PMC:7252096 / 111157-113145
Annnotations
LitCovid-PD-FMA-UBERON
| Id | Subject | Object | Predicate | Lexical cue | fma_id |
|---|---|---|---|---|---|
| T103 | 195-201 | Body_part | denotes | genome | http://purl.org/sig/ont/fma/fma84116 |
| T104 | 490-495 | Body_part | denotes | Cells | http://purl.org/sig/ont/fma/fma68646 |
| T105 | 666-671 | Body_part | denotes | cells | http://purl.org/sig/ont/fma/fma68646 |
| T106 | 781-786 | Body_part | denotes | cells | http://purl.org/sig/ont/fma/fma68646 |
| T107 | 906-911 | Body_part | denotes | elbow | http://purl.org/sig/ont/fma/fma24901 |
| T108 | 1278-1283 | Body_part | denotes | cells | http://purl.org/sig/ont/fma/fma68646 |
| T109 | 1530-1535 | Body_part | denotes | cells | http://purl.org/sig/ont/fma/fma68646 |
| T110 | 1563-1567 | Body_part | denotes | cell | http://purl.org/sig/ont/fma/fma68646 |
| T111 | 1814-1819 | Body_part | denotes | cells | http://purl.org/sig/ont/fma/fma68646 |
LitCovid-PD-UBERON
| Id | Subject | Object | Predicate | Lexical cue | uberon_id |
|---|---|---|---|---|---|
| T250 | 466-471 | Body_part | denotes | scale | http://purl.obolibrary.org/obo/UBERON_0002542 |
| T251 | 906-911 | Body_part | denotes | elbow | http://purl.obolibrary.org/obo/UBERON_0001461 |
LitCovid-PD-CLO
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T154 | 62-67 | http://purl.obolibrary.org/obo/CLO_0007836 | denotes | mouse |
| T155 | 464-465 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T156 | 490-495 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | Cells |
| T157 | 537-542 | http://purl.obolibrary.org/obo/OGG_0000000002 | denotes | genes |
| T158 | 649-654 | http://purl.obolibrary.org/obo/OGG_0000000002 | denotes | genes |
| T159 | 666-671 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | cells |
| T160 | 711-716 | http://purl.obolibrary.org/obo/OGG_0000000002 | denotes | genes |
| T161 | 781-786 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | cells |
| T162 | 837-838 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T163 | 1047-1048 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T164 | 1278-1283 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | cells |
| T165 | 1530-1535 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | cells |
| T166 | 1561-1562 | http://purl.obolibrary.org/obo/CLO_0001020 | denotes | a |
| T167 | 1563-1572 | http://purl.obolibrary.org/obo/CL_0000000 | denotes | cell type |
| T168 | 1623-1628 | http://purl.obolibrary.org/obo/OGG_0000000002 | denotes | genes |
| T169 | 1656-1660 | http://purl.obolibrary.org/obo/UBERON_0000473 | denotes | test |
| T170 | 1800-1805 | http://purl.obolibrary.org/obo/UBERON_0000473 | denotes | tests |
| T171 | 1814-1819 | http://purl.obolibrary.org/obo/GO_0005623 | denotes | cells |
LitCovid-PD-GO-BP
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T6 | 1252-1269 | http://purl.obolibrary.org/obo/GO_0006351 | denotes | transcriptionally |
LitCovid-sentences
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| T797 | 0-163 | Sentence | denotes | Libraries corresponding to 4 mice, with 2 Seq-Well arrays per mouse were sequenced using Illumina NextSeq as described (Gierahn et al., 2017, Hughes et al., 2019). |
| T798 | 164-315 | Sentence | denotes | Reads were aligned to the mm10 genome and processed according to the Drop-Seq Computational Protocol v2.0 (https://github.com/broadinstitute/Drop-seq). |
| T799 | 316-489 | Sentence | denotes | Data was normalized and scaled using the Seurat R package v2.3.4 (https://satijalab.org/seurat/): transforming the data to loge(UMI+1) and applying a scale factor of 10,000. |
| T800 | 490-556 | Sentence | denotes | Cells with fewer than 1000 UMIs and 500 unique genes were removed. |
| T801 | 557-717 | Sentence | denotes | To identify major axes of variation within our data, we first examined only highly variable genes across all cells, yielding approximately 5,000 variable genes. |
| T802 | 718-830 | Sentence | denotes | An approximate principal component analysis was applied to the cells to generate 200 principal components (PCs). |
| T803 | 831-1016 | Sentence | denotes | Using a combination of the Jackstraw function in Seurat and observing the “elbow” of the standard deviations of PCs, we chose the top 70 PCs for subsequent clustering and visualization. |
| T804 | 1017-1227 | Sentence | denotes | For 2D visualization, we used a Uniform Manifold Approximation and Projection (UMAP) dimensionality reduction technique (https://github.com/lmcinnes/umap) with “min_dist” set to 0.3 and “n_neighbors” set to 50. |
| T805 | 1228-1442 | Sentence | denotes | To identify clusters of transcriptionally similar cells, we employed unsupervised clustering as described above using the FindClusters tool within the Seurat R package with default parameters and k.param set to 10. |
| T806 | 1443-1536 | Sentence | denotes | Resolution was chosen based on maximization of the average silhouette width across all cells. |
| T807 | 1537-1775 | Sentence | denotes | Clusters were merged if a cell type expressed fewer than 25 significantly upregulated genes by differential expression test (FindAllMarkers implemented in Seurat, setting “test.use” to “bimod,” Bonferroni-adjusted p value cutoff < 0.001). |
| T808 | 1776-1936 | Sentence | denotes | Differential expression tests between cells from saline-treated or IFNa-treated mice were assessed using the FindMarkers function with “test.use” set to “bimod. |
| T809 | 1937-1988 | Sentence | denotes | This dataset can be visualized and downloaded here: |
LitCovid-PubTator
| Id | Subject | Object | Predicate | Lexical cue | tao:has_database_id |
|---|---|---|---|---|---|
| 2768 | 1843-1847 | Gene | denotes | IFNa | Gene:111654 |
| 2769 | 29-33 | Species | denotes | mice | Tax:10090 |
| 2770 | 62-67 | Species | denotes | mouse | Tax:10090 |
| 2771 | 1856-1860 | Species | denotes | mice | Tax:10090 |
2_test
| Id | Subject | Object | Predicate | Lexical cue |
|---|---|---|---|---|
| 32413319-28192419-20790582 | 136-140 | 28192419 | denotes | 2017 |