Transformed expression data for gene B3GNT3.

<p>Transformed expression data for gene B3GNT3.</p

Areas under the curve for the simulation study.

<p>A: 10 genes have a linear effect on the patient outcome. B: 20 genes have a linear effect. C: 10 genes have a logarithmic effect. D: 20 genes have a logarithmic effect.</p

Transformations for RNA-Seq data.

<p>Proposed transformations for RNA-Seq data. A check mark is given in the columns skewness, unequal variances or outliers, if the transformation is addressing the corresponding problem. The last column shows the transformed distribution of gene B3GNT3 as an example.</p

KIRC data: Number of selected genes in a CoxBoost model of the KIRC data.

<p>The transformations are separated in three blocks: those not using standardization, those using standardization and the non-parametric ones. The diagonal elements give mean and standard deviation for the corresponding transformation. Diagonal elements called “all” give the number of overlapping genes for the whole block of transformations. Non-diagonal elements show the number of overlapping genes for two transformation, the non-diagonal elements called “all” give the overlap between two blocks.</p

Added value for KIRC and AML data.

<p>Positive added values indicate improvement in prediction error. A: CoxBoost model used for prediction on KIRC data. B: Lasso used for prediction on KIRC data. C: CoxBoost model used for prediction on AML data. D: Lasso model used for prediction on AML data.</p

RNA-Seq of KIRC data.

<p>A: Scatterplot for all DESeq-normalized counts: Mean vs. variance. The larger the mean value, the larger the variance. The red dot is a randomly chosen gene called B3GNT3. B: Histogram of DESeq-normalized counts for gene B3GNT3. The distribution is skewed and has extreme values. C: 1000 highest gene-gene correlations for the original data compared to the same gene-gene correlations for data in which we truncated the extreme values.</p

Selected genes in the KIRC data.

<p>Median variance of selected genes plotted against the number of selected genes in 50 resampling datasets. We used a smoothing spline on the scatterplot for better visualization of the association.</p

Prediction error for the KIRC and AML data.

<p>The 0.632+ estimator for the prediction error in terms of the Brier Score. The solid black line is the prediction error of the Kaplan-Meier estimate which does not include clinical information nor RNA-Seq data, the dashed black line the prediction error of the clinical model. A: CoxBoost model used for prediction on KIRC data. B: Lasso used for prediction on KIRC data. C: CoxBoost model used for prediction on AML data. D: Lasso model used for prediction on AML data.</p

Scatter plots.

<p>Scatter plots showing the corresponding expression levels of (A) miR-148a, (B) miR-223, (C) miR-338-3p and (D) miR-107 for the paired pre- and post resection samples for each early-stage breast cancer patient (n = 24).</p

T.nylanderi contigs

de novo assembled contigs. Trimmed reads were assembled to contigs for each of the worker groups in CLC workbench. A meta-assembly on these contigs was conducted in MIRA