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Heatmaps were used to visualise matrices of gene expression values.
Participants were asked to submit a ranked list of predicted interactions from four matrices of gene expression data.
Tables 3A and B-wrap> show the Spearman correlation matrices of gene co-expressions (with rho and P-values) in normal and malignant cervical tissue.
MacClade 4.08 was used to generate the matrices of gene content and gene order data, to trace the encoded characters on tree topologies, and to calculate tree lengths.
All PCAs were performed on covariance matrices of gene expression data represented as log2 normalized probe intensities (n = 2,456 gene probes/sample) using PAST (v2.15) [ 28].
Therefore, we adopt a recently developed shrinkage covariance estimator (Schafer and Strimmer, 2005) for estimating the covariance matrices of gene sets.
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To do this we created data matrices of gene-gene correlations and demonstrate two methods to simply mine the matrix of correlation coefficients.
Matrix singular value decomposition (SVD) was carried out by conventional methods by applying the svd function of Splus or R to the 2,833×13 (cyclic genes) and the 7,784×13 (all genes) matrices of genes by time-points.
First we consider a new scheme of visualizing variation in substitution rates amongst genes and lineages which uses a matrix of gene tree branch lengths.
The matrix of gene expression was then transformed to presence/absence because of the uncertainty involved in the copy number calculation due to the amplification step.
PCA applies a singular value decomposition to the covariance matrix of gene expression data to identify the combination of conditions that explain the greatest variation in the data.
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CEO of Professional Science Editing for Scientists @ prosciediting.com