Tests for Rank Correlation Coefficients. I

Fieller, E. C.; Hartley, H. O.; Pearson, E. S.

doi:10.1093/biomet/44.3-4.470

Cited by 315 publications

(155 citation statements)

References 3 publications

Supporting

Mentioning

149

Contrasting

Unclassified

Order By: Relevance

“…The lowest mean GR (24.8%) was recorded in 2014 at Wenjiang, whereas the highest mean GR (60.8%) was recorded in 2012 at Ya’an (Supplementary Table S3). The GR was highly correlated across environments ( r = 0.54–0.80) based on the two-tailed Pearson product-moment correlation coefficient test (Fieller et al, 1957) ( Table 1 ). A total of 194 landraces exhibited a mean GR < 20.0%, of which 23 landraces displayed consistently dormant phenotypes across all six environments (Supplementary Table S2).…”

Section: Resultsmentioning

confidence: 99%

“…One-Way Analysis of variance (ANOVA) of GR for accessions derived from the 10 Chinese wheat-growing zones, which was performed by Duncan’s multiple test in each phenotyping tested environment. Two-tailed Pearson product-moment correlation coefficient tests were also carried out for GR obtained across the six environments (Fieller et al, 1957). …”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces

et al. 2017

View full text Add to dashboard Cite

Pre-harvest sprouting (PHS) is mainly caused by the breaking of seed dormancy in high rainfall regions, which leads to huge economic losses in wheat. In this study, we evaluated 717 Chinese wheat landraces for PHS resistance and carried out genome-wide association studies (GWAS) using to 9,740 DArT-seq and 178,803 SNP markers. Landraces were grown across six environments in China and germination testing of harvest-ripe grain was used to calculate the germination rate (GR) for each accession at each site. GR was highly correlated across all environments. A large number of landraces (194) displayed high levels of PHS resistance (i.e., mean GR < 0.20), which included nine white-grained accessions. Overall, white-grained accessions displayed a significantly higher mean GR (42.7–79.6%) compared to red-grained accessions (19.1–56.0%) across the six environments. Landraces from mesic growing zones in southern China showed higher levels of PHS resistance than those sourced from xeric areas in northern and north-western China. Three main quantitative trait loci (QTL) were detected by GWAS: one on 5D that appeared to be novel and two co-located with the grain color transcription factor Tamyb10 on 3A and 3D. An additional 32 grain color related QTL (GCR-QTL) were detected when the set of red-grained landraces were analyzed separately. GCR-QTL occurred at high frequencies in the red-grained accessions and a strong correlation was observed between the number of GCR-QTL and GR (R2 = 0.62). These additional factors could be critical for maintaining high levels of PHS resistance and represent targets for introgression into white-grained wheat cultivars. Further, investigation of the origin of haplotypes associated with the three main QTL revealed that favorable haplotypes for PHS resistance were more common in accessions from higher rainfall zones in China. Thus, a combination of natural and artificial selection likely resulted in landraces incorporating PHS resistance in China.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces

et al. 2017

View full text Add to dashboard Cite

show abstract

“…In this context, the Fisher z-transformation function serves as a normalizing transformation. The variance of the resulting z-scores depends on whether the sample correlation coefficient is the Pearson product-moment correlation coefficient (r p ) or the Spearman’s rank correlation coefficient (r s ) [24]. When the underlying distribution is assumed to be bivariate normally distributed, the variance can be calculated bywhere n is the sample size of the calculated correlation.…”

Section: Methodsmentioning

confidence: 99%

“…Notably, the variance of the correlation coefficients in a particular condition could differ due to a different number of samples or due to missing data in one or both of the genes’ expression measurements. These equations for the variance have been found to be valid over a wide range of sample sizes that are common in current biological data sets [24]. Due to the denominator, the equations require that there are at least 4 samples in each condition considered.…”

Section: Methodsmentioning

confidence: 99%

DGCA: A comprehensive R package for Differential Gene Correlation Analysis

et al. 2016

View full text Add to dashboard Cite

BackgroundDissecting the regulatory relationships between genes is a critical step towards building accurate predictive models of biological systems. A powerful approach towards this end is to systematically study the differences in correlation between gene pairs in more than one distinct condition.ResultsIn this study we develop an R package, DGCA (for Differential Gene Correlation Analysis), which offers a suite of tools for computing and analyzing differential correlations between gene pairs across multiple conditions. To minimize parametric assumptions, DGCA computes empirical p-values via permutation testing. To understand differential correlations at a systems level, DGCA performs higher-order analyses such as measuring the average difference in correlation and multiscale clustering analysis of differential correlation networks. Through a simulation study, we show that the straightforward z-score based method that DGCA employs significantly outperforms the existing alternative methods for calculating differential correlation. Application of DGCA to the TCGA RNA-seq data in breast cancer not only identifies key changes in the regulatory relationships between TP53 and PTEN and their target genes in the presence of inactivating mutations, but also reveals an immune-related differential correlation module that is specific to triple negative breast cancer (TNBC).ConclusionsDGCA is an R package for systematically assessing the difference in gene-gene regulatory relationships under different conditions. This user-friendly, effective, and comprehensive software tool will greatly facilitate the application of differential correlation analysis in many biological studies and thus will help identification of novel signaling pathways, biomarkers, and targets in complex biological systems and diseases.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0349-1) contains supplementary material, which is available to authorized users.

show abstract

“…The non-parametric Mann-Whitney U test (31) as well as the Kruskal-Wallis test (32) were used to investigate significant differences between the patient groups. The Spearman's rank correlation coefficient test was performed for correlation analyses (33). Visualization of the qPCR data was performed using the GraphPad Prism 5.…”

Section: Methodsmentioning

confidence: 99%

AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients

Schneider

Christopoulos

Muley

et al. 2017

International Journal of Oncology

125

119

View full text Add to dashboard Cite

The growth of a tumor depends to a certain extent on an increase in mitotic events. Key steps during mitosis are the regulated assembly of the spindle apparatus and the separation of the sister chromatids. The microtubule-associated protein Aurora kinase A phosphorylates DLGAP5 in order to correctly segregate the chromatids. Its activity and recruitment to the spindle apparatus is regulated by TPX2. KIF11 and CKAP5 control the correct arrangement of the microtubules and prevent their degradation. In the present study, we investigated the role of these five molecules in non-small cell lung cancer (NSCLC). We analyzed the expression of the five genes in a large cohort of NSCLC patients (n=362) by quantitative real-time PCR. Each of the genes was highly overexpressed in the tumor tissues compared to corresponding normal lung tissue. The correlation of the expression of the individual genes depended on the histology. An increased expression of AURKA, DLGAP5, TPX2, KIF11 and CKAP5 was associated with poor overall survival (P=0.001–0.065). AURKA was a significant prognostic marker using multivariate analyses (P=0.006). Immunofluorescence studies demonstrated that the five mitosis-associated proteins co-localized with the spindle apparatus during cell division. Taken together, our data demonstrate that the expression of the mitosis-associated genes AURKA, DLGAP5, TPX2, KIF11 and CKAP5 is associated with the prognosis of NSCLC patients.

show abstract

Tests for Rank Correlation Coefficients. I

Cited by 315 publications

References 3 publications

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces

Genome-Wide Association Study for Pre-harvest Sprouting Resistance in a Large Germplasm Collection of Chinese Wheat Landraces

DGCA: A comprehensive R package for Differential Gene Correlation Analysis

AURKA, DLGAP5, TPX2, KIF11 and CKAP5: Five specific mitosis-associated genes correlate with poor prognosis for non-small cell lung cancer patients

Contact Info

Product

Resources

About