diff --git a/inst/doc/tutorial.html b/inst/doc/tutorial.html index 211b849..10f8471 100644 --- a/inst/doc/tutorial.html +++ b/inst/doc/tutorial.html @@ -590,7 +590,7 @@

Example results

## [1] "Using a simulated sleuth object for the purposes of the tutorial.\n Simulated using built-in functionality of `rats`." ## ## $rats_version -## [1] '0.2.2' +## [1] '0.3.0' ## ## $R_version ## $R_version$platform @@ -639,7 +639,7 @@

Example results

  • The significance threshold is set to 0.05, the minimum count per sample is set to 10 fragments and the proportion has to change by at least 0.1 to be considered biologically significant. We require at least 0.95 of bootstrap iterations to agree on the call.
  • Are we doing gene-level or transcript-level tests: both.
  • Are we bootstrapping the gene-level or transcript-level tests: both. And we’re doing 100 iterations.
    • -
  • We used version “0.2.2” of the RATs package, and version “R version 3.2.4 (2016-03-10)” of the R engine.
    • +
  • We used version “0.3.0” of the RATs package, and version “R version 3.2.4 (2016-03-10)” of the R engine.
    • # Gene-level calls.
 print( mydtu$Genes )
    @@ -671,22 +671,22 @@

    Example results

    ## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 0.79 FALSE 0.002827543 0.0008647512 0.003923834 +## 6: 0.79 FALSE 0.002961201 0.0009091373 0.004101054 ## 7: NA NA NA NA NA ## 8: 1.00 TRUE 0.000000000 0.0000000000 0.000000000 ## 9: NA NA NA NA NA -## 10: 0.00 TRUE 0.810112959 0.7399410710 0.132418878 +## 10: 0.00 TRUE 0.781021243 0.6979317062 0.127822632 ## boot_p_stdevBA boot_p_minAB boot_p_minBA boot_p_maxAB boot_p_maxBA ## 1: NA NA NA NA NA ## 2: NA NA NA NA NA ## 3: NA NA NA NA NA ## 4: NA NA NA NA NA ## 5: NA NA NA NA NA -## 6: 0.001249149 5.030128e-05 1.550451e-05 0.0154553 0.00480727 +## 6: 0.001351673 5.030128e-05 1.550451e-05 0.0154553 0.00480727 ## 7: NA NA NA NA NA ## 8: 0.000000000 0.000000e+00 0.000000e+00 0.0000000 0.00000000 ## 9: NA NA NA NA NA -## 10: 0.183056538 5.143483e-01 3.311102e-01 0.9915083 0.98871978 +## 10: 0.179372455 5.275608e-01 3.311102e-01 0.9915083 0.98871978 ## boot_na ## 1: NA ## 2: NA @@ -792,19 +792,19 @@

    Example results

    ## 6: NA NA NA NA NA NA ## 7: NA NA NA NA NA NA ## 8: NA NA NA NA NA NA -## 9: TRUE 0.66 FALSE 3.590739e-02 2.876560e-02 5.944924e-03 -## 10: TRUE 0.66 FALSE 3.590739e-02 2.876560e-02 5.944924e-03 +## 9: TRUE 0.68 FALSE 3.691206e-02 2.879183e-02 5.944924e-03 +## 10: TRUE 0.68 FALSE 3.691206e-02 2.879183e-02 5.944924e-03 ## 11: NA NA NA NA NA NA -## 12: FALSE NA NA 4.597400e-01 3.636309e-01 1.993780e-03 -## 13: TRUE 1.00 TRUE 1.871354e-78 3.796695e-78 5.067939e-84 -## 14: TRUE 1.00 TRUE 4.060058e-49 9.213973e-49 5.772403e-53 -## 15: TRUE 0.00 TRUE 1.634819e-21 2.669347e-21 1.066067e-24 -## 16: TRUE 1.00 TRUE 1.005658e-44 1.498829e-44 1.516132e-46 +## 12: FALSE NA NA 4.602585e-01 3.714326e-01 1.993780e-03 +## 13: TRUE 1.00 TRUE 1.927614e-78 4.156033e-78 1.115206e-83 +## 14: TRUE 1.00 TRUE 4.129586e-49 9.060530e-49 5.772403e-53 +## 15: TRUE 0.00 TRUE 1.621090e-21 2.699008e-21 1.066067e-24 +## 16: TRUE 1.00 TRUE 1.131154e-44 1.536769e-44 1.516132e-46 ## 17: NA NA NA NA NA NA -## 18: FALSE 0.00 TRUE 6.998560e-01 1.785258e-01 3.567434e-01 +## 18: FALSE 0.00 TRUE 7.221683e-01 1.746248e-01 3.567434e-01 ## 19: NA NA NA NA NA NA -## 20: FALSE 0.00 TRUE 9.586990e-01 6.345817e-02 7.948790e-01 -## 21: FALSE 0.00 TRUE 9.586990e-01 6.345817e-02 7.948790e-01 +## 20: FALSE 0.00 TRUE 9.495883e-01 6.217841e-02 7.973074e-01 +## 21: FALSE 0.00 TRUE 9.495883e-01 6.217841e-02 7.973074e-01 ## sig boot_dtu_freq conf boot_p_mean boot_p_stdev boot_p_min ## boot_p_max boot_na ## 1: NA NA @@ -818,10 +818,10 @@

    Example results

    ## 9: 1.095659e-01 0.00 ## 10: 1.095659e-01 0.00 ## 11: NA NA -## 12: 1.000000e+00 0.18 +## 12: 1.000000e+00 0.14 ## 13: 2.129120e-77 0.00 ## 14: 6.559376e-48 0.00 -## 15: 8.294307e-21 0.00 +## 15: 7.978313e-21 0.00 ## 16: 6.566542e-44 0.00 ## 17: NA NA ## 18: 9.643718e-01 0.00 @@ -890,7 +890,7 @@

    Interactive plots

    # Start the interactive volcano plot.
 plot_shiny_volcano(mydtu)

    This is what is looks like for the example data (remember that the example has very few genes).

    -

    Transc Conf VS DTU

    +

    Transc Conf VS DTU

    You will need to close down the app to return to your R terminal.