Additional file 1 of Seq-ing improved gene expression estimates from microarrays using machine learning
Creators: name: Korir, Paul
name: Geeleher, Paul
name: Seoighe, Cathal
Publisher: name: Figshare
Year: 2015
Subjects: subject: Genetics
subject: FOS: Biological sciences
subject: Molecular Biology
subject: 59999 Environmental Sciences not elsewhere classified
subject: FOS: Earth and related environmental sciences
subject: 69999 Biological Sciences not elsewhere classified
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics

description: Additional information containing online methods is provided as a PDF file.Figure S1. The effect of training size on predictions. A test set of 100 samples was used for all training sample sizes. Figure S2. Out-of-bag (OOB) filtering. Scatter plot of cross-sample correlation coefficients from on OOB gene expression estimates and estimates obtained on test data for 1,000 randomly selected genes. Figure S3. Comparison of differential expression results. Comparison of the performance of MaLTE and median-polish on the problem of detecting differential gene expression between five heart-muscle and five skeletal-muscle samples in the GTEx dataset. Each method results in a list of genes, ranked by the q-value from the comparison of the gene expression level in the two groups of samples. We used the (a) cumulative Jaccard index and (b) concordance correlation to compare the similarity as a function of rank and the overall similarity, respectively, between lists of genes ranked by MaLTE/median-polish and by RNA-seq. The set of genes assessed were defined by RNA-Seq: genes with RPKM of above one in both tissues. Bootstrap re-sampling (100 pseudo-replicates) was used to assess the effect of sampling error for both cumulative Jaccard index and concordance correlations. Results of bootstrapping are shown as faint lines around the observed Jaccard index and yield the density plots shown in (b). Log of fold change estimates for (c) 120 differentially expressed genes common to MaLTE and median-polish and (d) MaLTE and PLIER for six common genes. Figure S4. Comparison of 10-sample DGE to 44-sample DGE. (a) Self-comparisons (e.g. MaLTE 10-sample DGE to MaLTE 44-sample DGE, with five and 22 samples in each tissue, respectively). Forty-four-sample DGE results are treated as putative true differential expressions at the indicated (top right or each plot) false discover rates (FDRs). FDRs were computed using the q-value method (Storey and Tibshirani 2003). Comparisons are made using the Jaccard index. (b) Comparison of each method to 44-sample DGE using RNA-Seq. Figure S5. Transcript isoform expression estimates. Densities of (a) Pearson and (b) Spearman cross-sample correlations for transcript isoform expression estimates obtained using MaLTE. Filtered data corresponds to transcript isoforms with rOOB&gt;0. Figure S6. Box plots of slopes Î˛ computed by linearly regressing RNA-Seq against array method. Dotted red line indicates unit gradient. Each box plot represents 12 slopes for brain samples. RNA-Seq expression is restricted to RPKM between one and 1000 because of high uncertainty at low values and few genes at high values representing 78% of genes quantified. Using all genes results in the same medians but wider variance in slopes due to outlying genes. Figure S7. Estimation of tissue mixture proportions. (a) MaLTE, (b) median-polish (RMA), and (c) PLIER. Each plot shows comparisons of true and estimated proportions with key statistics indicated in the legends.