PMC:7417788 / 67017-67263 JSONTXT

{"project":"LitCovid-PMC-OGER-BB","denotations":[{"id":"T1445","span":{"begin":15,"end":20},"obj":"SO:0002031"}],"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."}

{"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."}

{"project":"LitCovid-sentences","denotations":[{"id":"T373","span":{"begin":0,"end":246},"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."}