Description Usage Arguments Value Author(s) Examples
Analysis.DISEXP is a complete analysis based on user selection of linear or log regression. The gene expression is calculated as the absolute differences between sampled and normal gene expression data. Analysis also export sets of graphs to facilitate in model selection and analysis result validation.
1 2 3 4 5 6 7 8 | Analysis.DISEXP(
dis.name,
dis.distance,
exp.tumor,
exp.sample,
method = "linear",
showPlot = FALSE
)
|
dis.name |
Set of name of RNA distance |
dis.distance |
Set of RNA distance between mutate and original data |
exp.tumor |
Set of reads from gene expression from tumor samples |
exp.sample |
Set of reads from gene expression from normal samples (usually blood sample) |
method |
Selection of linear or gaussian log link function for regression (linear or log) |
showPlot |
TRUE and FALSE variable if TRUE the output image will be shown on the run |
Returns an S3 object of class DISEXP with results. list of output stats from the model
stats
Correlation - Beta value of the regression model based on data
PValue - P value calculated associated with the correlation
Show of four graph for validation
plots
ScatterPlot plots of gene expression data on RNA distance data with fitted line
GeneExpressionBoxPlot Boxplot checking outliars on gene expression
RNADistanceBoxPlot Boxplot checking outliars on distance
RNADistanceDensityPlot Density plot on distance by different RNA
GeneExpressionDensityPlot Density plot on gene expression by different RNA
Sijie Xu, sijie.xu@mail.utoronto.ca
1 2 3 4 5 6 | disexp <- Analysis.DISEXP(
dis.name = c("hsa-let-7a-1", "hsa-let-7a-1",
"hsa-let-7a-3", "hsa-let-7a-3", "hsa-let-7a-3"),
dis.distance = as.integer(c(10, 35, 91, 100, 92)),
exp.tumor = c(98691, 49201, 57540, 148702, 97721),
exp.sample = c(23495, 23310, 13274, 19337, 14389))
|
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