Guidelines for using sigQC for systematic evaluation of gene signatures
With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of...
| Main Authors: | , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Springer Nature
2019
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| _version_ | 1797080456134393856 |
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| author | Dhawan, A Barberis, A Cheng, W Domingo, E West, C Maughan, T Scott, J Harris, A Buffa, F |
| author_facet | Dhawan, A Barberis, A Cheng, W Domingo, E West, C Maughan, T Scott, J Harris, A Buffa, F |
| author_sort | Dhawan, A |
| collection | OXFORD |
| description | With the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets. |
| first_indexed | 2024-03-07T01:00:18Z |
| format | Journal article |
| id | oxford-uuid:897cb186-92b0-44e2-b94f-447c3a5a18bc |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T01:00:18Z |
| publishDate | 2019 |
| publisher | Springer Nature |
| record_format | dspace |
| spelling | oxford-uuid:897cb186-92b0-44e2-b94f-447c3a5a18bc2022-03-26T22:25:02ZGuidelines for using sigQC for systematic evaluation of gene signaturesJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:897cb186-92b0-44e2-b94f-447c3a5a18bcEnglishSymplectic Elements at OxfordSpringer Nature2019Dhawan, ABarberis, ACheng, WDomingo, EWest, CMaughan, TScott, JHarris, ABuffa, FWith the increased use of next-generation sequencing generating large amounts of genomic data, gene expression signatures are becoming critically important tools for the interpretation of these data, and are poised to have a substantial effect on diagnosis, management, and prognosis for a number of diseases. It is becoming crucial to establish whether the expression patterns and statistical properties of sets of genes, or gene signatures, are conserved across independent datasets. Conversely, it is necessary to compare established signatures on the same dataset to better understand how they capture different clinical or biological characteristics. Here we describe how to use sigQC, a tool that enables a streamlined, systematic approach for the evaluation of previously obtained gene signatures across multiple gene expression datasets. We implemented sigQC in an R package, making it accessible to users who have knowledge of file input/output and matrix manipulation in R and a moderate grasp of core statistical principles. SigQC has been adopted in basic biology and translational studies, including, but not limited to, the evaluation of multiple gene signatures for potential clinical use as cancer biomarkers. This protocol uses a previously obtained signature for breast cancer metastasis as an example to illustrate the critical quality control steps involved in evaluating its expression, variability, and structure in breast tumor RNA-sequencing data, a different dataset from that in which the signature was originally derived. We demonstrate how the outputs created from sigQC can be used for the evaluation of gene signatures on large-scale gene expression datasets. |
| spellingShingle | Dhawan, A Barberis, A Cheng, W Domingo, E West, C Maughan, T Scott, J Harris, A Buffa, F Guidelines for using sigQC for systematic evaluation of gene signatures |
| title | Guidelines for using sigQC for systematic evaluation of gene signatures |
| title_full | Guidelines for using sigQC for systematic evaluation of gene signatures |
| title_fullStr | Guidelines for using sigQC for systematic evaluation of gene signatures |
| title_full_unstemmed | Guidelines for using sigQC for systematic evaluation of gene signatures |
| title_short | Guidelines for using sigQC for systematic evaluation of gene signatures |
| title_sort | guidelines for using sigqc for systematic evaluation of gene signatures |
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