plotFit.GS | R Documentation |
This function plots graphs informing on the fit of the mixed modeling of the gene expression performed in TcGSA, for 1 or several gene sets.
plotFit.GS( x, expr, design, subject_name = "Patient_ID", time_name = "TimePoint", colnames_ID, plot_type = c("Fit", "Residuals Obs", "Residuals Est", "Histogram Obs"), GeneSetsList, color = c("genes", "time", "subjects"), marginal_hist = TRUE, gg.add = list(theme()) )
x |
a tcgsa object for |
expr |
a matrix or dataframe of gene expression. Its dimension are nxp, with the p samples in column and the n genes in row. |
design |
a matrix or dataframe containing the experimental variables that used in the model,
namely |
subject_name |
the name of the factor variable from |
time_name |
the name of a numeric variable from |
colnames_ID |
the name of the variable from |
plot_type |
a character string indicating the type of plot to be drawn. The options are
|
GeneSetsList |
a character string containing the names of the gene set whose fit is being checked. If several gene sets are being checked, can be a character list or vector of the names of those gene sets. |
color |
a character string indicating which color scale should be used. One of the 3 :
|
marginal_hist |
a logical flag indicating whether marginal histograms should be drawn.
Only used for |
gg.add |
A list of instructions to add to the |
Boris P. Hejblum
Hejblum BP, Skinner J, Thiebaut R, (2015) Time-Course Gene Set Analysis for Longitudinal Gene Expression Data. PLOS Comput. Biol. 11(6):e1004310. doi: 10.1371/journal.pcbi.1004310
plot1GS
, plotSelect.GS
if(interactive()){ data(data_simu_TcGSA) tcgsa_sim_1grp <- TcGSA.LR(expr=expr_1grp, gmt=gmt_sim, design=design, subject_name="Patient_ID", time_name="TimePoint", time_func="linear", crossedRandom=FALSE) plotFit.GS(x=tcgsa_sim_1grp, expr=expr_1grp, design=design, subject_name="Patient_ID", time_name="TimePoint", colnames_ID="Sample_name", plot_type="Residuals Obs", GeneSetsList=c("Gene set 1", "Gene set 2", "Gene set 3", "Gene set 4", "Gene set 5"), color="genes", gg.add=list(guides(color=FALSE)) ) plotFit.GS(x=tcgsa_sim_1grp, expr=expr_1grp, design=design, subject_name="Patient_ID", time_name="TimePoint", colnames_ID="Sample_name", plot_type="Histogram Obs", GeneSetsList=c("Gene set 1", "Gene set 5"), color="genes", gg.add=list(guides(fill=FALSE)) ) plotFit.GS(x=tcgsa_sim_1grp, expr=expr_1grp, design=design, subject_name="Patient_ID", time_name="TimePoint", colnames_ID="Sample_name", plot_type="Histogram Obs", GeneSetsList=c("Gene set 1", "Gene set 2", "Gene set 3", "Gene set 4", "Gene set 5"), color="genes") }
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