This visualization highlights the utility of phenome-wide data sets to draw insights into complex relations across different types of variables.
My current work involves the use of genome-wide data sets to study fine-scale patterns of population structure in both Native Americans and Hispanic/Latino populations from throughout the Americas.
However, despite the phenomenal growth in sequence information available, the optimal way to employ genome-wide data sets to inform more clade-specific molecular phylogenetic studies remains elusive [ 2, 3] due to a lack of methods that quantitatively assess the power of genes to resolve particular nodes in a phylogeny.
Mining the numerous genome wide data sets related to protein function to retrieve potential candidate genes for a particular biological process remains a challenge.
Presented principles and methods are illustrated through application to a small, wide data set of adults spanning a wide range in ages and multiple immunophenotypes that were assayed before and after immunization with inactivated influenza vaccine (IIV).
Small, wide data sets are commonplace in human immunophenotyping research.
We offer a set of prescriptions that are designed to facilitate low-variance (i.e. stable), low-bias, interpretive regression modeling of small, wide data sets.
Comparative studies of genome wide data sets are entirely dependent on the choice of common denominator for annotation [ 40].
An effective way to initiate genomic-wide data sets in non-model organisms is to focus on the transcriptome, or expressed sequence, which, unlike a whole-genome approach, increases the data's focus on functional genomic attributes.
Here, computer simulation of data (using the Monte Carlo method) allows a much wider range of data sets to be explored quickly and efficiently.
There has been a recent surge of interest in Australia and internationally in using population-wide linked administrative data sets to better understand the factors that promote positive early childhood health and development 25 26 and to evaluate the impact of early childhood programmes, services and policy changes in the 'real world'.
