Commit 6eede5c9 authored by Dorothee Childs's avatar Dorothee Childs

Fix file path, clean up comments

parent 3d25ab83
......@@ -25,7 +25,7 @@ library(knitr)
# Data import
First we load the data from the different TPP experiments. All data have been downloaded from the supplements of the respective publications [@Franken2015, @Reinhard2015, @Savitski2014], converted into tidy format, and concatenated into one table. This table will be made available as supplementary material to the paper. Until then, it can be found in the same folder as this vignette.
First we load the data from the different TPP experiments. All data have been downloaded from the supplements of the respective publications [@Franken2015, @Reinhard2015, @Savitski2014], converted into tidy format, and concatenated into one table. This table will be made available as supplementary data to the `NPARC` package (work in progress) and in the meanwhile is provided in this repository:
```{r load_data}
tppData <- readRDS("../data/tppData.Rds")
......@@ -403,7 +403,7 @@ message("For ", sum(allRSS$repeats == 0) ," proteins, the models successfully co
```
```{r echo=FALSE}
fname <- "allRSS_repeatsIfNeg=10_seed=repeats_reuseParsForNegDiffs.RDS"
fname <- "../data/allRSS.RDS"
if (!file.exists(fname)){
saveRDS(allRSS, fname)
}
......@@ -430,69 +430,6 @@ allRSSannotated %>%
```
<!-- Let us look at the first entries and at the column summaries of the data frame `allRSS`: -->
<!-- ```{r head_rssPerModel} -->
<!-- allRSS %>% -->
<!-- head %>% -->
<!-- kable(digits = 4) -->
<!-- ``` -->
<!-- <!-- ## Compare to results published in the manuscript -->
<!-- ```{r load_paper_results, echo = FALSE, eval=FALSE} -->
<!-- # for comparison: load in-house code results as reported in the paper -->
<!-- f_paper_results <- "/Users/dorotheechilds/Work/Projects/Thermal_proteome_profiling/Gitlab_repositories/Gitlab_paper_nonparametric_TPP/master/04 Data/cache/NPARC_results_min_qupm=1.Rds" -->
<!-- paperResults <- readRDS(f_paper_results) -->
<!-- paperResults <- paperResults %>% -->
<!-- dplyr::select(dataset = datasetID, uniqueID, nPaper = fitted_values, rssNullPaper = rss0, rssAlternativePaper = rss1) %>% -->
<!-- mutate(rssDiffPaper = rssNullPaper - rssAlternativePaper) %>% -->
<!-- mutate(dataset = plyr::mapvalues(dataset, -->
<!-- c("ATP_Reinhardetal_2015_PBS", "Dasatinib_Savitskietal_2014", "Panobinostat_Frankenetal_2015", "Staurosporine_Savitskietal_2014"), -->
<!-- c("ATP", "Dasatinib", "Panobinostat", "Staurosporine"))) %>% -->
<!-- filter(dataset %in% c("ATP", "Dasatinib", "Panobinostat", "Staurosporine")) -->
<!-- paperResults %>% -->
<!-- mutate(dataset = factor(dataset), -->
<!-- nPaper = factor(nPaper)) %>% -->
<!-- filter(!is.na(nPaper)) %>% -->
<!-- summary(digits = 2) %>% -->
<!-- kable() -->
<!-- ``` -->
<!-- ```{r add_paper_results, echo = FALSE, eval=FALSE} -->
<!-- comparison <- full_join(rssPerModel, paperResults) %>% -->
<!-- mutate(diffToPaperNull = rssNullPaper - rssNull) %>% -->
<!-- mutate(diffToPaperAlternative = rssAlternativePaper - rssAlternative) -->
<!-- ``` -->
<!-- ```{r find_diffs_to_paper_results, echo = FALSE, eval=FALSE} -->
<!-- comparison %>% -->
<!-- arrange(-abs(diffToPaperNull), - abs(diffToPaperAlternative)) %>% -->
<!-- head(10) -->
<!-- comparison %>% -->
<!-- filter(is.na(diffToPaperNull)) %>% -->
<!-- filter(dataset == "Panobinostat") -->
<!-- ``` -->
<!-- ```{r plot_diffs_to_paper_results, echo = FALSE, eval=FALSE} -->
<!-- datTmp <- filter(tppData, uniqueID == "RRP9_NA", dataset == "Panobinostat") -->
<!-- predTmp <- filter(allPredictions, uniqueID == "RRP9_NA", dataset == "Panobinostat") -->
<!-- ggplot(predTmp, aes(x = temperature, y = relAbundance)) + -->
<!-- geom_point(aes(shape = factor(replicate), color = factor(compoundConcentration))) + -->
<!-- geom_line(aes(y = alternativePrediction, color = factor(compoundConcentration))) + -->
<!-- theme_bw() + -->
<!-- ggtitle("RRP9_NA") + -->
<!-- scale_color_manual("molar panobinostat concentration", -->
<!-- values = c("1e-06" = "#da7f2d", -->
<!-- "0" = "#808080")) -->
<!-- ``` -->
## Compute test statistics
### Why we need to estimate the degrees of freedom
......
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