40 research outputs found
Predictors of Outcome in Aneurysmal Subarachnoid Hemorrhage Patients:Observations From a Multicenter Data Set
A table containing information on the qRT-PCR performed with seven novel miRNAs and two known miRNAs. Per miRNA, this information includes mean CT, range of CT, cDNA dilution, the number of samples (of 12) with CT < 40, the average read depth, and primer used. (XLSX 8 kb
Additional file 3: Figure S1. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Type-I error rates of regression methods from the balanced design. Type-I error rates of the Negative Binomial with true dispersion (NB), Classic Logistic (CL), Bayes Logistic (BL), and Firth’s Logistic (FL) regressions at alpha levels of 0.05 and 0.01 are shown. The black dotted horizontal lines represent 5 and 1% of Type-I error rates. Dispersion values (ϕ = 0.01 and 1) are separated by black dotted vertical lines. Four values of the number of cases (10, 25, 75 and 500) are placed within each dispersion value. Dotted lines within each symbol imply 95% confidence interval. Figure S1 (A): The figure presents the Type-I error rates when μ = 50. Figure S1 (B): This figure shows the Type-I error rates when μ = 1000. (PNG 399 kb
Additional file 15: Table S6. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Type-I error rates of the NB regression from the balanced design with N D=1  = 10 and μ = 1000. Disp: Dispersion, CovOR: Odds ratios between covariates and case–control status, Ncov: The number of covariates in a model, NB: Negative binomial regression, MLD: Maximum likelihood estimated Dispersion, QLD: Quasi-likelihood estimated Dispersion, TD: The dispersion is used for the sampling. (DOCX 59 kb
Additional file 10: Figure S6. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Bias from regression methods using the permuted HD data with μ g  > 3. Figure S6 contains bias from Negative Binomial regression using DESeq2, Classical Logistic regression (CL), Bayes Logistic regression (BL), and Firth’s Logistic regression (FL). Each black empty dot represents the bias of a gene. The black dotted horizontal line is no bias point. The bias of each gene is calculated using effect sizes of 10,000 permutations. (PNG 53 kb
Additional file 13: Table S4. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Top 10 significant genes from DESeq2 among genes not significant in logistic regressions. Mean.Exp.Case: Normalized mean expression value in cases, Mean.Exp.Cont: Normalized mean expression value in controls, Disp: Dispersion, NB.Pval: P-values from negative binomial regression with true dispersion, CL.Pval: P-values from classical logistic regression, BL.Pval: P-values from Bayes logistic regression, FL.Pval: P-values from Firth’s logistic regression. (DOCX 53 kb
Additional file 18: R code. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
This R code regenerates the simulated data sets. (R 6 kb
Additional file 6: Figure S2. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Type-I error rates from DESeq2 analysis of the permuted HD data. This contains Type-I error rates from DESeq2 (negative binomial model) analysis of the permuted HD data at alpha levels of 0.05 and 0.01. Each black empty dot represents Type-I error rate of a gene. The red dots denote average values of Type-I error rates in each category of dispersion groups. The black dotted horizontal lines are our alpha levels. Figure S2 (A) shows Type-I error rates of all genes at alpha level of 0.05. Figure S2 (B) displays Type-I error rates of all genes at alpha level of 0.01. (PNG 309 kb
Additional file 16: Table S7. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Bias with covariate models from the balanced design of N D=1  = 10 and μ D=0  = 1000. Disp: Dispersion, CovOR: Odds ratios between covariates and case–control status, Ncov: The number of covariates in a model, NB_TD: Negative binomial regression with the dispersion is used for the sampling, FL: Firth’s logistic regression. (DOCX 47 kb
Additional file 11: Figure S7. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
Q-Q plots of the HD Analyses. Figure S7 exhibits the Q-Q plots from the HD analysis adjusting for age at death and RIN from DESeq2 (A), and Classical (B), Bayes (C), and Firth’s (D) Logistic regressions. Each regression method contains three different ways of calculating p-values (Original, DA, and Perm). “Original” p-values (Blue dots) are estimated from asymptotic distribution. “DA” p-values (Black dots) are evaluated from data adaptive asymptotic distribution using 1,000 permutations. “Perm” p-values (Yellow dots) are calculated using 10,000 permutations. (PNG 157 kb
Additional file 14: Table S5. of Evaluation of logistic regression models and effect of covariates for case–control study in RNA-Seq analysis
All significant genes in FL regressions using the DA method. Mean.Exp.Case: Normalized mean expression value in cases, Mean.Exp.Cont: Normalized mean expression value in controls, Disp: Dispersion, NB, CL, BL, FL: P-values from negative binomial regression, classical logistic regression, Bayes logistic regression, Firth’s logistic regression. (XLS 1013 kb