of experimental groups were calculated for each metabolite using Progenesis QI. These data were used to identify and evaluate potential biomarkers. The identified metabolites were sorted, by three different methods, according to the maximum fold-change between: i) healthy volunteers and IDH1 positive ICC patients, ii) healthy volunteers and iii) IDH1 negative ICC patients and IDH1 negative and IDH1 positive ICC patients. Difference in metabolite abundance, which was statistically significant (p < 0.05), were mapped onto metabolic pathways in a heat-map format for visualisation.