A standard operating procedure for outlier removal in large-sample epidemiological transcriptomics datasets

Hm, Bøvelstad; Holsbø, Einar; Bongo, Lars Ailo; Lund, Eiliv

doi:10.1101/144519

Cited by 8 publications

(10 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We performed preprocessing of the raw microarray data according to the NOWAC standard procedure (17). Broadly this comprises the following steps; see the code linked above and the referenced manuscript for details:…”

Section: Laboratory Analyses and Data Pre-processingmentioning

confidence: 99%

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort

Holsbø

Olsen

2020

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

Breast cancer patients with metastatic disease have a higher incidence of deaths from breast cancer than patients with early-stage cancers. Recent findings suggest that there are differences in immune cell function between metastatic and non-metastatic cases, even years before diagnosis. We have analyzed whole blood gene expression by Illumina bead chips in blood samples taken using the PAXgene blood collection system up to two years before diagnosis. The final study sample included 197 breast cancer cases and 197 age-matched controls. We defined a causal directed acyclic graph to guide a Bayesian data analysis to estimate the risk of metastasis associated with the expression of all genes and with relevant sets of genes. We ranked genes and gene sets according to the sign probability for excess risk. Among the screening detected cancers, 82% were without metastasis, compared to 53% of between-screening detected cancers. Among the highest ranking genes and gene sets associated with metastasis risk, we identified plasmacytiod dentritic cell function, the SLC22 family of transporters, and glutamine metabolism as potential links between the immune system and metastasis. We conclude that there may be potentially wide-reaching differences in blood gene expression profiles between metastatic and non-metastatic breast cancer cases up to two years before diagnosis, which warrants future study.

show abstract

Section: Laboratory Analyses and Data Pre-processingmentioning

confidence: 99%

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort

Holsbø

Olsen

2020

Front. Oncol.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Pippeline and a description of our microarray preprocessing pipeline are available at: https://github.com/uitbdps/pippeline A demo dataset from [22] is available at: https://doi.org/10.18710/FGVLKS…”

Section: Discussionmentioning

confidence: 99%

“…Steps with a dashed border are optional, while steps with a solid border are mandatory. More details are in[22]-[26] A screenshot of the user interface in R Studio, viewing the documentation help page for the "Biopsies" dataset in the NOWAC study. The right-hand panel shows the documentation generated by the code in the top left panel.…”

mentioning

confidence: 99%

Reproducible data management and analysis using R

Fjukstad

Shvetsov

Nøst

et al. 2019

Preprint

Self Cite

View full text Add to dashboard Cite

Background. Standardizing and documenting computational analyses are necessary to ensure reproducible results. It is especially important for large and complex projects where data collection, analysis, and interpretation may span decades. Our objective is therefore to provide methods, tools, and best practice guidelines adapted for analyses in epidemiological studies that use -omics data. Results.We describe an R-based implementation of data management and preprocessing. The method is well-integrated with the analysis tools typically used for statistical analysis of -omics data. We document all datasets thoroughly and use version control to track changes to both datasets and code over time. We provide a web application to perform the standardized preprocessing steps for gene expression datasets. We provide best practices for reporting data analysis results and sharing analyses. Conclusion.We have used these tools to organize data storage and documentation, and to standardize the analysis of gene expression data, in the Norwegian Women and Cancer (NOWAC) system epidemiology study. We believe our approach and lessons learned are applicable to analyses in other large and complex epidemiology projects.

show abstract

“…The data were processed according to [6] and [7]. The pre-processed data is a 88 × 12404 fold change matrix, X, on the log 2 scale.…”

Section: Materials and Methods Datamentioning

confidence: 99%

Predicting breast cancer metastasis from whole-blood transcriptomic measurements

et al. 2020

Self Cite

View full text Add to dashboard Cite

Objective: In this exploratory work we investigate whether blood gene expression measurements predict breast cancer metastasis. Early detection of increased metastatic risk could potentially be life-saving. Our data comes from the Norwegian Women and Cancer epidemiological cohort study. The women who contributed to these data provided a blood sample up to a year before receiving a breast cancer diagnosis. We estimate a penalized maximum likelihood logistic regression. We evaluate this in terms of calibration, concordance probability, and stability, all of which we estimate by the bootstrap. Results: We identify a set of 108 candidate predictor genes that exhibit a fold change in average metastasized observation where there is none for the average non-metastasized observation.

show abstract

A standard operating procedure for outlier removal in large-sample epidemiological transcriptomics datasets

Cited by 8 publications

References 18 publications

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort

Metastatic Breast Cancer and Pre-Diagnostic Blood Gene Expression Profiles—The Norwegian Women and Cancer (NOWAC) Post-Genome Cohort

Reproducible data management and analysis using R

Predicting breast cancer metastasis from whole-blood transcriptomic measurements

Contact Info

Product

Resources

About