•SEM and bootstrapping cluster analysis offer different types of insights.•Simulated SEM and bootstrapping cluster simulations are both useful in validating scales.•Bootstrapping cluster simulations ...aid in validating constructs in smaller, moderately correlated data sets.•Bootstrapping cluster simulations validate scales visually with probability estimates.
The scale development paradigm was created to improve the measurement of latent constructs. Although several statistical techniques have been successfully integrated into the overall process, identifying factor patterns and validating constructs using smaller datasets with different correlational structures remain a concern. This paper presents heatmapping and bootstrapping cluster analysis (HMBCA), a novel machine-learning based diagnostic workflow, as a new tool to aid in strengthening the process. A substantive example on the overall organizational knowledge acquisition behaviors demonstrates that the bootstrapping cluster simulation approach provided promising results regarding the factor structure as measured by the Approximately Unbiased (AU) p-values under the following conditions: when factor correlations are weaker or moderate, with simulated data containing smaller samples. The study suggests that researchers may leverage bootstrapping cluster simulations to validate constructs through both visual inspection and probability estimates when faced with constraints such as a small sample size.
•An improved cross reactivity prediction method.•We search all HLA:0201 crystal structures and mapping contact region between TCR/p:MHC.•We use potential electrostatic and surface area accessible as ...input data.•Successful predicted cross reactivity in dengue and hepatitis C targets.•This promising method can originate a cross reactivity tool.
Cytotoxic T-lymphocytes (CTLs) are the key players of adaptive cellular immunity, being able to identify and eliminate infected cells through the interaction with peptide-loaded major histocompatibility complexes class I (pMHC-I). Despite the high specificity of this interaction, a given lymphocyte is actually able to recognize more than just one pMHC-I complex, a phenomenon referred as cross-reactivity. In the present work we describe the use of pMHC-I structural features as input for multivariate statistical methods, to perform standardized structure-based predictions of cross-reactivity among viral epitopes. Our improved approach was able to successfully identify cross-reactive targets among 28 naturally occurring hepatitis C virus (HCV) variants and among eight epitopes from the four dengue virus serotypes. In both cases, our results were supported by multiscale bootstrap resampling and by data from previously published in vitro experiments. The combined use of data from charges and accessible surface area (ASA) of selected residues over the pMHC-I surface provided a powerful way of assessing the structural features involved in triggering cross-reactive responses. Moreover, the use of an R package (pvclust) for assessing the uncertainty in the hierarchical cluster analysis provided a statistical support for the interpretation of results. Taken together, these methods can be applied to vaccine design, both for the selection of candidates capable of inducing immunity against different targets, or to identify epitopes that could trigger undesired immunological responses.
