Number of detected SNPs which are associated to the following seven diseases from WTCCC: Bipolar disorder (BD), coronary artery disease (CAD), hypertension (HT), Crohn's disease (IBD), rheumatoid arthritis (RA), type 1 diabetes (T1D) and type 2 diabetes (T2D). WTCCC refers to the regions reported by the original publication [41] in their Table 3, abbreviations for the other algorithms are just like in Table 2. In brackets we give the number of DNA regions which are covered by the detected SNPs. The whole HLA region on chromosome 6 is counted as only one region.

We show that existing RNA-seq, DNase-seq, and ChIP-seq data exhibit overdispersed per-base read count distributions that are not matched to existing computational method assumptions. To compensate for this overdispersion we introduce a nonparametric and universal method for processing per-base sequencing read count data called Fixseq. We demonstrate that Fixseq substantially improves the performance of existing RNA-seq, DNase-seq, and ChIP-seq analysis tools when compared with existing alternatives.

Supporting tables and figures. Table S1. The impact of different effect sizes on gene selection strategies when the sample size is fixed and relatively small. Mean (STD) of true positives computed from SIMU1 with 20 repetitions are reported. Sample size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S2. The impact of different effect sizes on gene selection strategies when the sample size is fixed and relatively small. Mean (STD) of false positives computed from SIMU1 with 20 repetitions are reported. Sample size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S3. The impact of different sample sizes on gene selection strategies when the effect size is fixed and relatively small. Mean (STD) of true positives computed from SIMU2 with 20 repetitions are reported. Effect size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S4. The impact of different sample sizes on gene selection strategies when the effect size is fixed and relatively small. Mean (STD) of false positives computed from SIMU2 with 20 repetitions are reported. Effect size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S5. The impact of different sample sizes on gene selection strategies when the effect size is fixed and relatively large. Mean (STD) of true positives computed from SIMU2 with 20 repetitions are reported. Effect size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S6. The impact of different sample sizes on gene selection strategies when the effect size is fixed and relatively large. Mean (STD) of false positives computed from SIMU2 with 20 repetitions are reported. Effect size: . Total number of genes: 1000. Number of differentially expressed genes: 100. Number of permutations for Nstat: 10000. The significance threshold: 0.05. Table S7. The impact of different sample sizes on gene selection strategies with simulation based on biological data. Mean (STD) of true positives computed from SIMU-BIO with 20 repetitions are reported. Total number of genes: 9005. Number of permutations for Nstat: 100000. The significance threshold: 0.05. Table S8. The impact of different sample sizes on gene selection strategies with simulation based on biological data. Mean (STD) of false positives computed from SIMU-BIO with 20 repetitions are reported. Total number of genes: 9005. Number of permutations for Nstat: 100000. The significance threshold: 0.05. Table S9. The numbers of differentially expressed genes detected by different selection strategies. Total number of genes: 9005. Number of permutations for Nstat: 100000. The significance threshold: 0.05. Figure S1. Histogram of pairwise Pearson correlation coefficients between genes computed from HYPERDIP without normalization. Number of genes: 9005. Number of arrays: 88. (PDF)

The average over 200 simulation runs is reported for the number of detected associations (Size), the estimated power, the number of false positive detections (FP), the estimated false discovery rate (FDR) and the average number of misclassifications (Mis). GWASelect performed with parameters is abbreviated as GS , MOSGWA as MOS, HLASSO as HL, and single marker tests as SM.

Variables Used for Statistical Analysis.

PRISMA Checklist. (DOC)

Statistical analyses on the groups (contingent positive (CP), negative (CN) and sham) differences in performance averaged over sessions during motor imagery without feedback (MIT) and with proprioceptive feedback (MIT&F), active and passive movement and rest. The performance measures were the percent of time moving the orthosis (PercT), maximum consecutive time moving the orthosis per trial (MaxC), number of orthosis movement onsets (NOns), latency to the first orthosis Onset (Lat) and reaching target performance (ReachT) per session. Statistically significant values (p

Abbreviations: BMI, body mass index; ADRB2, Beta 2-adrenergic receptor gene; Gln27Glu (rs1042714), at codon 27; Arg16Gly (rs1042713), at codon 16.

refers to the total number of SNPs. The methods analyzed are MOSGWA (MOS), HLASSO with three different choices of the parameter , GWASelect with three different choices of the stable-selection-threshold , and single marker tests (SM) with Benjamini Hochberg procedure at level .

Biological data were analysed with all 6 methods, the mean period value is reported in the table (standard deviation in brackets). The expected period is 24 h as the clock is entrained by a 24 h light:dark cycle. 1) The data were collected in two different conditions: LD and SD, monitoring 5 output genes in each of them. 2) (All) represents aggregated results from all data sets. 3) NoCAT3 represents aggregated results from all data sets except the CAT3 marker. +) The cases for which mean period is not statistically different from the 24 h are marked with +.

Abbreviations: OR, odds ratio; CI, confidence interval; I2, Cochran's c–based Q-statistic test for assessing the heterogeneity (>50% indicates a substantial heterogeneity).

ADRB2, Beta 2-adrenergic receptor gene; Gln27Glu (rs1042714), at codon 27; Arg16Gly (rs1042713), at codon 16.

The Method and Variant columns list the names for each method that are used throughout the text and in our Figures. Var. imp. stands for a general variable importance measure; the Trafo. column indicates how the input is transformed before calculating a meta-analysis statistic; the Weights columns indicates the weights used in the calculation of the meta-analysis statistic via Equations 4 or 5.

Column # samples lists the number of samples (after our removal of potential outliers) in each data set. MSAS, Multi-Site Adenocarcinoma Study; HLM, Moffit Cancer Center; DFCI, Dana-Farber Cancer Institute; MSKCC, Memorial Sloan-Kettering Cancer Center; WB, whole blood; PMP, postmenopausal.

This is the repository for our publication "Comparative analyses of phenotypic sequences using phylogenetic trees". This large '.zip' file contains several directories will all scripts and data for our manuscript. The repository is divided into directories with the correspondent information and data./data: Has the song sequences in raw format and time rescaled formats. Also, divided in complete sequences (the 5s recording interval) and song bouts. See main text and supplementary material for more information./empirical_analysis: Has the scripts and results for the sequence alignment and model fit to the data. These are subdivided into treatments following the same rationale described on the manuscript./phylogeny: Has the molecular alignment, BEAST xml files and the MCC tree used in this study./r_package_source_file: The source file with the R package used for model fit./simulations: The scripts and results for simulations./tutorial_analyses: An script showing how to use the resources of the R package.

Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.

Stable distribution function fitting parameters.

n.s. = not significant; n.a. = not applicable; NRI and NTI are measures of phylogenetic dispersion; see methods section for more detail.

Mean of the estimations of the group effect obtained using simulations according to the sample size (Ng; g = 0,1), the number of items (J) and the distribution of the latent trait (Beta distribution).

Methodological quality of 18 articles enrolled in our study by the “Newcastle-Ottawa Quality Assessment Scale”.