Examining the practical limits of batch effect-correction algorithms: When should you care about batch effects?

Batch effects are technical sources of variation and can confound analysis. While many performance ranking exercises have been conducted to establish the best batch effect-correction algorithm (BECA), we hold the viewpoint that the notion of best is context-dependent. Moreover, alternative questions beyond the simplistic notion of "best" are also interesting: are BECAs robust against various degrees of confounding and if so, what is the limit? Using two different methods for simulating class (phenotype) and batch effects and taking various representative datasets across both genomics (RNA-Seq) and proteomics platforms, we demonstrate that under situations where sample classes and batch factors are moderately confounded, most BECAs are remarkably robust and only weakly affected by upstream normalization procedures.

Proteomic investigation of intra-tumor heterogeneity using network-based contextualization - A case study on prostate cancer.

Cancer is a heterogeneous disease, confounding the identification of relevant markers and drug targets. Network-based analysis is robust against noise, potentially offering a promising approach towards biomarker identification. We describe here the application of two network-based methods, qPSP (Quantitative Proteomics Signature Profiling) and PFSNet (Paired Fuzzy SubNetworks), in an intra-tissue proteome data set of prostate tissue samples.

Deeper investigation into the utility of functional class scoring in missing protein prediction from proteomics data.

Functional Class Scoring (FCS) is a network-based approach previously demonstrated to be powerful in missing protein prediction (MPP). We update its performance evaluation using data derived from new proteomics technology (SWATH) and also checked for reproducibility using two independent datasets profiling kidney tissue proteome. We also evaluated the objectivity of the FCS -value, and followed up on the value of MPP from predicted complexes.

Toward a single-layer two-dimensional honeycomb supramolecular organic framework in water.

The self-assembly of well-defined 2D supramolecular polymers in solution has been a challenge in supramolecular chemistry. We have designed and synthesized a rigid stacking-forbidden 1,3,5-triphenylbenzene compound that bears three 4,4'-bipyridin-1-ium (BP) units on the peripheral benzene rings. Three hydrophilic bis(2-hydroxyethyl)carbamoyl groups are introduced to the central benzene ring to suppress 1D stacking of the triangular backbone and to ensure solubility in water.