Non-parametric one-sample hypothesis testing via dynamic slicing with O(n)-resolution. The basic idea of ds_eqp_1 is almost the same as ds_1. Difference between these two functions is that ds_eqp_1 considers an equal partition on [0, 1] but ds_1 does not. Candidate slicing boundaries in ds_eqp_1 only depend on the total number of samples and are unrelated to sample quantiles. In ds_1 they are immediately to the left or right of sample quantile.
● Data Source:
CranContrib
● Keywords:
● Alias: ds_eqp_1
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P53 NCI-60 data set provided by Subramanian et al., (2005). A list with phenotypes vector and a vector of sample label values.
● Data Source:
CranContrib
● Keywords:
● Alias: gsa_label
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Load gene set from .gmt file
● Data Source:
CranContrib
● Keywords:
● Alias: load_gmt
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Load phenotype file from .cls file
● Data Source:
CranContrib
● Keywords:
● Alias: load_cls
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Dependency detection between level k (k > 1) categorical variable and continuous variable. The basic idea is that the different values of categorical variable correspond to different distribution of continuous variable if there exist dependency between this two varibles, otherwise the distributions of continuous variable do not show difference conditioning on the values of categorical variable. Statistic for this dynamic slicing method is a regularized likelihood-ratio calculated via a dynamic programming procedure. For more details please refer to Jiang, Ye & Liu (2015). Results contains value of dynamic slicing statistic and slicing strategy. It could be applied for non-parametric K-sample hypothesis testing.
● Data Source:
CranContrib
● Keywords:
● Alias: ds_k
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Gene set analysis via dynamic slicing.
● Data Source:
CranContrib
● Keywords:
● Alias: ds_gsa
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Load gene expression data from .gct file
● Data Source:
CranContrib
● Keywords:
● Alias: load_gct
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Showing slicing result and plotting counts of observations in each slice.
● Data Source:
CranContrib
● Keywords:
● Alias: slice_show
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Ranking genes by signal to noise ratio according to their expression data.
● Data Source:
CranContrib
● Keywords:
● Alias: rank_by_s2n
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Dependency detection between level k (k > 1) categorical variable and continuous variable via dynamic slicing with O(n^{1/2})-resolution. The basic idea is almost the same as ds_k. The only different is that ds_eqp_k groups samples into approximate O(n^{1/2}) groups which contain approximate O(n^{1/2}) samples and performs dynamic slicing on their boundaries. This much faster version could reduce computation time substantially without too much power loss. Based on the strategy of ds_eqp_k, we recommend to apply it in large sample size problem and use ds_k for ordinary problem. For more details please refer to Jiang, Ye & Liu (2015). Results contains value of dynamic slicing statistic and slicing strategy. It could be applied for non-parametric K-sample hypothesis testing.
● Data Source:
CranContrib
● Keywords:
● Alias: ds_eqp_k
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providing 
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