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We described a general approach for integrating and managing similarities and differences between different datasets using CDEs and inference rules.
STAMP was used to analyse motifs generated by MEME-ChIP for similarity between different datasets using the settings "Metric = PCC, Alignment = SWU, Gap-open = 1000, Gap-extend = 1000,-nooverlapalign Multiple Alignment = IR, Tree = UPGMA, Matching against: user-defined" [ 44].
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The probability that observed overlaps between different datasets would occur by chance was calculated using the hypergeometric cumulative distribution function in the Statistics Toolbox of MATLAB [ 45].
To calculate significance of overlap in gene content between modules and between different datasets we performed Fisher's exact tests using: http://research.microsoft.com/en-us/um/redmond/projects/mscompbio/fisherexacttest.
When properly calibrated, the three sensor sets capture trends in the data quite well, with slight variations between different datasets highlighting the importance of synergistic use of the instruments to determine the most accurate measurement when disagreements arise.
We did a similar analysis using RefSeq IDs as the matching criterion between different datasets.
The Genomic HyperBrowser was used to determine overlap and hierarchical clustering between different datasets [ 46, 47].
Given these considerations, we assumed that the simple raw CNV calculation method we used might not be appropriate to predict reliable CNVs between different datasets.
These datasets can be used to compare and benchmark methods, to provide links between different datasets facilitating meta-analysis, and to enable researchers who lack such resources to test hypotheses that address important questions.
Although the same reference was used for each dataset, comparisons across different datasets are less reliable because of differences in the number and length of sequencing reads between different datasets.
Corresponding scale breaks were found between different datasets.
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