Journal: PLOS Computational Biology

Article Title: scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing

doi: 10.1371/journal.pcbi.1012083

Figure Lengend Snippet: Single-cell RNA-seq and bulk RNA cohorts used in this study.

Article Snippet: We collected single-cell RNA sequencing (scRNA-seq) data for septic patients and normal controls from the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ), with portal ID: SCP548 (subject PBMCs).

Techniques: Microarray, Biomarker Discovery

Journal: PLOS Computational Biology

Article Title: scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing

doi: 10.1371/journal.pcbi.1012083

Figure Lengend Snippet: A. Single-cell gene expression of peripheral blood mononuclear cells collected from sepsis patients and normal controls. B. Deep neural network architecture of scCaT. scCaT was constructed by blending capsule network and Transformer, and then it was trained using the gene expression of cells. C. Dynamic routing procedures of scCaT. D. Transfer learning. scCaT was transferred to subjects using bulk RNA data for fine-tune. It was evaluated and compared on independent cohorts.

Article Snippet: We collected single-cell RNA sequencing (scRNA-seq) data for septic patients and normal controls from the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ), with portal ID: SCP548 (subject PBMCs).

Techniques: Gene Expression, Construct

Journal: PLOS Computational Biology

Article Title: scCaT: An explainable capsulating architecture for sepsis diagnosis transferring from single-cell RNA sequencing

doi: 10.1371/journal.pcbi.1012083

Figure Lengend Snippet: A-B. ROC and PRC demonstrating the performance of scCaT, existing biomarkers, and traditional machine learning methods, for sepsis diagnosis from single-cell data. C-D. AUROC and AUPRC scores demonstrating the performance of scCaT and the other methods for sepsis diagnosis from microarray data. E-F. Heatmap showing the AUROC and AUPRC scores of scCaT transferred on one cohort and tested on the others.

Article Snippet: We collected single-cell RNA sequencing (scRNA-seq) data for septic patients and normal controls from the Broad Institute Single Cell Portal ( https://singlecell.broadinstitute.org/single_cell ), with portal ID: SCP548 (subject PBMCs).

Techniques: Biomarker Discovery, Microarray

Journal: Nature Communications

Article Title: Single-cell analysis of two severe COVID-19 patients reveals a monocyte-associated and tocilizumab-responding cytokine storm

doi: 10.1038/s41467-020-17834-w

Figure Lengend Snippet: a Flowchart depicting the overall design of the study. Blood draws from patient P1 were performed at 2 time points (day 1 and day 5) and from P2 at 3 time points (day 1, day 5 and day 7). P1 on day 1 and P2 on day 1 and day 5 were positive based on a nucleic acid test of a throat swab specimen. P1 on day 5 and P2 on day 7 were negative based on a nucleic acid test of a throat swab specimen. Patients on day 1 were at the severe stage, and patients were in the remission stage on day 5 (P1 and P2); the day 7 blood draw for P2 (still in the remission stage) was performed based on a positive nucleic acid test on day 5. Note that the samples on day 1 were collected within 12 hours of tocilizumab treatment. b - d UMAP representations of integrated single-cell transcriptomes of 69,237 PBMCs, with 13,239 cells derived from our COVID-19 patients and 55,998 derived from the 10X Genomics official website . Cells are colour-coded by clusters ( b ), disease state ( c ), and sample origin ( d ). Dotted circles represent cell types with a > 5% proportion within PBMCs in ( b ), and clusters significantly enriched in patients versus controls are shown in ( c , d ). Mono, monocyte; NK, natural killer cells; mDCs, myeloid dendritic cells; pDCs, plasmacytoid dendritic cells. e Violin plots of selected marker genes (upper row) for multiple cell subpopulations. The left column presents the cell subtypes identified based on combinations of marker genes.

Article Snippet: We also used published datasets as controls or comparable data, including (1) the scRNA-seq data of PBMCs from 2 healthy donors downloaded from the 10X Genomics official website [ https://support.10xgenomics.com/single-cell-gene-expression/datasets/3.1.0/5k_pbmc_NGSC3_aggr ]; (2) the scRNA-seq data of PBMCs from 22 sepsis patients and 19 related controls , which is available on the Institute Single Cell Portal [ https://singlecell.broadinstitute.org/single_cell ] under accession number SCP548; (3) the bulk RNA-seq data of PBMCs from 3 COVID-19 patients and 3 related controls , which were downloaded from the GSA at the BIG Data Centre under accession number CRA002390 ; and (4) the GRCh38 human reference genome used for the sequencing data alignment, which is available on the 10X Genomics official website [ https://support.10xgenomics.com/single-cell-gene-expression/software/downloads/latest ].

Techniques: Derivative Assay, Marker