Sentence examples for gene filtering from inspiring English sources

To assess whether filtering with RNA-seq data could improve the clustering results, we performed hierarchical clustering using only the differentially expressed genes (DEGs) identified in Fig. 2 and found that RNA-seq could obtain a similar level of clustering power compared to ATAC-seq by performing gene filtering.

Before marker gene selection, we used following gene filtering.

The graph are calculated for the data corresponding to two methods: MAS5-Spearman without gene filtering (all gn) (Fig. 4A) and RMA-Pearson with gene filtering (filtered gn) (Fig. 4B).

The original coexpression data used in Figure 2 are obtained without any gene filtering, however for the analyses in Figure 3 it was convenient to study the effect of gene filtering upon the accuracy and coverage of the methods.

We do caution that this perfect-feature gene approach is sensitive to the amount and type of gene filtering.

For our next analysis, we used SWISS to evaluate the effect of different types of and amounts of gene filtering.

After applying the gene filtering steps, a total of n = 3,364 genes were selected for the microarray predictor building and testing.

Given the current scale of high throughput data, a combinatorial gene selection scheme is needed at different stages of gene filtering.

The gene-filtering parameters and results are summarized in Supplemental Material, Table 1 (http://www.ehponline.org/members/2008/11229/suppl.pdf).org/members/2008/11229/suppl.pdf

This procedure was utilized only as a gene-filtering step, as the original weighting for these genes in the predictive algorithm was retained.

Accordingly, gene-filtering criteria were based on 2-fold change in gene expression in As-treated Col-0 versus Ws-2 (Col-0 200 μM As/Col-0 Control versus Ws-2 100 μM As/Ws-2 Control).