Data for Non-Neotissue Constituents as Underestimated Confounders in the Assessment of Tissue Engineered Constructs by Near-Infrared Spectroscopy

Datasets, orange workflow file and R scripts necessary to replicate the results of the study "Non-Neotissue Constituents as Underestimated Confounders in the Assessment of Tissue Engineered Constructs by Near-Infrared Spectroscopy". S1-Samples.csv: The details of the samples used in the study. S2-Spectra.csv: The labeled spectra used for data analysis. S3-OrangeWorkFlow.ows: The orange workflow file used for primary machine learning as mentioned in section 2.3 in the study. Requires the software Quasar or Orange 3.33 with the Quasar add-on (Bioinformatics Lab, University of Ljubljana, Slovenia). Upload the S2-Spectra.csv file in the "file" widget for the analysis to start (may require manual intiation in some widgets too). S4.Orangeworkflows.pdf: Detailed overview of the S3-OrangeWorkFlow.ows. S5-Propensity Score Matching.zip: Files used for Propensity Score Matching mentioned in section 2.3.2 S6-matchedmatrix.csv: The matched pair from Propensity Score Matching produced from "S5-Propensity Score Matching.zip" and used for controlling the models in "S3-OrangeWorkFlow.ows" and in "S7-MCCVofSVM.Rmd" as described in section 2.3.2. S7-MCCVofSVM.Rmd: Used for Monte Carlo cross-validation mentioned in section 2.3.2 and produce figure 4 S8-ConfoundersPredictionPerformances.xlsx: The machine learning cross-validation results for models classifying the constructs according to the non-neotissue constituents. Table 3 of the study presents summary of selected metrics form these spreadsheet. S9-ControlledVsUncontrolledPerformances.xlsx: The comprehensive results showing all metrics and the statistical analysis results of the controlled vs the uncontrolled models mentioned in section 2.3.2 and described in section 3.2 S10- Statistics: Files and results of the statistical analysis described in section 3.2 and presented in Table 4