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. This observation is consistently supported across the multitude of test datasets. BECAs do have limits: When sample classes and batch factors are strongly confounded, BECA performance declines, with variable performance in precision, recall and also batch correction. We also report that while conventional normalization methods have minimal impact on batch effect correction, they do not affect downstream statistical feature selection, and in strongly confounded scenarios, may even outperform BECAs. In other words, removing batch effects is no guarantee of optimal functional analysis. Overall, this study suggests that simplistic performance ranking exercises are quite trivial, and all BECAs are compromises in some context or another.National Research Foundation (NRF)Accepted versionWWBG gratefully acknowledges Limsoon Wong, National University of Singapore, for inspiring this work. WWBG gratefully acknowledges support from the National Research Foundation of Singapore, NRF-NSFC (Grant No. NRF2018NRF-NSFC003SB-006)

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 p-value, and followed up on the value of MPP from predicted complexes. Our results suggest that (1) FCS p -values are non-objective, and are confounded strongly by complex size, (2) best recovery performance do not necessarily lie at standard p -value cutoffs, (3) while predicted complexes may be used for augmenting MPP, they are inferior to real complexes, and are further confounded by issues relating to network coverage and quality and (4) moderate sized complexes of size 5 to 10 still exhibit considerable instability, we find that FCS works best with big complexes. While FCS is a powerful approach, blind reliance on its non-objective p -value is ill-advised

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. Despite high basal variation, we find that traditional statistical analysis may exaggerate the extent of heterogeneity. We also report that network-based analysis outperforms protein-based feature selection with concomitantly higher cross-validation accuracy. Overall, network-based analysis provides emergent signal that boosts sensitivity while retaining good precision. It is a potential means of circumventing heterogeneity for stable biomarker discovery.National Research Foundation (NRF)Accepted versionThis research was supported by a NRF-NSFC (Grant No. NRF2018NRF-NSFC003SB-006) to WWBG, the Westlake Startup Grant to TG, Zhejiang Provincial Natural Science Foundation of China (Grant No. LR19C050001) to TG, and a Kwan Im Thong Hood Cho Temple Chair Professorship to LW

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. Mixing the triangular preorganized molecule with cucurbit[8]­uril (CB[8]) in a 2:3 molar ratio in water leads to the formation of the first solution-phase single-layer 2D supramolecular organic framework, which is stabilized by the strong complexation of CB[8] with two BP units of adjacent molecules. The periodic honeycomb 2D framework has been characterized by various ¹H NMR spectroscopy, dynamic light scattering, X-ray diffraction and scattering, scanning probe and electron microscope techniques and by comparing with the self-assembled structures of the control systems

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. Mixing the triangular preorganized molecule with cucurbit[8]­uril (CB[8]) in a 2:3 molar ratio in water leads to the formation of the first solution-phase single-layer 2D supramolecular organic framework, which is stabilized by the strong complexation of CB[8] with two BP units of adjacent molecules. The periodic honeycomb 2D framework has been characterized by various ¹H NMR spectroscopy, dynamic light scattering, X-ray diffraction and scattering, scanning probe and electron microscope techniques and by comparing with the self-assembled structures of the control systems