Table 1 Benchmark datasets. Eleven published datasets were used to compare %total and %parent in significance testing and classification using the treekoR workflow. “Name” is used to refer to each dataset throughout the manuscript
Name | Technology | Description | Number of cells | Number of samples | Outcome or response variable | References |
|---|---|---|---|---|---|---|
Age chronic | CyTOF | Age chronic inflammation predicting young vs old | 1036209 | 29 | Young / old | Shen-Orr et al. 2016 [18] Immport [30] SDY887 dataset |
Anti-CTLA-4 and anti-PD-1 | CyTOF | Predicting response vs non-response in anti-CTLA-4 and anti-PD-1 treatments | 7264780 | 24 | Response / non-response to treatment | Subrahmanyam et al. 2018 [21] |
Anti-PD-1 | CyTOF | Predicting response vs non-response in anti-PD-1 treatment | 85718 | 20 | Response / non-response to treatment | Kreig et al. 2018 [31] |
BCR-XL-sim | CyTOF | Detecting samples with stimulated B cells | 88435 | 16 | Spiked / non-spiked | Weber et al. 2019 [23] |
Breast cancer tumor | CyTOF | Predicting tumor in breast cancer samples | 855914 | 194 | Tumor/non-tumor breast cancer samples | Wagner et al. 2019 [32] |
CMV | CyTOF | Predicting positive vs negative CMV titer results in influenza patients | 18153877 | 69 | Positive/negative results from CMV titer | Tomic et al. 2019 [16] Immport [30] SDY478 dataset |
COVID-19 whole blood CyTOF | CyTOF | Profiling whole blood to predict COVID-19 vs. healthy patients | 4747543 | 21 | COVID-19 / healthy control | Geanon et al. 2021 [33] |
COVID-19 PBMCs | Flow cytometry | Predicting between ICU vs. hospital ward COVID-19 patients | 4790053 | 38 | ICU / ward | Humblet-Baron et al. 2021 [34] |
COVID-19 PBMC CD8+ non-naive T cells | Flow cytometry | Profile of CD8+ Non-Naive T Cells to distinguish recovered from COVID-19 vs. healthy | 11591741 (60% of cells were sampled and analyzed) | 168 | COVID-19 recovered / healthy | Mathew et al. 2020 [35] |
COVID-19 T cells | Flow cytometry | T cell compartment samples (CD4 and CD8) to predict healthy vs COVID-19 | 5000 | 31 | COVID-19 / healthy control | De Biasi et al. 2020 [4] |
Melanoma | scRNA-seq | Predicting response to checkpoint immunotherapy in melanoma | 5928 | 19 | Responder/non-responder | Sade-Feldman et al. 2019 [36] |