Sentence examples for binomial distribution to test from inspiring English sources

We use the Hansen's generalized shrinkage estimator in conjunction with the Negative Binomial distribution to test genes for differential expression.

Differential expression was assessed using DESeq2, which estimates variance-mean dependence in read counts and uses negative binomial distribution to test for differential expression.

The DESeq package uses a modified Fisher's exact test with data fit to a negative binomial distribution to test for pair-wise differences in count data between sample classes, allowing within-transcript comparisons across a broad dynamic range.

Differential gene expression analysis was conducted using the DESeq package (Anders and Huber 2010) in R, which utilizes a negative binomial distribution to test for differential expression among treatments to better accommodate the well known phenomenon of overdispersion in RNA-seq data.

It uses the Binomial distribution to test whether the number of improved estimates is equal to the number that got worse, where the number of trials is the total of improvements and deteriorations; improvement is a success; and the probability of success is 0.5 under the null hypothesis.

We conducted analysis of the differential abundance of OTUs in different samples by fitting a local regression model with a negative binomial distribution to the data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) and in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

Differential abundance of OTUs in cultivated or wild rice rhizosphere was assessed by fitting a local regression model with a negative binomial distribution to the data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

a — Differential abundance of OTUs in the earliest diverged Oryza species rhizosphere was assessed by fitting a local regression model with a negative binomial distribution to the sequence count data and testing for differential abundance with a likelihood ratio test as implemented in the R package DESeq2 (Love et al. 2014) in conjunction with the Phyloseq package (McMurdie and Holmes 2013).

This tool uses a negative binomial distribution model to test for differential gene expression [ 55].

Cuffdiff [ 7] uses a beta negative binomial distribution model to test the significance of change between samples.

The DESEQ program normalizes mapped reads for individual samples by contig length and library depth using a negative binomial distribution previous to test differences in reads mapped between conditions.