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feat(dataProcessPlots): Add confidence interval bars to profile plots #179

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Merged
tonywu1999 merged 4 commits into from
Jan 7, 2026
Merged

feat(dataProcessPlots): Add confidence interval bars to profile plots #179

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71039a9
feat(dataProcessPlots): Add confidence interval bars to profile plots
tonywu1999 Jan 7, 2026
caf4f87
Adjust plot width parameter in utils_dataprocess_plots
tonywu1999 Jan 7, 2026
52670fd
Refactor UPPERBOUND calculation in dataProcessPlots.R
tonywu1999 Jan 7, 2026
b5e943c
add vignette of profile plot
tonywu1999 Jan 7, 2026
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7 changes: 5 additions & 2 deletions R/dataProcessPlots.R
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Expand Up @@ -385,8 +385,11 @@ dataProcessPlots = function(
list(PROTEIN = unique(Protein), PEPTIDE = "Run summary",
TRANSITION = "Run summary", FEATURE = "Run summary",
LABEL = "Endogenous", RUN = RUN,
ABUNDANCE = LogIntensities, FRACTION = 1)
]
ABUNDANCE = LogIntensities, FRACTION = 1,
UPPERBOUND = if("Variance" %in% names(.SD)) LogIntensities + 1.96 * sqrt(Variance) else NA_real_, # 95% confidence interval
LOWERBOUND = if("Variance" %in% names(.SD)) LogIntensities - 1.96 * sqrt(Variance) else NA_real_
)
]
if (is_censored) {
quant$censored = FALSE
}
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10 changes: 9 additions & 1 deletion R/utils_dataprocess_plots.R
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Expand Up @@ -172,6 +172,14 @@
aes(x = .data$RUN, y = .data$newABUNDANCE,
color = .data$analysis, size = .data$analysis,
shape = .data$censored)) +
geom_errorbar(data = input[input$PEPTIDE == "Run summary"],
aes(x = .data$RUN,
ymin = .data$LOWERBOUND,
ymax = .data$UPPERBOUND,
color = .data$analysis),
width = 0.3,
linewidth = 0.5,
linetype = "solid") +
scale_shape_manual(values = c(16, 1),
labels = c("Detected data",
"Censored missing data"))
Expand Down Expand Up @@ -288,4 +296,4 @@
theme_msstats("CONDITIONPLOT", x.axis.size, y.axis.size,
text_angle = text.angle)
plot
}
}
6 changes: 6 additions & 0 deletions vignettes/MSstatsPlus.Rmd
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Expand Up @@ -297,6 +297,12 @@ will be included called `Variance` indicating the variance of the underlying
protein abundance estimate. This variance is used in the next step of the
workflow.

Meanwhile, one can generate a profile plot to visualize the variance of the underlying protein abundance estimates in the form of 95% confidence intervals. For example, with Q9UFW8, one may observe that confidence intervals are wider for runs with more imputed values.

```{r}
dataProcessPlots(summarized, "ProfilePlot", which.Protein = "Q9UFW8", address = FALSE)
```

## Differential analysis

The final step of the `MSstats+` workflow is to perform differential analysis.
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