Objective: This study investigated maxillary skeletal structures and dentition shape and size in subjects with palatally impacted maxillary canines using geometric morphometric analysis. Methods: In this cross sectional study, cone-beam computed tomography images for 40 subjects (22 females, 18 males, mean age 16 years) with palatally impacted canines and 40 subjects (22 females, 18 males, mean age 17 years) with nondisplaced canines were included from a single radiology center. On cone-beam computed tomography images the nasal cavity, palate, sinus, alveolar crest, maxillary lateral walls, and dentition were landmarked by three examiners. Landmarked CBCT images were evaluated for shape using geometric morphometric analysis by performing procrustes superimpositions and principle component analysis. Shape differences were further investigated by using logistic regression and linear regression analyses. Results: Principle component analysis revealed subjects with palatally impacted maxillary canines had a nasal cavity that was more constricted, the palate was more deeply vaulted and constricted, the sinus extended less vertically, canines were wider both mesial-distally and buccal-palatally in relation to length, and the lateral incisors were more narrow both mesial-distally and buccal-palatally in relation to length. Logistic regression depicted five variables that were associated with palatally impacted maxillary canines: Maxillary basal width at the first molar area, palatal depth, canine centroid size, lateral incisor centroid size, and central incisor centroid size (p<.05). Conclusions: In palatally displaced maxillary canines, both the canine and lateral incisor size was diminished while the central incisor size was comparatively increased. The nasal cavity was constricted, the palate perimeter was constricted, the palate was more deeply vaulted, the sinus extended less vertically, and the basal maxillary width was constricted at the first molar area.

High-throughput sequencing technologies, such as the Illumina Genome Analyzer, are powerful new tools for investigating a wide range of biological and medical questions. Statistical and computational methods are key for drawing meaningful and accurate conclusions from the massive and complex datasets generated by the sequencers. We provide a detailed evaluation of statistical methods for normalization and differential expression (DE) analysis of Illumina transcriptome sequencing (mRNA-Seq) data.

Results:

We compare statistical methods for detecting genes that are significantly DE between two types of biological samples and find that there are substantial differences in how the test statistics handle low-count genes. We evaluate how DE results are affected by features of the sequencing platform, such as, varying gene lengths, base-calling calibration method (with and without phi X control lane), and flow-cell/library preparation effects. We investigate the impact of the read count normalization method on DE results and show that the standard approach of scaling by total lane counts (e.g., RPKM) can bias estimates of DE. We propose more general quantile-based normalization procedures and demonstrate an improvement in DE detection.

Conclusions:

Our results have significant practical and methodological implications for the design and analysis of mRNA-Seq experiments. They highlight the importance of appropriate statistical methods for normalization and DE inference, to account for features of the sequencing platform that could impact the accuracy of results. They also reveal the need for further research in the development of statistical and computational methods for mRNA-Seq.