{"doi":"10.1504/ijcbdd.2018.10011905","title":"Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq","abstract":null,"journal":"International Journal of Computational Biology and Drug Design","year":2018,"id":648630,"datarank":0.3413095109838057,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"self_citation_contribution":0.20794415416798362,"citation_network_contribution":0.1333653568158221,"self_endowment_contribution":0.20794415416798362,"citer_contribution":0.1333653568158221,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":3,"citers_with_citation_signal":3,"citers_with_endowment":3,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":443872,"name":"Robert H. Chow","orcid":"0000-0002-1322-2070","position":1,"is_corresponding":false},{"id":443873,"name":"Oleg V. Evgrafov","orcid":"0000-0003-0167-3939","position":2,"is_corresponding":false},{"id":1690532,"name":"William Mack","orcid":null,"position":3,"is_corresponding":false},{"id":1690533,"name":"Christopher P. Walker","orcid":null,"position":4,"is_corresponding":false},{"id":621507,"name":"Jonathan J. Russin","orcid":"0000-0002-5304-4977","position":5,"is_corresponding":false},{"id":1214625,"name":"Fan Gao","orcid":"0000-0002-5133-4102","position":6,"is_corresponding":false},{"id":443868,"name":"Jae Mun Kim","orcid":"0000-0002-8610-9677","position":7,"is_corresponding":false},{"id":1690534,"name":"JiHong Kim","orcid":null,"position":8,"is_corresponding":false},{"id":444740,"name":"Ming Yi Lin","orcid":null,"position":9,"is_corresponding":false},{"id":227170,"name":"Charles Y. Liu","orcid":"0000-0001-6423-8577","position":10,"is_corresponding":false},{"id":1071445,"name":"Kai Wang","orcid":"0000-0003-4328-8799","position":11,"is_corresponding":false},{"id":5239,"name":"James A. Knowles","orcid":"0000-0002-3307-5741","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Evaluation of biological and technical variations in low-input RNA-Seq and single-cell RNA-Seq","abstract":"Copyright © 2018 Inderscience Enterprises Ltd. Background: Low-input or single-cell RNA-Seq are widely used today, but two technical questions remain: 1) in technical replicates, what proportion of noises comes from input RNA quantity rather than variation of bioinformatics tools?; 2) In single neurons, whether variation in gene expression is attributable to biological heterogeneity or just random noise? To examine the sources of variability, we have generated RNA-Seq data from low-input (10/100/1000pg) reference RNA samples and 38 single neurons from human brains. Results: For technical replicates, the quantity of input RNA is negatively correlated with expression variation. For genes in the medium- and high-expression groups, input RNA amount explains most of the variation, whereas bioinformatic pipelines explain some variation for the low-expression group. The t-distributed stochastic neighbour embedding (t-SNE) method reveals data-inherent aggregation of low-input replicate data, and suggests heterogeneity of single pyramidal neuron transcriptome. Interestingly, expression variation in single neurons is biologically relevant. Conclusions: We found that differences in bioinformatics pipelines do not present a major source of variation.","is_dataset_classified":null,"base_score":1.3862943611198906,"endowment":1.3862943611198906,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"19162232","pmcid":null,"openalex_id":"https://openalex.org/W2790791672","authors":[],"funders":[],"total_grants":0,"fwci":0.2474,"citation_percentile":0.51105367,"influential_citations":0,"citation_trend":[{"year":2020,"count":2},{"year":2021,"count":1}],"oa_status":"bronze","license":null,"oa_locations":[{"url":"https://doi.org/10.1504/ijcbdd.2018.10011905","host_type":"journal"},{"url":"https://doi.org/10.1504/ijcbdd.2018.10011905","host_type":"publisher"},{"url":"http://www.inderscienceonline.com/doi/full/10.1504/IJCBDD.2018.10011905","host_type":"publisher"}],"fields_of_study":["Single-cell and spatial transcriptomics","Gene expression and cancer classification","RNA Research and Splicing"],"mesh_terms":[],"keywords":["RNA-Seq","RNA","Computational biology","Computer science","Biology","Gene expression","Transcriptome","Genetics","Gene"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Industry, innovation and infrastructure"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-10T02:48:58.274931Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}