Typical analyses in JMP Life Sciences often generate a large number of output files and data sets. This is especially true when input and output files are contained with the same folder, when a process produces multiple output files, or when the same output folder is used for multiple processes. I f any or all of these conditions occur, the number of files in a folder can multiply dramatically. How do you identify and distinguish between the different files? More importantly, how do you prevent overwriting existing files with the output of subsequent processes, particularly when all of the files tend to be similarly named?JMP Life Sciences adds a unique suffix to each output file generated by a process. Suffixes are specific to each process and are dependent on the type of content contained in the file. These suffixes enable you to identify different output files and to correlate them with specific processes. All of the suffixes used by JMP Life Sciences are listed alphabetically in the table below. Each suffix is defined both by the process that generates it and by the contents of the file that carries it.Note : Even with the suffixes, it is still possible to overwrite existing files. You should take care to specify different names for output files when doing multiple runs with the same input files. Alternatively, you can specify different output folders.
Output data set containing allele frequencies
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• Output data set listing the X - and Y -coordinates, probeset IDs, and intensities for each spot on the specified array
• Output data set listing standardized residuals for each observations in the input data set Output data set containing the log-ratio of RNA intensity data for estimated homozygous genotypes Annotation file created by ArrayTrack Output data set containing mu and standard deviation across batches Output data set containing mu and standard deviation across batches Output data set containing case-control association test statistics and .jsl file for plotting p -values Output data set listing the covariates of all the different variables in the input data
• Output data set containing LSMeans differences. Useful in ANOVA -related processes.
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• Data set listing row-wise statistics for the primary data set. Rows meeting the criteria specified in the Delete Rows Satisfying this Expression box on the Options tab are excluded from the data set.
• Output annotation data set associating expanded genotype markers with markers Output data set containing meta-analysis p -values and heterogeneity statistics Graph Time Trends drill down data set. Output data set containing all of the variables from the input data set plus results (row means and standard values, the cluster to which each belongs and its place within the cluster) of the hierarchical clustering .html output files, .jsl file for plotting p -values, and output data set containing association test statistics Output data set containing haplotype frequency estimates that can be used as input to the htSNP Selection process Output data set containing Haseman-Elston regression test statistics and .jsl file for plotting p -values Output data set containing phase assignment probabilities. This data set can be used as the input data set for the Haplotype Trend Regression process. Output data set containing dependent variable (s), window, and F-statistic information. .html output files .html output files, .jsl file for displaying htSNPs, and output data set containing htSNP information Output data set for chromosome N in tall format with sample genotypes, IBS sharing count, and run statistics Output data set containing the covariance parameter estimates from every model fit during the compression .jsl file for displaying LD results .html output file summarizing the tagSNP information .jsl file for creating plots and output data set containing estimates of LDU between pairs of markers Output data set listing power values and associated t-statistics for a set of hypothesis tests for a range of alpha and effects sizes
• Output data set listing the results of the regression analysis and -log2 p -values for association of each of the markers with each of the two quantitative traits, along with annotation information for each of the markers Lists the statistics associated with the ability of each marker to be used as a predictor for the dependent class variable Output symmetric matrix of dissimilarities between specified groups within a study Output data set listing the individual F-statistics for each marker Output data set listing the overall F-statistics for the population Output data set containing the columns in the input data set with four additional columns listing the adjusted p -values for each set of observations Output data set containing the columns in the input data set with four additional columns listing the adjusted p -values for each set of observations Output data set containing the variance within each of the principal components that can be attributed to each of the variance components Output data set containing combined p -values Output data set containing the log intensities of the input data Output data set containing the log ratio of the two-channel input data
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• Output data set listing the X and Y coordinates for each row with the Z values for each sample listed in a separate column
• Output data set listing the X and Y coordinates for each row with the Z values for each sample listed in a separate column
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• The tall data set resulting from either the transposition of a wide data set or the unstacking of a stacked data set. Note: These processes also generate an associated EDDS.
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• Output data test data set containing the p-values for the test that the correlation equals zero Output data set listing each of the markers with selected statistics for the chi-square statistics and p -values for the likelihood ratio test or the z-scores and p -values for the Wald test that test for linkage between each marker and the quantitative trait