JMP Genomics Starter | Expression | Row-by-Row Modeling

Row-by-Row Modeling
Click on a button corresponding to an expression row-by-row modeling process. Refer to the table below for guidance.
Fitting a one-way repeated-measures ANOVA linear model to rows of the input data set, using all combinations of different effects as distinct groups
Tip : To examine all possible effects and their interactions separately , choose the ANOVA process instead.
Caution : These processes can be computationally intensive for large data sets.
Fitting a mixed linear model on a row-by-row basis to pre-normalized data and creating numerous output displays, with maximum sophistication and flexibility
Note : You must understand SAS PROC MIXED syntax to use this process.
Testing association of each normalized input data set row with a censored response, fitting a Cox proportional hazards model on a row-by-row basis
Screening for potential allele -specific expression, using both DNA and RNA intensity data
Tip : These statements can be saved in a file and used in ANOVA and Mixed Model Analysis (and in Workflows using these processes) to test an arbitrary set of linear hypotheses regarding the relative importance of different combinations of fixed effects parameters.
Quickly and easily selecting LSMeans fixed effect level differences
Tip : These differences can be saved in a file and used in ANOVA and Mixed Model Analysis (and in workflows using these processes)
Drawing HTML line plots and bar charts for selected rows of a tall data set and two selected experimental design variables, in order to visualize the effects of individual experimental factors , or to detect possible interactions between experimental factors for a given gene or set of genes
See Expression for other subcategories.