0000000000374793
AUTHOR
Paolo Rosati
Analysis of low-correlated spatial gene expression patterns: A clustering approach in the mouse brain data hosted in the Allen Brain Atlas
The Allen Brain Atlas (ABA) provides a similar gene expression dataset by genome-scale mapping of the C57BL/6J mouse brain. In this study, the authors describe a method to extract the spatial information of gene expression patterns across a set of 1047 genes. The genes were chosen from among the 4104 genes having the lowest Pearson correlation coefficient used to compare the expression patterns across voxels in a single hemisphere for available coronal and sagittal volumes. The set of genes analysed in this study is the one discarded in the article by Bohland et al. , which was considered to be of a lower consistency, not a reliable dataset. Following a normalisation task with a global and …
Birth-weight differences at term are explained by placental dysfunction and not by maternal ethnicity. Study in newborns of first generation immigrants.
The aim of the study was to investigate the influence of ethnicity and cerebroplacental ratio (CPR) on the birth weight (BW) of first generation Indo-Pakistan immigrants' newborns.This was a retrospective study in a mixed population of 620 term Caucasian and Indo-Pakistan pregnancies, evaluated in two reference hospitals of Spain and Italy. All fetuses underwent a scan and Doppler examination within two weeks of delivery. The influence of fetal gender, ethnicity, GA at delivery, CPR, maternal age, height, weight and parity on BW was evaluated by multivariable regression analysis.Newborns of first generation Indo-Pakistan immigrants were smaller than local Caucasian newborns (mean BW mean= 3…
Clustering of low-correlated spatial gene expression patterns in the mouse brain in the Allen Brain Atlas
In this paper, clustering techniques are applied to spatial gene expression patterns with a low genomic correlation between the sagittal and coronal projections. The data analysed here are hosted on an available public DB named ABA (Allen Brain Atlas). The results are compared to those obtained by Bohland et al. on the complementary dataset (high correlation values). We prove that, by analysing a reduced dataset,hence reducing the computational burden, we get the same accuracy in highlighting different neuroanatomical region.