Additional file 9 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 9: Table S6. Summary for the DEG and DEP from RTT-MECP2 vs MDS and RTT-MECP2 versus RTT-like

Additional file 4 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 4: Table S1. Detailed description of the study cohort

Additional file 7 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 7: Table S4. Enrichment analysis results for the RTT-MECP2 versus healthy controls, RTT versus MDS, RTT versus RTT-like, transcriptomic and proteomic data

Additional file 6 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 6: Table S3. RNAseq and proteomics differential expression results for RTT - MECP2 versus healthy controls

Additional file 3 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 3: Fig. S3. Comparison of genes from Genotype-Tissue Expression (GTEx) project and mean TPM (Transcripts per Kilobase Million) in fibroblast cultured cells in RNAseq and RT-qPCR

Additional file 2 of Identification of molecular signatures and pathways involved in Rett syndrome using a multi-omics approach

Additional file 2: Fig. S2. Comparison of the gene expression results between RNAseq and RT-qPCR

Power analysis.

<p>Standard deviation of the residuals from the model in Eq (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199938#pone.0199938.e011" target="_blank">2</a>) (left y-axis) against number of wells per biological sample and per plate (x-axis) for each OCR log-ratio difference (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199938#pone.0199938.t001" target="_blank">Table 1</a>). The right y-axis corresponds to the minimal detectable relative differences using three plates at 5% significance level (Materials and methods). For every number of wells, the 10 data points correspond to each of the 10 random samplings without replacement of the wells per biological sample and per plate.</p

Principle of the mitochondrial stress test assay.

<p>(<b>A</b>) Cartoon illustration of OCR levels (y-axis) versus time (x-axis). Injection of the three compounds oligomycin, FCCP, and rotenone delimits four time intervals within each of which OCR is roughly constant. (<b>B</b>) Targets of each compound in the electron transport chain. (<b>C</b>) Typical layout of a mitochondrial stress test 96-well plate.</p

OCR ratios, abbreviations, definitions, metrics, and analogous definitions.

<p>OCR ratios, abbreviations, definitions, metrics, and analogous definitions.</p

OCR behavior over time.

<p>(<b>A</b>) Typical time series replicates inside a plate. Behavior of OCR expressed in pmol/min (y-axis) of Fibro_VY_017 over time (x-axis). Colors indicate the row and shape the column of 12-well replicates. Variation increases for larger OCR values, OCR has a systematic well effect, and there are two types of outliers: well-level and single-point. (<b>B</b>) Scatterplot of standard deviation (y-axis) vs. mean (x-axis) of all three time replicates of each interval, well, and plate of OCR of NHDF only shows a positive correlation (<i>n</i> = 409). (<b>C</b>) The same as (B) but for the logarithm of OCR, where the correlation disappears.</p