English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

When dealing with large scale gene expression studies, observations arecommonly contaminated by unwanted variation factors such as platforms orbatches. Not taking this unwanted variation into account when analyzing thedata can lead to spurious associations and to missing important signals. Whenthe analysis is unsupervised, e.g., when the goal is to cluster the samples orto build a corrected version of the dataset - as opposed to the study of anobserved factor of interest - taking unwanted variation into account can becomea difficult task. The unwanted variation factors may be correlated with theunobserved factor of interest, so that correcting for the former can remove thelatter if not done carefully. We show how negative control genes and replicatesamples can be used to estimate unwanted variation in gene expression, anddiscuss how this information can be used to correct the expression data orbuild estimators for unsupervised problems. The proposed methods are thenevaluated on three gene expression datasets. They generally manage to removeunwanted variation without losing the signal of interest and compare favorablyto state of the art corrections.

Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed