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To investigate this, we computed the proportion of observed datasets in which each gene set was declared significant by the independence assumption method, and compared these values to the previously generated proportions observed under random permutation.
To empirically demonstrate that variance inflation results in the false significance of gene sets, we randomly permuted the sample labels associated with each of the 202 Gene Expression Omnibus data sets 10,000 times, and then performed a gene set analysis on each permutation using a common independence assumption method.
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In regard to this assumption, methods and algorithms known from area of control systems with deadtime can be used to compensate influence of network delays in NCS.
Independence assumption methods assume ρ = 0, implying that the variance equals 1.
Fortunately, the shortcomings of independence assumption methods can be addressed through the use of resampling-based methods.
[ X] can be calculated in terms of the concentrations of the underlying components using the rapid equilibrium assumption (Methods).
Many software tools use independence assumption methods (reviewed in [ 7]) and these tools are employed by the majority of publications using gene set testing.
Clearly such high false positive rates have serious implications for the conclusions drawn by any study which uses independence assumption methods to provide biological interpretation of microarray data.
In other words, categories declared statistically significant by independence assumption methods in actual datasets are often declared significant when treatment assignments are made purely at random.
As the use of independence assumption methods in gene array studies is widespread, we conclude that published false gene set findings may be common.
In summary, despite the availability of software and a wide array of methods that address inter-gene correlation, the literature on microarray analysis is rife with studies that use independence assumption methods.
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