# Load and attach PRONE
library(PRONE)Here, we are directly working with the SummarizedExperiment data. For more information on how to create the SummarizedExperiment from a proteomics data set, please refer to the “Get Started” vignette.
The example TMT data set originates from (Biadglegne et al. 2022).
data("tuberculosis_TMT_se")
se <- tuberculosis_TMT_seThis SummarizedExperiment object already includes data of different normalization methods. Since this vignette should show you how to use the PRONE workflow for novel proteomics data, we will remove the normalized data and only keep the raw and log2 data that are available after loading the data accordingly.
se <- subset_SE_by_norm(se, ain = c("raw", "log2"))For normalization, there are multiple functions available which can be used to normalize the data. First of all, to know which normalization methods can be applied:
get_normalization_methods()
#> [1] "GlobalMean" "GlobalMedian" "Median" "Mean"
#> [5] "IRS" "Quantile" "VSN" "LoessF"
#> [9] "LoessCyc" "RLR" "RlrMA" "RlrMACyc"
#> [13] "EigenMS" "MAD" "RobNorm" "TMM"
#> [17] "limBE" "NormicsVSN" "NormicsMedian"You can use normalize_se() by specifying all normalization methods you want to apply on the data. For instance, if you want to perform RobNorm, median, and mean normalization, just execute this line:
se_norm <- normalize_se(se, c("RobNorm", "Mean", "Median"),
combination_pattern = NULL)
#> RobNorm completed.
#> Mean completed.
#> Median completed.Tandem mass tag (TMT), a chemical labeling method that enables the simultaneous MS-analysis of up to 18 samples pooled together, is increasingly being applied in large-scale proteomic studies. Since the integration of multiple TMT batches within a single analysis leads to high batch variation and affects data quality, a batch effect correction method, such as internal reference scaling (IRS) or the limma::removeBatchEffects method (in PRONE: limBE), is required to adjust for these systematic biases. Commonly, batch effect correction is applied after basic normalization. However, the order of normalization and batch effect correction can be changed in PRONE.
For instance, if you want to perform IRS to reduce the batch effects on top of the previously normalized data, you can use the combination pattern “on”.
se_norm <- normalize_se(se, c("RobNorm", "Mean", "Median", "IRS_on_RobNorm",
"IRS_on_Mean", "IRS_on_Median"),
combination_pattern = "_on_")
#> RobNorm completed.
#> Mean completed.
#> Median completed.
#> IRS normalization performed on RobNorm-normalized data completed.
#> IRS normalization performed on Mean-normalized data completed.
#> IRS normalization performed on Median-normalized data completed.Finally, you can also normalize your data by applying the specific normalization method. This makes it possible to design the parameters of an individual function more specifically. For instance, if you want to normalize the data by the median, you can use the function medianNorm(). By default, median normalization is performed on raw-data. Using the individual normalization functions, you can easily specify
se_norm <- medianNorm(se_norm, ain = "log2", aout = "Median")All normalized intensities are stored in the SummarizedExperiment object and you can check the already performed normalization techniques using:
names(assays(se_norm))
#> [1] "raw" "log2" "RobNorm" "Mean"
#> [5] "Median" "IRS_on_RobNorm" "IRS_on_Mean" "IRS_on_Median"We suggest using the default value of the on_raw parameter. This parameter specifies whether the data should be normalized on raw or log2-transformed data. The default value of the “on_raw” parameters was made for each normalization method individually based on publications.
Sample distributions are often skewed due to the systematic biases introduced throughout all steps of an MS-experiment. To evaluate the performance of the normalization methods, the distribution of the normalized data can be visualized using boxplots. One would expect to align the sample distributions more closely after normalization.
Moreover, since normalization tries to remove the technical bias while keeping the biological variation, PCA plots of the log2-transformed data should be used to analyze if the technical bias is more prominent than the biological variation. After normalization, the samples should cluster according to the biological groups rather than the technical biases. So it may be helpful to analyze the PCA plots of the different normalization techniques to see which normalization method mostly reduced the technical bias.
Finally, the assessment of the normalization methods is commonly centered on their ability to decrease intragroup variation between samples, using intragroup pooled coefficient of variation (PCV), pooled estimate of variance (PEV), and pooled median absolute deviation (PMAD) as measures of variability. Furthermore, the Pearson correlation between samples is used to measure the similarity of the samples within the same group. A normalization method should reduce intragroup variation between samples and increases the correlation between samples within the same group. However, please do not focus solely on intragroup variation, but also consider the other evaluation methods and perform differential expression analysis to further evaluate the methods and analyze the impact of normalization on the downstream results. More details on how to evaluate the performance of the normalization techniques are coming along with our paper. Once it is published, it will be referenced here.
Hence, PRONE offers many functions to comparatively evaluate the performance of the normalization methods. Notably, the parameter “ain” can always be set. By specifying “ain = NULL”, all normalization methods that were previously performed and are saved in the SummarizedExperiment object are considered. If you want to evaluate only a selection of normalization methods, you can specify them in the “ain” parameter. For instance, if you want to evaluate only the IRS_on_RobNorm and Mean normalization methods, you can set “ain = c(”IRS_on_RobNorm”, “Mean”)“.
You can comparatively visualize the normalized data by using the function plot_boxplots(), plot_densities(), and plot_pca().
plot_boxplots(se_norm, ain = NULL, color_by = NULL,
label_by = NULL, ncol = 3, facet_norm = TRUE) +
ggplot2::theme(legend.position = "bottom", legend.direction = "horizontal")
#> All assays of the SummarizedExperiment will be used.
#> Condition of SummarizedExperiment used!
#> Label of SummarizedExperiment used!