PMC:7417788 / 67017-67739 JSONTXT

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T1445","span":{"begin":15,"end":20},"obj":"SO:0002031"},{"id":"T1446","span":{"begin":302,"end":307},"obj":"SO:0000466"},{"id":"T1447","span":{"begin":340,"end":345},"obj":"SO:0000466"},{"id":"T1448","span":{"begin":609,"end":614},"obj":"SO:0000704"},{"id":"T1449","span":{"begin":685,"end":690},"obj":"SO:0005855"}],"text":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}

{"project":"LitCovid-PD-MONDO","denotations":[{"id":"T139","span":{"begin":182,"end":185},"obj":"Disease"}],"attributes":[{"id":"A139","pred":"mondo_id","subj":"T139","obj":"http://purl.obolibrary.org/obo/MONDO_0018859"}],"text":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}

{"project":"LitCovid-PD-CLO","denotations":[{"id":"T1120","span":{"begin":302,"end":307},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"},{"id":"T1121","span":{"begin":523,"end":524},"obj":"http://purl.obolibrary.org/obo/CLO_0001020"},{"id":"T1122","span":{"begin":609,"end":614},"obj":"http://purl.obolibrary.org/obo/OGG_0000000002"}],"text":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}

{"project":"LitCovid-PD-CHEBI","denotations":[{"id":"T35821","span":{"begin":271,"end":274},"obj":"Chemical"}],"attributes":[{"id":"A14703","pred":"chebi_id","subj":"T35821","obj":"http://purl.obolibrary.org/obo/CHEBI_36751"},{"id":"A17168","pred":"chebi_id","subj":"T35821","obj":"http://purl.obolibrary.org/obo/CHEBI_62248"}],"text":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}

{"project":"LitCovid-sentences","denotations":[{"id":"T373","span":{"begin":0,"end":246},"obj":"Sentence"},{"id":"T374","span":{"begin":247,"end":394},"obj":"Sentence"},{"id":"T375","span":{"begin":395,"end":601},"obj":"Sentence"},{"id":"T376","span":{"begin":602,"end":722},"obj":"Sentence"}],"namespaces":[{"prefix":"_base","uri":"http://pubannotation.org/ontology/tao.owl#"}],"text":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}

{"project":"LitCovid-PubTator","denotations":[{"id":"1457","span":{"begin":636,"end":643},"obj":"Species"}],"attributes":[{"id":"A1457","pred":"tao:has_database_id","subj":"1457","obj":"Tax:100569"}],"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":"To analyze the scRNA-seq data, we log normalized data (1 + counts per 10,000) with the ‘‘sc.pp.normalize_total’’ function before clustering, reduction and performing 2-dimensional t-SNE algorithm clustering with the first 50 principal components. This was done following PCA on top 5,000 most variable genes by using “sc.pp.highly_variable_genes” function in Scanpy with the default parameters. Dimensionality method and identification of significant clusters and was performed using Leiden clustering algorithm which uses a shared nearest neighbour modularity optimization-based clustering algorithm. Marker genes for each significant cluster were found using the function sc.tl.rank_genes_groups with default parameters."}