On Determination of Minimum Sample Size for Discovery of Temporal Gene Expression Patterns

2006 ◽  
Author(s):  
Fang-Xiang Wu ◽  
W. J. Zhang ◽  
Anthony J. Kusalik
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Minimum Sample Size ◽  
Temporal Gene Expression ◽  
Minimum Sample

scholarly journals Determination of minimum sample size and discriminatory expression patterns in microarray data

2002 ◽  
Vol 18 (9) ◽  
pp. 1184-1193 ◽  
Author(s):  
D. Hwang ◽  
W. A. Schmitt ◽  
G. Stephanopoulos ◽  
G. Stephanopoulos
Keyword(s):  
Sample Size ◽  
Microarray Data ◽  
Expression Patterns ◽  
Minimum Sample Size ◽  
Minimum Sample

scholarly journals Methods and Rule-of-Thumbs in The Determination of Minimum Sample Size When Appling Structural Equation Modelling: A Review

2020 ◽  
Vol 15 ◽  
pp. 102-107
Author(s):  
Hunuwala Malawarage Suranjan Priyanath ◽  
Ranatunga RVSPK ◽  
Megama RGN
Keyword(s):  
Structural Equation Modeling ◽  
Sample Size ◽  
Structural Equation ◽  
Sample Size Determination ◽  
Equation Modeling ◽  
Minimum Sample Size ◽  
Research Project ◽  
Rules Of Thumb ◽  
Minimum Sample

Basic methods and techniques involved in the determination of minimum sample size at the use of Structural Equation Modeling (SEM) in a research project, is one of the crucial problems faced by researchers since there were some controversy among scholars regarding methods and rule-of-thumbs involved in the determination of minimum sample size when applying Structural Equation Modeling (SEM). Therefore, this paper attempts to make a review of the methods and rule-of-thumbs involved in the determination of sample size at the use of SEM in order to identify more suitable methods. The paper collected research articles related to the sample size determination for SEM and review the methods and rules-of-thumb employed by different scholars. The study found that a large number of methods and rules-of-thumb have been employed by different scholars. The paper evaluated the surface mechanism and rules-of-thumb of more than twelve previous methods that contained their own advantages and limitations. Finally, the study identified two methods that are more suitable in methodologically and technically which have identified by non-robust scholars who deeply addressed all the aspects of the techniques in the determination of minimum sample size for SEM analysis and thus, the prepare recommends these two methods to rectify the issue of the determination of minimum sample size when using SEM in a research project.

scholarly journals Dispersion analysis of PoTRA ranked mRNA mediated dysregulated pathways in Breast Invasive Cancer from a TCGA Pan-Cancer study

2018 ◽  
Author(s):  
Margaret K Linan ◽  
Valentin Dinu
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Gene Expression Data ◽  
Data Sets ◽  
Minimum Sample Size ◽  
Expression Data ◽  
Sampled Data ◽  
Average Rank ◽  
Minimum Sample ◽  
Google Search

Background. Our publication of the new pathways of topological rank analysis (PoTRA) algorithm demonstrated a novel approach for using the Google Search PageRank algorithm to analyze gene expression networks to identify biological pathways significantly disrupted in hepatocellular carcinoma. In order to apply the PoTRA algorithm to analyze other cancer gene expression data sets, of various sizes and normal:tumor ratio composition, two important questions must be answered: 1. What is the optimal normal:tumor sample ratio?; and 2. What is the minimum number of samples that should be used for PoTRA analysis? To address these questions, the average standard deviation (SD) in PoTRA-ranked mRNA mediated dysregulated pathways was studied using randomly sampled data sets with various normal:tumor ratios and sizes drawn from the TCGA Breast Invasive Carcinoma (TCGA-BRCA) project. Methods. To identify the optimal normal:tumor sample ratios, the SD analysis used random combinations of 1:N unbalanced normal:tumor data sets: (1:1, 1:2, 1:3, 1:5, 1:7, 1:9). To identify the minimum sample size, random resampling of normal and tumor samples of various sizes are used: (3 vs 3), (5 vs 5), (10 vs 10), (25 vs 25), (50 vs 50), (75 vs 75), (100 vs 100), and (113 vs 113). Results. This analysis suggests that the 1:1 ratio achieves the lowest average rank variation and that the minimum sample size of 50 normal and 50 tumor samples reaches a steady state in the average rank variation. Conclusion. In conclusion, future applications of the PoTRA algorithm to analyze gene expression data sets such as TCGA should use balanced data sets as well as a minimum sample size of 50 for both normal and tumor to ensure the most robust performance.

scholarly journals Optic nerve crush induces spatial and temporal gene expression patterns in retina and optic nerve of BALB/cJ mice

2014 ◽  
Vol 9 (1) ◽  
pp. 14 ◽  
Author(s):  
Tasneem P Sharma ◽  
Colleen M McDowell ◽  
Yang Liu ◽  
Alex H Wagner ◽  
David Thole ◽  
...  
Keyword(s):  
Gene Expression ◽  
Optic Nerve ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Optic Nerve Crush ◽  
Nerve Crush ◽  
Temporal Gene Expression

scholarly journals Dispersion analysis of PoTRA ranked mRNA mediated dysregulated pathways in Breast Invasive Cancer from a TCGA Pan-Cancer study

2018 ◽  
Author(s):  
Margaret K Linan ◽  
Valentin Dinu
Keyword(s):  
Gene Expression ◽  
Sample Size ◽  
Gene Expression Data ◽  
Data Sets ◽  
Minimum Sample Size ◽  
Expression Data ◽  
Sampled Data ◽  
Average Rank ◽  
Minimum Sample ◽  
Google Search

Background. Our publication of the new pathways of topological rank analysis (PoTRA) algorithm demonstrated a novel approach for using the Google Search PageRank algorithm to analyze gene expression networks to identify biological pathways significantly disrupted in hepatocellular carcinoma. In order to apply the PoTRA algorithm to analyze other cancer gene expression data sets, of various sizes and normal:tumor ratio composition, two important questions must be answered: 1. What is the optimal normal:tumor sample ratio?; and 2. What is the minimum number of samples that should be used for PoTRA analysis? To address these questions, the average standard deviation (SD) in PoTRA-ranked mRNA mediated dysregulated pathways was studied using randomly sampled data sets with various normal:tumor ratios and sizes drawn from the TCGA Breast Invasive Carcinoma (TCGA-BRCA) project. Methods. To identify the optimal normal:tumor sample ratios, the SD analysis used random combinations of 1:N unbalanced normal:tumor data sets: (1:1, 1:2, 1:3, 1:5, 1:7, 1:9). To identify the minimum sample size, random resampling of normal and tumor samples of various sizes are used: (3 vs 3), (5 vs 5), (10 vs 10), (25 vs 25), (50 vs 50), (75 vs 75), (100 vs 100), and (113 vs 113). Results. This analysis suggests that the 1:1 ratio achieves the lowest average rank variation and that the minimum sample size of 50 normal and 50 tumor samples reaches a steady state in the average rank variation. Conclusion. In conclusion, future applications of the PoTRA algorithm to analyze gene expression data sets such as TCGA should use balanced data sets as well as a minimum sample size of 50 for both normal and tumor to ensure the most robust performance.

scholarly journals Pseudo-Location: A novel predictor for predicting pseudo-temporal gene expression patterns using spatial functional regression

2020 ◽  
Author(s):  
Kyungmin Ahn ◽  
Hironobu Fujiwara
Keyword(s):  
Gene Expression ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Functional Regression ◽  
Rna Seq ◽  
Temporal Gene Expression ◽  
Early Stages ◽  
The Future

Statement of withdrawalThe authors have withdrawn version 1 of this manuscript because a draft manuscript, which was still in the early stages of preparation and required major revisions including the replacement of the source RNA-seq datasets, was erroneously submitted. The authors do not wish this version to be cited as reference for this study. We will post a revised manuscript in the future. If you have any questions, please contact the corresponding author.

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