Returns a data.table with a special print method that shows the top 2 most significant genes by contrast.
This data.table contains columns:
the gene
C=continuous, D=discrete, logFC=log fold change, S=combined using Stouffer's method, H=combined using hurdle method
the coefficient/contrast of interest
upper bound of confidence interval
lower bound of confidence interval
point estimate
z score (coefficient divided by standard error of coefficient)
likelihood ratio test p-value (only if doLRT=TRUE)
Some of these columns will contain NAs if they are not applicable for a particular component or contrast.
# S4 method for ZlmFit
summary(
object,
logFC = TRUE,
doLRT = FALSE,
level = 0.95,
parallel = FALSE,
...
)A ZlmFit object
If TRUE, calculate log-fold changes, or output from a call to getLogFC.
if TRUE, calculate lrTests on each coefficient, or a character vector of such coefficients to consider.
what level of confidence coefficient to return. Defaults to 95 percent.
If TRUE and option(mc.cores)>1 then multiple cores will be used in fitting.
ignored
data.table
print.summaryZlmFit
data(vbetaFA)
z <- zlm(~Stim.Condition, vbetaFA[1:5,])
#>
#> Done!
zs <- summary(z)
#> Combining coefficients and standard errors
#> Calculating log-fold changes
names(zs)
#> [1] "datatable"
print(zs)
#> Fitted zlm with top 2 genes per contrast:
#> ( log fold change Z-score )
#> primerid Stim.ConditionUnstim
#> BCL2 -2.3*
#> CCL3 -3.8*
##Select `datatable` copmonent to get normal print method
zs$datatable
#> primerid component contrast ci.hi ci.lo coef
#> 1: CCL2 D (Intercept) -2.44236757 -3.3620226 -2.9021951
#> 2: CCL3 D (Intercept) -1.58916186 -2.1962433 -1.8927026
#> 3: BCL2 D (Intercept) -1.56579681 -2.1670394 -1.8664181
#> 4: B3GAT1 D (Intercept) -2.69053309 -3.7525507 -3.2215419
#> 5: BAX D (Intercept) -0.33128091 -0.7564909 -0.5438859
#> 6: CCL2 S (Intercept) NA NA NA
#> 7: CCL3 S (Intercept) NA NA NA
#> 8: CCL2 C (Intercept) 22.37163738 19.2284654 20.8000514
#> 9: CCL3 C (Intercept) 23.15685816 20.9696836 22.0632709
#> 10: B3GAT1 S (Intercept) NA NA NA
#> 11: BCL2 S (Intercept) NA NA NA
#> 12: BAX S (Intercept) NA NA NA
#> 13: B3GAT1 C (Intercept) 18.66804935 17.6920962 18.1800728
#> 14: BCL2 C (Intercept) 19.41604938 18.4701211 18.9430853
#> z
#> 1: -12.3702861
#> 2: -12.2211896
#> 3: -12.1685068
#> 4: -11.8907750
#> 5: -5.0139783
#> 6: 9.5954215
#> 7: 19.3191106
#> 8: 25.9402614
#> 9: 39.5425379
#> 10: 43.2252343
#> 11: 46.9035705
#> 12: 60.4223239
#> 13: 73.0204876
#> 14: 78.5001723
#> [ reached getOption("max.print") -- omitted 22 rows ]
## Can use parallel processing for LRT now
summary(z, doLRT = TRUE, parallel = TRUE)
#> Combining coefficients and standard errors
#> Calculating log-fold changes
#> Calculating likelihood ratio tests
#> Refitting on reduced model...
#>
#> Done!
#> Fitted zlm with top 2 genes per contrast:
#> ( log fold change Z-score )
#> primerid Stim.ConditionUnstim
#> BCL2 -2.3*
#> CCL3 -3.8*