Statistical Analysis of Root Count Data from Minirhizotrons

2015 ◽  
pp. 81-87
Author(s):  
D. M. Glenn ◽  
M. W. Brown ◽  
F. Takeda
Keyword(s):  
Statistical Analysis ◽  
Count Data

Statistical analysis of the item-count technique using Stata

2019 ◽  
Vol 19 (2) ◽  
pp. 390-434
Author(s):  
Chi-lin Tsai
Keyword(s):  
Data Analysis ◽  
Statistical Analysis ◽  
Count Data ◽  
Profile Likelihood ◽  
Likelihood Estimation ◽  
Great Flexibility ◽  
Recent Developments ◽  
List Experiment ◽  
Experiment Technique ◽  
Item Count Technique

In this article, I review recent developments of the item-count technique (also known as the unmatched-count or list-experiment technique) and introduce a new package, kict, for statistical analysis of the item-count data. This package contains four commands: kict deff performs a diagnostic test to detect the violation of an assumption underlying the item-count technique. kict ls and kict ml perform least-squares estimation and maximum likelihood estimation, respectively. Each encompasses a number of estimators, offering great flexibility for data analysis. kict pfci is a postestimation command for producing confidence intervals with better coverage based on profile likelihood. The development of the item-count technique is still ongoing. I will continue to update the kict package accordingly.

scholarly journals Statistical Analysis of Root Count Data

HortScience ◽  
1995 ◽  
Vol 30 (4) ◽  
pp. 907A-907
Author(s):  
D. Michael Glenn
Keyword(s):  
Statistical Analysis ◽  
Analysis Of Variance ◽  
Power Law ◽  
Count Data ◽  
Root Systems ◽  
Trial And Error ◽  
Taylor’S Power Law ◽  
First Approximation ◽  
Taylor's Power Law ◽  
The Mean

The minirhizotron approach for studying the dynamics of root systems is gaining acceptance; however, problems have arisen in the analysis of data. The purposes of this study were to determine if analysis of variance (ANOVA) was appropriate for root count data, and to evaluate transformation procedures to utilize ANOVA. In peach, apple, and strawberry root count data, the variance of treatment means was positively correlated with the mean, violating assumptions of ANOVA. A transformation based on Taylor's power law as a first approximation, followed by a trial and error approach, developed transformations that reduced the correlation of variance and mean.

scholarly journals Application of Spectral Density/Periodogram Analysis to Serial Neutrophil Counts to Diagnose Cyclic Neutropenia

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 4608-4608
Author(s):  
Nicholas J. Dobbins ◽  
Audrey Anna Bolyard ◽  
Robert T. Chang ◽  
Julian Self ◽  
Gabriel Provencher Langlois ◽  
...  
Keyword(s):  
At Risk ◽  
Statistical Analysis ◽  
Clinical Diagnosis ◽  
Count Data ◽  
Severe Congenital Neutropenia ◽  
Congenital Neutropenia ◽  
Simple Method ◽  
Cyclic Neutropenia ◽  
Serial Blood ◽  
Periodogram Analysis

Abstract Background: Cyclic neutropenia is characterized by oscillatory fluctuations in blood neutrophil counts, usually with nadirs <0.2 x 109/L at approximately 3 week intervals. Visual inspection of graphs of serial counts is usually the basis for diagnosis. Detection of mutations in ELANE is helpful but not diagnostic because of the overlap of the specific mutation patterns with those associated with severe congenital neutropenia. Making the correct diagnosis of cyclic neutropenia is important because these patients are not thought to be at risk of developing myelodysplasia or acute myeloid leukemia (MDS/AML). In contrast, patients with severe congenital neutropenia, whose counts are usually lower, are at risk of developing MDS/AML. Methods: We have implemented a website application for easy and direct data entry of serial blood counts to detect statistically significant periodicities using the Lomb periodogram. Physicians, nurses, other healthcare providers or patients can directly enter the blood count data for analysis on a website to allow immediate visualization of the serial counts and calculation of the probability of statistically significant cycling and the period, i.e., length of the cycle. Results: We have analyzed the counts from 42 patients (21 ELANE positive, 8 ELANE negative, 13 ELANE unknown) enrolled in the Severe Chronic Neutropenia International Registry with a clinical diagnosis of cyclic neutropenia to determine the accuracy of clinical diagnoses based on this form of statistical analysis. Our preliminary results showed that it is easy to learn how to use this program. We estimate that at least 20 counts obtained at 2-3 day intervals for 6 weeks are the minimum needed to detect cyclic neutropenia on a statistically sound basis, while 20-40 counts obtained at 2-3 day intervals over an 8-10 week period was more likely to yield statistical and clinical certainty about the diagnosis. The figure below shows readouts for the periodogram analysis for one patient. It shows the influence of 17 counts versus 31 counts for a patient with the clinical diagnosis of cyclic neutropenia and a mutation in ELANE. The confidence intervals (95%) and (99%) are exceeded for the series of 31 counts but not for the shorter series. The peak, approximate cycle length is 22 days for this series of counts. As of yet, we do not have the sufficient daily count data to determine if more frequent testing (e.g. daily testing) is better than testing every 2-3 days. We are currently testing the patterns of neutrophil fluctuations in patients on G-CSF to see if cyclic neutropenia can be diagnosed in patients that are on (or during) treatment. We have learned that many patients with the clinical diagnosis of CyN do not have sufficient serial blood cell count data to confirm this diagnosis on a statistical basis. Conclusion: We have developed a simple method for making periodogram analysis much more widely available to clinicians and patients on a world-wide basis. Statistical analysis of carefully collected serial data will help to secure the diagnosis of cyclic neutropenia and provide patients with important prognostic information. Figure 1. Figure 1. Disclosures Dale: Amgen: Consultancy, Honoraria, Research Funding.

scholarly journals A Powerful Approach for Identification of Differentially Transcribed mRNA Isoforms

2014 ◽  
Author(s):  
Yuande Tan
Keyword(s):  
Statistical Analysis ◽  
Statistical Methods ◽  
Count Data ◽  
Differentially Expressed Gene ◽  
Small Sample ◽  
T Test ◽  
Test Method ◽  
Differentially Expressed ◽  
Next Generation ◽  
Mrna Isoforms

Next generation sequencing is being increasingly used for transcriptome-wide analysis of differential gene expression. The primary goal in profiling expression is to identify genes or RNA isoforms differentially expressed between specific conditions. Yet, the next generation sequence-based count data are essentially different from the microarray data that are continuous type, therefore, the statistical methods developed well over the last decades cannot be applicable. For this reason, a variety of new statistical methods based on count data of transcript reads has been correspondingly developed. But currently the transcriptomic count data coming only from a few replicate libraries have high technical noise and small sample size bias, performances of these new methods are not desirable. We here developed a new statistical method specifically applicable to small sample count data called mBeta t-test for identifying differentially expressed gene or isoforms on the basis of the Beta t-test. The results obtained from simulated and real data showed that the mBeta t-test method significantly outperformed the existing statistical methods in all given scenarios. Findings of our method were validated by qRT-PCR experiments. The mBeta t-test method significantly reduced true false discoveries in differentially expressed genes or isoforms so that it had high work efficiencies in all given scenarios. In addition, the mBeta t-test method showed high stability in performance of statistical analysis and in estimation of FDR. These strongly suggests that our mBeta t-test method would offer us a creditable and reliable result of statistical analysis in practice.

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