Additional file 1 of Decoding cell-type contributions to the cfRNA transcriptomic landscape of liver cancer
Creators: name: Safrastyan, Aram
name: zu Siederdissen, Christian Höner
name: Wollny, Damian
Publisher: name: figshare
Year: 2023
Subjects: subject: Space Science
subject: Microbiology
subject: FOS: Biological sciences
subject: Cell Biology
subject: Genetics
subject: Physiology
subject: Biotechnology
subject: Environmental Sciences not elsewhere classified
subject: Chemical Sciences not elsewhere classified
subject: Biological Sciences not elsewhere classified

description: Additional file 1. Figure S1: Principal component analysis (PCA) plot. Plots were generated using variance stabilized counts prior to batch correction for Roskams-Hieter et al. (A), Chen et al. (B), Zhu et al. (C) and Block et al. (D) datasets. HD, healthy donor; LC, liver cancer. Figure S2: Performance of model training with Roskams-Hieter et al. (2022) dataset. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values. Figure S3: Differences in cellular sources of cfRNA between matched plasma and extracellular vesicle (EV) samples in the Block et al. (2022) dataset. (A) Importance of cell types in the classification of plasma and EV samples as measured by the Mean Decrease in Accuracy (MDA) value from the random forest model. Cell types were ordered in descending order of MDA. (B) Hepatocyte proportion differences between matched plasma and EV samples from each patient. Figure S4: Performance of model testing on each dataset represented with receiver operator characteristic (ROC) curves and area under ROC (AUC) values. The targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) models were trained with the Roskams-Hieter et al. (2022) dataset and tested on the remaining datasets. The results were assessed with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values . The error bars depicted in the figure represent the 95% confidence intervals. Figure S5: Confusion matrices of model testing. Confusion matrices for the targeted cellular deconvolution (A), Roskams-Hieter et al. gene markers (B), Chen et al. gene markers (C) and combined gene markers (D) model testing. The left-hand top and right-hand bottom numbers represent the positive and negative predictive values, respectively. The numbers on the top and bottom represent the success rate of classifications. Figure S6: Age distribution in Roskams-Hieter et al. and Zhu et al. datasets. Age distribution of female and male healthy donor (HD) and liver cancer (LC) samples in Roskams-Hieter et al. and Zhu et al. datasets (only these datasets contained comprehensive per sample annotations) (A), all samples (B), samples misclassified by the targeted cellular deconvolution model. Figure S7: Influence of sample collection date on model performance. Performance of targeted cellular deconvolution model on Block et al. dataset (only this dataset contained bleed date information): full, after removing liver cancer samples generated after 2010 and after further removing liver cancer samples generated after 2016. The results are represented with receiver operator characteristic (ROC) curves and area under ROC curves (AUC) values.