Using Runtime Traces to Improve Documentation and Unit Test Authoring for Dynamic Languages

Supplemental Figure 3. Dipeptides were significantly upregulated in ovarian cancer cells compared with colon cancer cells. The log-scaled metabolite intensities are presented as box plots representing the median values of experiments performed in 10 (HCT15, HTC116, OVCAR3) and 8 (SKOV3) replicates. Values were obtained after statistical data analysis using the metaP server

Additional file 6: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Figure 3. Dipeptides were significantly upregulated in ovarian cancer cells compared with colon cancer cells. The log-scaled metabolite intensities are presented as box plots representing the median values of experiments performed in 10 (HCT15, HTC116, OVCAR3) and 8 (SKOV3) replicates. Values were obtained after statistical data analysis using the metaP server

Additional file 5: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Table 3. Metabolites that significantly distinguish HCT116 from HCT15

Additional file 1: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Figure 1. Identification of outliers using PCA. PCA score plots generated by the metaP server reveal distinct clustering of HCT15 (dark blue), HCT116 (light blue), OVCAR3 (red), and SKOV3 (orange). Two sample outliers were identified in the SKOV3 cell line and were removed from the analysis

Additional file 2: of Metabolic signatures differentiate ovarian from colon cancer cell lines

Supplemental Table 1. Metabolites present in all examined cell lines. Star (*) indicates compounds that have not been officially “plexed” (based on a standard), although we are confident in their identity

Optimisation à l'échelle du paysage pour un compromis entre services écosystémiques

Metabolomics data from the second sample collection, measured by Metabolon (DS2). (XLSX 379 kb

Additional file 1: of Metabolomics of dates (Phoenix dactylifera) reveals a highly dynamic ripening process accounting for major variation in fruit composition

Figure S1. Phenotyping the date samples. Figure S2. Iterative optimization of the O2PLS-DA classifier. Figure S3. Quality control based on Metabolon/MetaSysX replicate measurements. Figure S4. Heatmap analysis based on DS1-bolon data. Figure S5. Boxplots of PC3&4 loading values arranged by metabolic class. (PPTX 567 kb

Additional file 3: of Metabolomics of dates (Phoenix dactylifera) reveals a highly dynamic ripening process accounting for major variation in fruit composition

Sample processing and metabolomics measurement. (DOCX 31 kb