PMC:7039956 / 17034-17914 JSONTXT

{"project":"LitCovid-PubTator","denotations":[{"id":"413","span":{"begin":868,"end":874},"obj":"Chemical"}],"namespaces":[{"prefix":"Tax","uri":"https://www.ncbi.nlm.nih.gov/taxonomy/"},{"prefix":"MESH","uri":"https://id.nlm.nih.gov/mesh/"},{"prefix":"Gene","uri":"https://www.ncbi.nlm.nih.gov/gene/"},{"prefix":"CVCL","uri":"https://web.expasy.org/cellosaurus/CVCL_"}],"text":"Single cell RNA-seq data preprocessing and analysis\nThe FASTQ files were analyzed with the Cell Ranger Software Suite (version 3.1; 10× Genomics). The Seurat (version 3.0) was applied to read the gene-barcode matrix of four tissues. To control quality, we removed cells with \u003c200 genes and 500 UMI counts, and as well as the cells with mitochondrial content higher than 5%. Besides, the genes detected in \u003c3 cells were filtered out. The “sctransform” wrapper in Seurat was applied to normalize the data and remove confounding sources of variation, the “IntegrateData” was used for integrated the Seurat objects from four tissues. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and clustering the cells, cell types were assigned based on their canonical markers. UMAP plots, heatmap, and violin plots were generated with Seurat in R."}

{"project":"LitCovid-PD-FMA-UBERON","denotations":[{"id":"T313","span":{"begin":7,"end":11},"obj":"Body_part"},{"id":"T314","span":{"begin":12,"end":15},"obj":"Body_part"},{"id":"T315","span":{"begin":91,"end":95},"obj":"Body_part"},{"id":"T316","span":{"begin":196,"end":200},"obj":"Body_part"},{"id":"T317","span":{"begin":224,"end":231},"obj":"Body_part"},{"id":"T318","span":{"begin":264,"end":269},"obj":"Body_part"},{"id":"T319","span":{"begin":325,"end":330},"obj":"Body_part"},{"id":"T320","span":{"begin":408,"end":413},"obj":"Body_part"},{"id":"T321","span":{"begin":621,"end":628},"obj":"Body_part"},{"id":"T322","span":{"begin":744,"end":749},"obj":"Body_part"},{"id":"T323","span":{"begin":751,"end":755},"obj":"Body_part"}],"attributes":[{"id":"A313","pred":"fma_id","subj":"T313","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A314","pred":"fma_id","subj":"T314","obj":"http://purl.org/sig/ont/fma/fma67095"},{"id":"A315","pred":"fma_id","subj":"T315","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A316","pred":"fma_id","subj":"T316","obj":"http://purl.org/sig/ont/fma/fma74402"},{"id":"A317","pred":"fma_id","subj":"T317","obj":"http://purl.org/sig/ont/fma/fma9637"},{"id":"A318","pred":"fma_id","subj":"T318","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A319","pred":"fma_id","subj":"T319","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A320","pred":"fma_id","subj":"T320","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A321","pred":"fma_id","subj":"T321","obj":"http://purl.org/sig/ont/fma/fma9637"},{"id":"A322","pred":"fma_id","subj":"T322","obj":"http://purl.org/sig/ont/fma/fma68646"},{"id":"A323","pred":"fma_id","subj":"T323","obj":"http://purl.org/sig/ont/fma/fma68646"}],"text":"Single cell RNA-seq data preprocessing and analysis\nThe FASTQ files were analyzed with the Cell Ranger Software Suite (version 3.1; 10× Genomics). The Seurat (version 3.0) was applied to read the gene-barcode matrix of four tissues. To control quality, we removed cells with \u003c200 genes and 500 UMI counts, and as well as the cells with mitochondrial content higher than 5%. Besides, the genes detected in \u003c3 cells were filtered out. The “sctransform” wrapper in Seurat was applied to normalize the data and remove confounding sources of variation, the “IntegrateData” was used for integrated the Seurat objects from four tissues. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and clustering the cells, cell types were assigned based on their canonical markers. UMAP plots, heatmap, and violin plots were generated with Seurat in R."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T322","span":{"begin":7,"end":11},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T323","span":{"begin":91,"end":95},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T324","span":{"begin":196,"end":200},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T325","span":{"begin":264,"end":269},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T326","span":{"begin":280,"end":285},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T327","span":{"begin":325,"end":330},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T328","span":{"begin":387,"end":392},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T329","span":{"begin":408,"end":413},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T330","span":{"begin":603,"end":610},"obj":"http://purl.obolibrary.org/obo/BFO_0000030"},{"id":"T331","span":{"begin":744,"end":749},"obj":"http://purl.obolibrary.org/obo/GO_0005623"},{"id":"T332","span":{"begin":751,"end":761},"obj":"http://purl.obolibrary.org/obo/CL_0000000"}],"text":"Single cell RNA-seq data preprocessing and analysis\nThe FASTQ files were analyzed with the Cell Ranger Software Suite (version 3.1; 10× Genomics). The Seurat (version 3.0) was applied to read the gene-barcode matrix of four tissues. To control quality, we removed cells with \u003c200 genes and 500 UMI counts, and as well as the cells with mitochondrial content higher than 5%. Besides, the genes detected in \u003c3 cells were filtered out. The “sctransform” wrapper in Seurat was applied to normalize the data and remove confounding sources of variation, the “IntegrateData” was used for integrated the Seurat objects from four tissues. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and clustering the cells, cell types were assigned based on their canonical markers. UMAP plots, heatmap, and violin plots were generated with Seurat in R."}

{"project":"LitCovid-sentences","denotations":[{"id":"T108","span":{"begin":0,"end":51},"obj":"Sentence"},{"id":"T109","span":{"begin":52,"end":146},"obj":"Sentence"},{"id":"T110","span":{"begin":147,"end":232},"obj":"Sentence"},{"id":"T111","span":{"begin":233,"end":373},"obj":"Sentence"},{"id":"T112","span":{"begin":374,"end":432},"obj":"Sentence"},{"id":"T113","span":{"begin":433,"end":629},"obj":"Sentence"},{"id":"T114","span":{"begin":630,"end":809},"obj":"Sentence"},{"id":"T115","span":{"begin":810,"end":880},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"Single cell RNA-seq data preprocessing and analysis\nThe FASTQ files were analyzed with the Cell Ranger Software Suite (version 3.1; 10× Genomics). The Seurat (version 3.0) was applied to read the gene-barcode matrix of four tissues. To control quality, we removed cells with \u003c200 genes and 500 UMI counts, and as well as the cells with mitochondrial content higher than 5%. Besides, the genes detected in \u003c3 cells were filtered out. The “sctransform” wrapper in Seurat was applied to normalize the data and remove confounding sources of variation, the “IntegrateData” was used for integrated the Seurat objects from four tissues. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and clustering the cells, cell types were assigned based on their canonical markers. UMAP plots, heatmap, and violin plots were generated with Seurat in R."}