6  Heart use case: a MaxQuant LFQ DDA dataset with a more complex design

6.1 Introduction

In this chapter we show how to analyse LFQ data from an experiment with a more complex design. The data are a small subset of the public dataset PXD006675 on PRIDE.

Particularly, the proteomes of the atrium and ventriculum in the left and the right heart region are profiled for 3 patients (identifiers 3, 4, and 8). Hence, the design consists of a factor tissue (atrium, ventriculum), region (left, right) and block (patient 3,4, and 8).

Suppose that researchers are mainly interested in comparing the ventricular to the atrial proteome. Particularly, they would like to compare the left atrium to the left ventricle, the right atrium to the right ventricle, the average ventricular vs atrial proteome and if ventricular vs atrial proteome shifts differ between left and right heart region.

6.2 Load packages

First, we load the msqrob2 package and additional packages for data manipulation and visualisation.

library("msqrob2")
library("ggplot2")
library("patchwork")
library("ggrepel")
library("dplyr")

We also configure the parallelisation framework.

library("BiocParallel")
register(SerialParam())

6.3 Load Data

6.3.1 Getting the data

The data were searched with MaxQuant version version 1.5.5.6 and are deposited on the PRIDE repository PXD006675.

In this chapter we use a small subset of the data that is available on TODO put on Zenodo and use BiocFileCache.

library("BiocFileCache")
bfc <- BiocFileCache()
pepFile <- bfcrpath(bfc, "https://raw.githubusercontent.com/statOmics/PDA21/data/quantification/heart/peptides.txt")

After downloading the files, we can load the peptide table, which is in “wide format”. Hence, each row represents a single peptide and that each quantification column (that starts with "Intensity") represents a single sample.

peps <- read.delim(pepFile)
quantcols <- grep("Intensity\\.", names(peps), value = TRUE)
Sequence N.term.cleavage.window C.term.cleavage.window Amino.acid.before First.amino.acid Second.amino.acid Second.last.amino.acid Last.amino.acid Amino.acid.after A.Count R.Count N.Count D.Count C.Count Q.Count E.Count G.Count H.Count I.Count L.Count K.Count M.Count F.Count P.Count S.Count T.Count W.Count Y.Count V.Count U.Count O.Count Length Missed.cleavages Mass Proteins Leading.razor.protein Start.position End.position Gene.names Protein.names Unique..Groups. Unique..Proteins. Charges PEP Score Identification.type.LA3 Identification.type.LA4 Identification.type.LA8 Identification.type.LV3 Identification.type.LV4 Identification.type.LV8 Identification.type.RA3 Identification.type.RA4 Identification.type.RA8 Identification.type.RV3 Identification.type.RV4 Identification.type.RV8 Fraction.Average Fraction.Std..Dev. Fraction.1 Fraction.2 Fraction.3 Fraction.4 Fraction.5 Fraction.6 Fraction.7 Fraction.8 Fraction.100 Experiment.LA3 Experiment.LA4 Experiment.LA8 Experiment.LV3 Experiment.LV4 Experiment.LV8 Experiment.RA3 Experiment.RA4 Experiment.RA8 Experiment.RV3 Experiment.RV4 Experiment.RV8 Intensity Intensity.LA3 Intensity.LA4 Intensity.LA8 Intensity.LV3 Intensity.LV4 Intensity.LV8 Intensity.RA3 Intensity.RA4 Intensity.RA8 Intensity.RV3 Intensity.RV4 Intensity.RV8 Reverse Potential.contaminant id Protein.group.IDs Mod..peptide.IDs Evidence.IDs MS.MS.IDs Best.MS.MS Oxidation..M..site.IDs MS.MS.Count
AAAAAAAAAK AKFRKQERAAAAAAAA AAAAAAAKNGSSGKKS R A A A K N 9 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 10 0 785.4396 Q99453 Q99453 159 168 PHOX2B Paired mesoderm homeobox protein 2B yes yes 2 0.0000121 170.060 By matching By matching By matching By matching By matching By matching By matching By matching 83.5 35.9 NA 2 1 5 1 7 2 6 113 1 1 NA 1 1 NA NA 1 1 1 1 NA 3.4832e+10 288590000 118170000 0 257550000 308710000 0 0 194640000 144740000 456330000 107900000 0 NA 1 9885 1 9;10;11;12;13;14;15;16;17;18;19;20;21;22;23;24;25;26;27;28;29;30;31;32;33;34;35;36;37;38;39;40;41;42;43;44;45;46;47;48;49;50;51;52;53;54;55;56;57;58;59;60;61;62;63;64;65;66;67;68;69;70;71;72;73;74;75;76;77;78;79;80;81;82;83;84;85;86;87;88;89;90;91;92;93;94;95;96;97;98;99;100;101;102;103;104;105;106;107;108;109;110;111;112;113;114;115;116;117;118;119;120;121;122;123;124;125;126;127;128;129;130;131;132;133;134;135;136;137;138;139;140;141;142;143;144;145 9;10;11;12;13;14;15;16;17;18;19;20;21;22;23;24;25;26;27;28;29;30;31;32;33;34;35;36;37;38;39;40;41;42;43;44;45;46;47;48;49;50;51;52;53;54;55;56;57;58;59;60;61;62 18 50
AAAAAAAAEQQSSNGPVK ________________ QSSNGPVKKSMREKAV M A A V K K 8 0 1 0 0 2 1 1 0 0 0 1 0 0 1 2 0 0 0 1 0 0 18 0 1640.8118 Q16585 Q16585 2 19 SGCB Beta-sarcoglycan yes yes 2 0.0000000 185.250 By MS/MS By MS/MS 4.5 1.8 NA 2 NA NA 1 3 NA NA NA NA NA NA NA NA NA NA NA NA 1 1 NA 9.4024e+07 0 0 0 0 0 0 0 0 0 0 29925000 0 NA 4 7001 4;5 149;150;151;152;153;154 67;68;69;70;71;72;73 67 7
AAAAAAAAGAFAGR APLLGARRAAAAAAAA AAGAFAGRRAACGAVL R A A G R R 10 1 0 0 0 0 0 2 0 0 0 0 0 1 0 0 0 0 0 0 0 0 14 0 1145.5942 Q8N697 Q8N697 20 33 SLC15A4 Solute carrier family 15 member 4 yes yes 2 0.0003300 119.620 7.0 0.0 NA NA NA NA NA NA 5 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 2.5454e+08 0 0 0 0 0 0 0 0 0 0 0 0 NA 5 8631 6 155;156;157;158;159 74;75 74 1
AAAAAAAPEPPLGLQQLSALQPEPGGVPLHSSWTFWLDR AREPPGSRAAAAAAAP SWTFWLDRSLPGATAA R A A D R S 8 1 0 1 0 3 2 3 1 0 6 0 0 1 6 3 1 2 0 1 0 0 39 0 4049.0799 Q8N5X7 Q8N5X7 21 59 EIF4E3 Eukaryotic translation initiation factor 4E type 3 yes yes 3;4 0.0000700 57.832 By matching By MS/MS 2.5 1.5 1 2 NA NA 1 NA NA NA NA 1 NA NA NA NA NA 1 NA NA NA NA NA 8.5505e+07 15932000 0 0 0 0 0 8996400 0 0 0 0 0 NA 9 8622 10 601;602;603;604 349;350;351 349 3
AAAAAAGAASGLPGPVAQGLK ________________ GPVAQGLKEALVDTLT M A A L K E 9 0 0 0 0 1 0 4 0 0 2 1 0 0 2 1 0 0 0 1 0 0 21 0 1747.9581 Q96P70 Q96P70 2 22 IPO9 Importin-9 yes yes 2 0.0000001 177.810 1.0 0.0 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3.3872e+07 0 0 0 0 0 0 0 0 0 0 0 0 NA 12 9760 13 686 450 450 1
AAAAAATAPPSPGPAQPGPR AAPARAPRAAAAAATA GPAQPGPRAQRAAPLA R A A P R A 8 1 0 0 0 1 0 2 0 0 0 0 0 0 6 1 1 0 0 0 0 0 20 0 1754.9064 Q6SPF0 Q6SPF0 151 170 SAMD1 Atherin yes yes 2 0.0007018 72.290 7.0 0.0 NA NA NA NA NA NA 1 NA NA NA NA NA NA NA NA NA NA NA NA NA NA 9.4351e+06 0 0 0 0 0 0 0 0 0 0 0 0 NA 16 7795 18 720 472 472 1

We now extract the sample annotations. We will build a table where each row in the annotation table contains information for one sample (the table below shows the first 6 rows). This information is extracted from the sample names.

coldata <- data.frame(quantCols = quantcols) |> 
  mutate(location  = substr(quantCols, 11, 11)) |> # heart region left-right
  mutate(tissue  = substr(quantCols, 12, 12)) |> # tissue Atrium-Ventriculum
  mutate(patient  = substr(quantCols, 13, 13)) # patient id
quantCols location tissue patient
Intensity.LA3 L A 3
Intensity.LA4 L A 4
Intensity.LA8 L A 8
Intensity.LV3 L V 3
Intensity.LV4 L V 4
Intensity.LV8 L V 8

6.3.2 The QFeatures data class

We combine the two tables into a QFeatures object.

(pe <- readQFeatures(
  peps, colData = coldata, fnames = "Sequence", name = "peptides"
))
An instance of class QFeatures (type: bulk) with 1 set:

 [1] peptides: SummarizedExperiment with 31319 rows and 12 columns

We now have a QFeatures object with 1 set, containing r nrows(pe)[[1]] rows (peptides) and 12 columns (samples).

6.4 Data preprocessing

msqrob2 relies on the QFeatures data structure, meaning that we can directly make use of QFeatures’ data preprocessing functionality (see also the QFeatures documentation).

6.4.1 Encoding missing values

Peptides with zero intensities should be encoded using NA.

pe <- zeroIsNA(pe, "peptides")

We calculate how many non zero intensities we have per peptide and this is often useful for filtering.

naResults <- nNA(pe, "peptides")
data.frame(naResults$nNArows) |> 
  ggplot() +
  aes(x = nNA) +
  geom_histogram()

6.4.2 PSM filtering

We filter features based on 3 criteria (see PSM filtering).

  1. Remove failed protein inference

We remove peptides that could not be uniquely mapped to a protein.

pe <- filterFeatures(pe,
  ~ Proteins != "" & ## Remove failed protein inference
    !grepl(";", Proteins)) ## Remove protein groups
  1. Remove reverse sequences (decoys) and contaminants

We remove the contaminants and peptides that map to decoy sequences. These features bear no information of interest and will reduce the statistical power upon multiple test adjustment.

pe <- filterFeatures(pe, ~ Reverse != "+" & Potential.contaminant != "+")
  1. Remove highly missing peptides.

We keep peptides that were observed at last 3 times out of the \(n = 12\) samples, so we tolerate the following proportion of NAs: \(\text{pNA} = \frac{(n - 3)}{n} = 0.75\), so we keep peptides that are observed in at least 25% of the samples.

nObs <- 3
n <- ncol(pe[["peptides"]])
(pe <- filterNA(pe, i = "peptides", pNA = (n - nObs) / n))
An instance of class QFeatures (type: bulk) with 1 set:

 [1] peptides: SummarizedExperiment with 15630 rows and 12 columns

We keep 15630 peptides upon filtering.

6.4.3 Standard preprocessing workflow

We can now prepare the data for modelling. The workflow ensures the data complies to msqrob2’s requirements:

  1. Intensities are log-transformed.
pe <- logTransform(pe, base = 2, i = "peptides", name = "peptides_log")
  1. Normalisation with Median of Ratios method.
pseudoRef <- assay(pe[["peptides_log"]]) |> 
  rowMeans(na.rm = TRUE) #1. Calculate the row means 

nfLog <- sweep(
  assay(pe[["peptides_log"]]), 
  MARGIN = 1, 
  pseudoRef) |> #2. Subtract the row means row-by-row (MARGIN = 1)
  colMedians(na.rm = TRUE)  #3. Calculate the column median 

pe <- 
  sweep(pe, 
        MARGIN = 2, 
        STATS = nfLog , 
        i = "peptides_log", 
        name = "peptides_norm") #4. Subtract log2 norm factor column-by-column (MARGIN = 2)

Upon the normalisation the density curves should be nicely centred. To confirm this, we will plot the intensity distributions for each biorepeat (mouse). longForm() seamlessly combines the quantification and annotation data into a table suitable for ggplot2 visualisation. We also subset the object with the data before and after normalisation.

longForm(pe[, , c("peptides_log", "peptides_norm")], colvar = "patient") |> 
  ggplot() +
  aes(x = value, group = colname, color = patient) +
  geom_density() +
  facet_wrap(~ assay, scale = "free")

  1. Summarisation to protein level.

We use the robust summary approach to infer protein-level data from peptide-level data, accounting for the fact that different peptides have ionisation efficiencies hence leading to different intensity baselines.

pe <- aggregateFeatures(
  pe, i = "peptides_norm", fcol = "Proteins", 
  fun = MsCoreUtils::medianPolish, 
  na.rm = TRUE, name = "proteins"
)

6.5 Data exploration

We will explore the main sources of variation in the data using MDS.

library("scater")
se <- getWithColData(pe, "proteins") |> 
  as("SingleCellExperiment") |> 
  runMDS(exprs_values = 1) 
plotMDS(se, colour_by = "tissue") +
  plotMDS(se, colour_by = "location") +
  plotMDS(se, colour_by = "patient")

Note, that the samples upon robust summarisation show a clear separation according to the tissue type in the first dimension and according to location in the second dimension.

6.6 Data modelling

The preprocessed data can now be modelled to answer biologically relevant questions. Particularly, the protein abundance can differ according to tissue type (A-V) and location (L-R). Moreover, the effect of the tissue type can differ according to the location and vice versa. Hence, there can be an interaction between tissue and location.

The samples are also not independent as four biopsies (LA, RA, LV and RV) were taken for each patient. Because the proteome is profiled for each tissue x location combination within each patient, the design is a randomised complete block (RCB) design.

RCB designs can be correctly analysed by incorporating the block effect for patient either as a fixed or a random effect. The use of a fixed patient effect is here also possible because the effect of each factor combination can be estimated within block (patient).

Here, we choose to account for the patient effect using fixed effects because mixed models are computationally more demanding and rely on asymptotic inference (i.e. statistical inference is only valid for experiments with large sample sizes).

Now we have identified the sources of variation in the experiment that we have to account for (tissue, location and patient id), we can define a model.

model <- ~ location*tissue + ## (1) fixed effects: main effects for location and tissue type, and a tissue x location interaction
  patient  ## (2) fixed block effect for patient

6.6.1 Estimate the model

We estimate the model with msqrob(). Recall that variables defined in model are automatically retrieved from the colData (i.e. "tissue", "location", and "patient").

pe <- msqrob(
  pe, i = "proteins", formula = model, robust = TRUE, ridge = TRUE
)

6.7 Statistical inference

Once the models are estimated, we can start answering biological questions by performing Statistical inference. We must translate the biological questions into a statistical hypotheses:

  1. Is there an effect of tissue type (V-A) in the left heart region?
  2. Is there an effect of tissue type (V-A) in the right heart region?
  3. Is there on average an effect of tissue type in the heart.
  4. Does the effect of tissue type (V-A) differ according to the heart region (L-R)?

In other words, we must translate these questions in a linear combination of the model parameters, also referred to as a contrast. To aid defining contrasts, we will visualise the experimental design using the ExploreModelMatrix package.

library("ExploreModelMatrix")
vd <- VisualizeDesign(
    sampleData =  colData(pe),
    designFormula = ~ location*tissue + patient,
    textSizeFitted = 4
)
vd$plotlist
$`location = L`


$`location = R`

6.7.1 Research question 1: is there an effect of tissue in the left heart region?

From the plot we can see that the average log2 intensity for patient 3 in the left ventriculum equals `(Intercept) + tissueV’:

\[ \mu^L_{V,3} = \beta_0 + \beta_V \] and for the left atrium (Intercept):

\[ \mu^L_{A,3} = \beta_0 \] So the average \(\log_2 FC\) between atrium and ventriculum for patient 3 equals to parameter tissueV

\[ \log_2 FC_{V-A}^L = \mu^L_{V,3} -\mu^L_{A,3} = \beta_V \] The same can be seen for patient 4:

\[ \log_2 FC_{V-A}^L= \mu^L_{V,4} -\mu^L_{A,4} = \beta_0 + \beta_V + \beta_4 - (\beta_0 + \beta_4) = \beta_V \] So the parameter tissueV has the interpretation of the average \(\log_2 FC\) between ventriculum and atrium after correction for the patient effect, which quantifies the effect size for the first research hypothesis.

6.7.2 Research question 2: is there an effect of tissue in the right heart region?

When we use the same rationale for the right heart region, we can see that the average \(\log_2 FC\) between atrium and ventriculum upon correction for the patient effect equals tissueV + locationR:tissueV. So, it consists of the main effect for tissue and the location x tissue interaction.

We will illustrate this here for patient 4:

\[ \begin{array}{rcl} \log_2 FC_{V-A}^R& =&\mu^R_{V,4} -\mu^R_{A,4} \\ &=& \beta_0 + \beta_R + \beta_V + \beta_{R:V} + \beta_4 - (\beta_0 + \beta_R + \beta_4) \\ &=& \beta_V + \beta_{R:V} \end{array} \]

6.7.3 Research question 3: is there an effect of tissue on average in the heart?

This research question can be quantified by calculating the averaging the \(\log_2\) fold change between Ventriculum and Atrium over the left and right heart regions, which equals tissueV + 0.5*locationR:tissueV

\[ \begin{array}{rcl} (\log_2 FC_{V-A}^R + \log_2 FC_{V-A}^R)/ 2 &=& (\beta_V + \beta_V + \beta_{R:V})/2 \\ &=& \beta_V + 0.5\times\beta_{R:V} \end{array} \]

6.7.4 Research question 4: does the effect of tissue differs according to the heart region?

This research question can be quantified by calculating the difference in the \(\log_2\) fold change between Ventriculum and Atrium in the right and left heart regions, which equals locationR:tissueV

\[ \begin{array}{rcl} \log_2 FC_{V-A}^R- \log_2 FC_{V-A}^R &=& \beta_V + \beta_{R:V}-\beta_V \\ &=& \beta_{R:V} \end{array} \] ### Setting up the contrasts

We can set up the four contrasts:

  1. We make the design matrix so that we can easily extract all parameter names from the model
  2. We make the contrast matrix for the four contrasts
design <- model.matrix(~ location*tissue + patient, data = colData(pe))
L <- makeContrast(
  c(
    "ridgetissueV = 0",
    "ridgetissueV + ridgelocationR:tissueV = 0",
    "ridgetissueV + 0.5*ridgelocationR:tissueV = 0",
    "ridgelocationR:tissueV = 0"
  ),
  parameterNames = paste0("ridge", colnames(design))
  )

We can now falsify the null hypothesis of each contrast:

pe <- hypothesisTest(
  object = pe, i = "proteins", contrast = L, overwrite = TRUE
)

6.7.5 Evaluate results for contrast \(\log_2 FC_{V-A}^L\)

Let us retrieve the result table from the rowData. Note that the hypothesis testing results are stored in rowData columns named after the column names of the contrast matrix L. The first column contains the results for contrast \(\log_2 FC_{V-A}^L\).

inferenceLeft <- rowData(pe[["proteins"]])[[colnames(L)[1]]]
inferenceLeft$Protein <- rownames(inferenceLeft)
head(inferenceLeft)
             logFC           se        df          t      pval adjPval Protein
A0PJW6  0.00000000 5.509988e-10 14.003000  0.0000000 1.0000000       1  A0PJW6
A0PJZ3          NA           NA        NA         NA        NA      NA  A0PJZ3
A0PK00          NA           NA  5.826529         NA        NA      NA  A0PK00
A1A4S6  0.03452594 8.910408e-02 13.324734  0.3874788 0.7045204       1  A1A4S6
A1A5D9          NA           NA        NA         NA        NA      NA  A1A5D9
A1IGU5 -0.12078957 2.045995e-01 10.934858 -0.5903708 0.5669427       1  A1IGU5

Notice that some rows contain missing values. This is because data modelling resulted in a fitError for some proteins, probably because not enough data was available for model fitting due to missing values in the quantitative data (see how to deal with fitErrors).

Volcano plot

Volcano plots are straightforward to generate from the inference table above. We also use ggrepel to annotate the 20 most significant proteins.

ggplot(inferenceLeft) +
  aes(x = logFC, y = -log10(pval), color = adjPval < 0.05) +
  geom_point() +
  geom_text_repel(data = slice_min(inferenceLeft, adjPval, n = 20),
                  aes(label = Protein)) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  ggtitle("log2 FC V-A left",
          paste("Hypothesis test:", colnames(L)[1], "= 0"))

Heatmap

We can also build a heatmap for the significant proteins which are obtained by filtering the inference table. We first retrieve the data with proteins that are differentially abundant between the atrium and the ventriculum in the left heart.

sigNamesLeft <- inferenceLeft |> 
  filter(!is.na(adjPval), adjPval < 0.05) |> 
  pull()
se <- getWithColData(pe, "proteins")[sigNamesLeft, ]

We then plot the protein-wise standardised data as an annotated heatmap.

quants <- t(scale(t(assay(se))))
library("ComplexHeatmap")
annotations <- columnAnnotation(
  tissue = se$tissue,
  location = se$location
)
set.seed(1234) ## annotation colours are randomly generated by default
Heatmap(
 quants, name = "log2 intensity",
 top_annotation = annotations
)

There are 91 proteins significantly differentially expressed at the 5% FDR level. Below you can find the list of significant proteins.

inferenceLeft |>
  na.exclude() |>
  filter(adjPval<0.05) |>
  arrange(pval)  |>
  knitr::kable()
logFC se df t pval adjPval Protein
P08590 8.250133 0.4618098 9.066700 17.864783 0.0000000 0.0000501 P08590
P12883 4.683827 0.3506520 9.108910 13.357477 0.0000003 0.0003054 P12883
P10916 7.126335 0.4767443 7.093703 14.947920 0.0000013 0.0009474 P10916
P14854 2.370452 0.2234186 9.066347 10.609916 0.0000021 0.0011498 P14854
O94875-10 2.631801 0.2944480 9.230071 8.938084 0.0000076 0.0032078 O94875-10
Q6UWY5 -2.820577 0.3178826 9.147279 -8.873014 0.0000086 0.0032078 Q6UWY5
P51888 -2.532542 0.3183317 9.246559 -7.955671 0.0000197 0.0063061 P51888
P46821 -1.716407 0.2365827 9.150270 -7.255001 0.0000439 0.0103944 P46821
Q8N474 -2.758038 0.3551103 8.190289 -7.766709 0.0000475 0.0103944 Q8N474
P21810 -2.518077 0.3552103 9.230160 -7.088977 0.0000504 0.0103944 P21810
P02747 -2.301603 0.3282428 9.364965 -7.011892 0.0000511 0.0103944 P02747
O75368 -1.751397 0.2535261 9.191185 -6.908151 0.0000632 0.0117700 O75368
O14967 -1.916404 0.2835587 9.248331 -6.758403 0.0000728 0.0122440 O14967
P05546 -1.563360 0.2304074 9.089659 -6.785196 0.0000767 0.0122440 P05546
P29622 -1.544848 0.2320175 9.113749 -6.658327 0.0000876 0.0122901 P29622
Q9ULL5-3 -2.771336 0.3986314 8.505757 -6.952128 0.0000879 0.0122901 Q9ULL5-3
P18428 -1.750339 0.2688643 9.299328 -6.510121 0.0000950 0.0124912 P18428
P08294 -2.135352 0.3312298 9.307183 -6.446739 0.0001021 0.0126793 P08294
Q8TBQ9 -2.126623 0.3493479 9.446801 -6.087408 0.0001491 0.0169428 Q8TBQ9
P00325 -1.696574 0.2749298 9.177945 -6.170934 0.0001515 0.0169428 P00325
P15924 1.473571 0.2403994 9.110066 6.129679 0.0001644 0.0175054 P15924
Q9UBB5 2.681925 0.3323530 6.082236 8.069506 0.0001809 0.0183864 Q9UBB5
P06858 2.005077 0.3377476 9.148010 5.936613 0.0002052 0.0199517 P06858
P24844 -1.952282 0.3366253 9.433933 -5.799568 0.0002167 0.0201925 P24844
P24311 1.966170 0.3414895 9.362276 5.757630 0.0002356 0.0210760 P24311
Q5JVS0 -2.937575 0.3186422 5.020511 -9.219040 0.0002467 0.0210902 Q5JVS0
O95865 -1.567784 0.2751262 9.360986 -5.698419 0.0002547 0.0210902 O95865
P02452 -2.032726 0.3640935 9.585356 -5.582980 0.0002720 0.0211347 P02452
P13533 -3.159311 0.5620164 9.431456 -5.621385 0.0002741 0.0211347 P13533
Q9P2B2 -1.649939 0.2981357 9.357789 -5.534187 0.0003166 0.0229231 Q9P2B2
Q15113 -1.934718 0.3529607 9.536113 -5.481398 0.0003178 0.0229231 Q15113
P02743 -1.682963 0.3088914 9.259904 -5.448396 0.0003682 0.0257297 P02743
P23083 -3.595505 0.5864530 7.380951 -6.130935 0.0003865 0.0259912 P23083
P23434 1.426496 0.2644910 9.231040 5.393363 0.0004005 0.0259912 P23434
P04196 -1.424906 0.2661117 9.273169 -5.354540 0.0004154 0.0259912 P04196
Q9BW30 -2.113450 0.4001977 9.534123 -5.281013 0.0004185 0.0259912 Q9BW30
O43677 -2.187788 0.4161198 9.401692 -5.257592 0.0004526 0.0262839 O43677
Q9UKS6 1.527078 0.2923847 9.488160 5.222840 0.0004608 0.0262839 Q9UKS6
Q53GQ0 -1.766962 0.3426900 9.718619 -5.156155 0.0004684 0.0262839 Q53GQ0
P36955 -1.684290 0.3276053 9.772134 -5.141217 0.0004702 0.0262839 P36955
P05997 -2.292742 0.4438855 9.559204 -5.165165 0.0004876 0.0263397 P05997
P04209 1.537844 0.2981332 9.495921 5.158245 0.0005029 0.0263397 P04209
Q14764 -1.154973 0.2205762 9.165409 -5.236163 0.0005065 0.0263397 Q14764
O95631 -3.641005 0.4782087 5.133102 -7.613842 0.0005523 0.0280667 O95631
P51884 -1.625915 0.3221562 9.576049 -5.046979 0.0005730 0.0283194 P51884
P08582 -1.400068 0.2752535 9.355058 -5.086468 0.0005826 0.0283194 P08582
Q16647 -1.821645 0.3658315 9.742336 -4.979465 0.0005991 0.0285012 Q16647
P19429 2.446925 0.4900303 9.586884 4.993416 0.0006163 0.0287104 P19429
Q6YN16 1.372126 0.2728193 9.276732 5.029431 0.0006473 0.0289927 Q6YN16
P01699 -3.706945 0.5955534 6.325570 -6.224371 0.0006483 0.0289927 P01699
Q9UNW9 3.556427 0.7181405 9.536696 4.952272 0.0006641 0.0291146 Q9UNW9
P14923 1.046597 0.2091414 9.171357 5.004255 0.0006940 0.0293444 P14923
P04083 -1.155807 0.2326322 9.318065 -4.968389 0.0006956 0.0293444 P04083
Q96LL9 -1.832427 0.3733546 9.526194 -4.908006 0.0007099 0.0293942 Q96LL9
O95980 -1.644847 0.3372152 9.498408 -4.877737 0.0007478 0.0304004 O95980
Q00G26 1.468999 0.2999726 9.271814 4.897112 0.0007803 0.0311574 Q00G26
Q13011 1.343312 0.2779008 9.478440 4.833782 0.0008015 0.0314398 Q13011
Q9BX66-5 1.645228 0.3003872 7.236228 5.477024 0.0008305 0.0320157 Q9BX66-5
Q9UBG0 -1.737280 0.3711535 9.768266 -4.680758 0.0009230 0.0346124 Q9UBG0
Q9NVN8 -5.198643 0.7881637 5.337400 -6.595893 0.0009338 0.0346124 Q9NVN8
P12110 -1.294302 0.2752573 9.536991 -4.702153 0.0009541 0.0346124 P12110
P17540 1.186827 0.2524879 9.408560 4.700530 0.0009922 0.0346124 P17540
Q8WZ42-6 1.025527 0.2181058 9.391830 4.701968 0.0009949 0.0346124 Q8WZ42-6
Q9UL18 -1.765694 0.3684251 8.951891 -4.792544 0.0009988 0.0346124 Q9UL18
A6NDG6 1.268609 0.2689144 9.276465 4.717521 0.0010062 0.0346124 A6NDG6
Q9Y4W6 1.073065 0.2294084 9.235088 4.677530 0.0010787 0.0365449 Q9Y4W6
P24298 1.544611 0.3397789 9.384519 4.545929 0.0012525 0.0418012 P24298
P46063 -1.073446 0.2365976 9.340311 -4.537009 0.0012845 0.0422366 P46063
P36021 -2.202548 0.4760910 8.828176 -4.626317 0.0013081 0.0423886 P36021
Q5NDL2 -1.747813 0.3912896 9.517072 -4.466802 0.0013600 0.0429751 Q5NDL2
Q13636 -2.478227 0.4646276 6.499442 -5.333792 0.0013751 0.0429751 Q13636
P02775 -1.516837 0.3394756 9.442684 -4.468178 0.0013838 0.0429751 P02775
Q9BXN1 -1.948674 0.4430265 9.779246 -4.398551 0.0014117 0.0432408 Q9BXN1
Q9NZ01 -1.568868 0.3603847 9.896186 -4.353317 0.0014718 0.0436957 Q9NZ01
Q92736-2 -2.605932 0.5910197 9.526607 -4.409213 0.0014794 0.0436957 Q92736-2
O60760 -2.498746 0.5569448 9.077881 -4.486523 0.0014858 0.0436957 O60760
Q06828 -3.276730 0.7507098 9.730028 -4.364843 0.0015047 0.0436957 Q06828
O75629 1.543697 0.3542839 9.693237 4.357233 0.0015361 0.0437116 O75629
Q9HBL0 1.646254 0.3543908 8.180895 4.645306 0.0015594 0.0437116 Q9HBL0
O00180 -3.220740 0.7131678 8.732659 -4.516103 0.0015702 0.0437116 O00180
P06727 -1.098656 0.2488030 9.225409 -4.415767 0.0015835 0.0437116 P06727
Q8WZA9 -1.005633 0.2319000 9.553531 -4.336494 0.0016406 0.0447356 Q8WZA9
P02776 -1.337487 0.3054296 9.240633 -4.379034 0.0016652 0.0448599 P02776
Q9UGT4 -1.651943 0.3842304 9.601092 -4.299354 0.0017164 0.0456878 Q9UGT4
P07451 -1.114780 0.2610269 9.585814 -4.270745 0.0017998 0.0465444 P07451
Q96H79 -2.280431 0.4560710 6.634593 -5.000166 0.0018317 0.0465444 Q96H79
P02671 -1.686681 0.3221285 6.105609 -5.236051 0.0018443 0.0465444 P02671
Q86VU5 1.516645 0.3572766 9.638787 4.245018 0.0018493 0.0465444 Q86VU5
Q5JPH6 3.579987 0.7035217 6.397711 5.088666 0.0018571 0.0465444 Q5JPH6
P35754 1.382605 0.3285348 9.831307 4.208396 0.0018734 0.0465444 P35754
Q9BXV9 1.608759 0.3856887 9.805041 4.171135 0.0019979 0.0490914 Q9BXV9

6.7.6 Evaluate results for contrast \(\log_2 FC_{V-A}^R\)

Let us retrieve the result table from the rowData. The second column contains the results for contrast \(\log_2 FC_{V-A}^R\).

inferenceRight <- rowData(pe[["proteins"]])[[colnames(L)[2]]]
inferenceRight$Protein <- rownames(inferenceRight)
head(inferenceRight)
             logFC           se        df          t       pval   adjPval
A0PJW6  0.00000000 5.509988e-10 14.003000  0.0000000 1.00000000 1.0000000
A0PJZ3          NA           NA        NA         NA         NA        NA
A0PK00          NA           NA  5.826529         NA         NA        NA
A1A4S6  0.04979859 8.881359e-02 13.324734  0.5607091 0.58430170 1.0000000
A1A5D9          NA           NA        NA         NA         NA        NA
A1IGU5 -0.40996946 2.015378e-01 10.934858 -2.0342065 0.06691788 0.3697382
       Protein
A0PJW6  A0PJW6
A0PJZ3  A0PJZ3
A0PK00  A0PK00
A1A4S6  A1A4S6
A1A5D9  A1A5D9
A1IGU5  A1IGU5

Volcano plot

Volcano plots are straightforward to generate from the inference table above. We also use ggrepel to annotate the 20 most significant proteins.

ggplot(inferenceRight) +
  aes(x = logFC, y = -log10(pval), color = adjPval < 0.05) +
  geom_point() +
  geom_text_repel(data = slice_min(inferenceRight, adjPval, n = 20),
                  aes(label = Protein)) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  ggtitle("log2 FC V-A Right",
          paste("Hypothesis test:", colnames(L)[2], "= 0"))

Heatmap

We can also build a heatmap for the significant proteins which are obtained by filtering the inference table1.

sigNamesRight <- inferenceRight |> 
  filter(!is.na(adjPval), adjPval < 0.05) |> 
  pull()
se <- getWithColData(pe, "proteins")[sigNamesRight, ]
quants <- t(scale(t(assay(se))))
set.seed(1234) ## annotation colours are randomly generated by default
Heatmap(
 quants, name = "log2 intensity",
 top_annotation = annotations
)

There are 59 proteins significantly differentially expressed at the 5% FDR level.

Below you can find the list of significant proteins.

inferenceRight |>
  na.exclude() |>
  filter(adjPval<0.05) |>
  arrange(pval)  |>
  knitr::kable()
logFC se df t pval adjPval Protein
P08590 5.493574 0.4626211 9.066700 11.874888 0.0000008 0.0015317 P08590
P06858 3.674709 0.3338983 9.148010 11.005474 0.0000014 0.0015317 P06858
P48163 -2.616881 0.3097355 9.233764 -8.448762 0.0000121 0.0088573 P48163
P02776 -2.374280 0.2974779 9.240633 -7.981367 0.0000193 0.0091147 P02776
Q9ULD0 -3.097487 0.3131518 7.104538 -9.891328 0.0000208 0.0091147 Q9ULD0
Q00G26 2.200504 0.2999726 9.271814 7.335685 0.0000375 0.0136795 Q00G26
P54652 -2.287138 0.3255640 9.325197 -7.025158 0.0000515 0.0137287 P54652
Q6UWY5 -2.154394 0.3049324 9.147279 -7.065153 0.0000542 0.0137287 Q6UWY5
P11586 2.100538 0.3026749 9.333226 6.939915 0.0000565 0.0137287 P11586
P21810 -2.331556 0.3429892 9.230160 -6.797753 0.0000702 0.0153595 P21810
Q04760 2.281002 0.3674889 9.441126 6.206996 0.0001286 0.0208338 Q04760
P23434 1.656292 0.2644910 9.231040 6.262189 0.0001323 0.0208338 P23434
P24298 2.252174 0.3642189 9.384519 6.183572 0.0001358 0.0208338 P24298
P60468 -2.014787 0.3022799 8.191586 -6.665302 0.0001422 0.0208338 P60468
P05546 -1.388646 0.2220578 9.089659 -6.253532 0.0001428 0.0208338 P05546
Q6YN16 1.728901 0.2895814 9.276732 5.970346 0.0001861 0.0237989 Q6YN16
P13533 -3.148481 0.5388200 9.431456 -5.843288 0.0002050 0.0237989 P13533
Q69YU5 3.065356 0.5307692 9.579396 5.775308 0.0002110 0.0237989 Q69YU5
A6NDG6 1.557838 0.2665039 9.276465 5.845460 0.0002180 0.0237989 A6NDG6
P04004 -1.609110 0.2765621 9.270800 -5.818258 0.0002263 0.0237989 P04004
P28066 -1.685066 0.2929535 9.457629 -5.751991 0.0002284 0.0237989 P28066
P12883 1.989033 0.3417758 9.108910 5.819703 0.0002417 0.0240397 P12883
O43677 -2.375819 0.4202739 9.401692 -5.653026 0.0002660 0.0253014 O43677
P23786 1.210900 0.2128018 9.134748 5.690270 0.0002820 0.0257048 P23786
P10916 3.113228 0.4767443 7.093703 6.530185 0.0003069 0.0268623 P10916
P35625 -3.190450 0.5540567 8.562007 -5.758346 0.0003299 0.0277622 P35625
P30711 -1.692001 0.3157636 9.492387 -5.358443 0.0003818 0.0295584 P30711
P14854 1.259547 0.2300868 9.066347 5.474224 0.0003831 0.0295584 P14854
P29622 -1.296379 0.2383018 9.113749 -5.440070 0.0003935 0.0295584 P29622
Q15327 -1.566469 0.2941564 9.451901 -5.325292 0.0004053 0.0295584 Q15327
Q9NRG4 2.572149 0.4631363 8.484155 5.553763 0.0004377 0.0308916 Q9NRG4
O75368 -1.320075 0.2537600 9.191185 -5.202062 0.0005256 0.0344087 O75368
Q5NDL2 -2.083671 0.4071966 9.517072 -5.117114 0.0005290 0.0344087 Q5NDL2
P01031 -1.205507 0.2348255 9.289894 -5.133627 0.0005579 0.0344087 P01031
Q6PCB0 -1.734824 0.3399387 9.378729 -5.103342 0.0005646 0.0344087 Q6PCB0
P61925 2.051193 0.3786314 8.266540 5.417388 0.0005661 0.0344087 P61925
A6NMZ7 -2.357794 0.4729659 9.707078 -4.985124 0.0006007 0.0348990 A6NMZ7
Q5JPH6 3.345723 0.5352998 6.397711 6.250186 0.0006061 0.0348990 Q5JPH6
Q9P2B2 -1.483082 0.2953969 9.357789 -5.020641 0.0006381 0.0358009 Q9P2B2
Q9HAT2 2.033947 0.4116210 9.296680 4.941310 0.0007275 0.0385182 Q9HAT2
Q9UGT4 -1.864637 0.3842304 9.601092 -4.852913 0.0007514 0.0385182 Q9UGT4
P23142 -2.130549 0.4396656 9.545973 -4.845840 0.0007718 0.0385182 P23142
P08294 -1.614308 0.3320461 9.307183 -4.861700 0.0008114 0.0385182 P08294
Q14764 -1.079085 0.2205762 9.165409 -4.892120 0.0008127 0.0385182 Q14764
P35052 -1.566974 0.3100259 8.489597 -5.054332 0.0008208 0.0385182 P35052
P51888 -1.505290 0.3098730 9.246559 -4.857764 0.0008314 0.0385182 P51888
Q9HCB6 -1.756881 0.3660009 9.478445 -4.800210 0.0008413 0.0385182 Q9HCB6
P02775 -1.630806 0.3394307 9.442684 -4.804531 0.0008450 0.0385182 P02775
Q8N142 1.394024 0.2921992 9.488674 4.770801 0.0008753 0.0390833 Q8N142
Q9Y4W6 1.102812 0.2294084 9.235088 4.807199 0.0008963 0.0392226 Q9Y4W6
P48681 -1.101098 0.2312917 9.238145 -4.760646 0.0009568 0.0410483 P48681
P46821 -1.120872 0.2355359 9.150270 -4.758817 0.0009851 0.0412685 P46821
Q9Y6X5 -1.159408 0.2466137 9.378833 -4.701313 0.0009996 0.0412685 Q9Y6X5
Q9BSD7 2.569845 0.5189540 7.936523 4.951970 0.0011446 0.0463795 Q9BSD7
Q06828 -3.298913 0.7287820 9.730028 -4.526612 0.0011758 0.0467768 Q06828
P51970 1.071905 0.2350925 9.453305 4.559505 0.0012049 0.0470148 P51970
Q6UWS5 1.979951 0.3609481 6.427167 5.485418 0.0012248 0.0470148 Q6UWS5
P10109 1.211344 0.2678468 9.462988 4.522524 0.0012695 0.0478898 P10109
Q9NRX4 1.605283 0.3585462 9.576287 4.477201 0.0013185 0.0488954 Q9NRX4

6.7.7 Evaluate results average contrast \(\log_2 FC_{V-A}\)

Let us retrieve the result table from the rowData. The second column contains the results for contrast \(\log_2 FC_{V-A}\).

inferenceAvg <- rowData(pe[["proteins"]])[[colnames(L)[3]]]
inferenceAvg$Protein <- rownames(inferenceAvg)
head(inferenceAvg)
             logFC           se        df          t       pval   adjPval
A0PJW6  0.00000000 3.896150e-10 14.003000  0.0000000 1.00000000 1.0000000
A0PJZ3          NA           NA        NA         NA         NA        NA
A0PK00          NA           NA  5.826529         NA         NA        NA
A1A4S6  0.04216227 6.290247e-02 13.324734  0.6702799 0.51412912 1.0000000
A1A5D9          NA           NA        NA         NA         NA        NA
A1IGU5 -0.26537952 1.435953e-01 10.934858 -1.8481073 0.09179013 0.3358475
       Protein
A0PJW6  A0PJW6
A0PJZ3  A0PJZ3
A0PK00  A0PK00
A1A4S6  A1A4S6
A1A5D9  A1A5D9
A1IGU5  A1IGU5

Volcano plot

Volcano plots are straightforward to generate from the inference table above. We also use ggrepel to annotate the 20 most significant proteins.

ggplot(inferenceAvg) +
  aes(x = logFC, y = -log10(pval), color = adjPval < 0.05) +
  geom_point() +
  geom_text_repel(data = slice_min(inferenceAvg, adjPval, n = 20),
                  aes(label = Protein)) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  ggtitle("log2 FC V-A Right",
          paste("Hypothesis test:", colnames(L)[2], "= 0"))

Heatmap

We can also build a heatmap for the significant proteins which are obtained by filtering the inference table.

sigNamesAvg <- inferenceAvg |> 
  filter(!is.na(adjPval), adjPval < 0.05) |> 
  pull()
se <- getWithColData(pe, "proteins")[sigNamesAvg, ]
quants <- t(scale(t(assay(se))))
set.seed(1234) ## annotation colours are randomly generated by default
Heatmap(
 quants, name = "log2 intensity",
 top_annotation = annotations
)

There are 264 proteins significantly differentially expressed at the 5% FDR level.

Below you can find the list of significant proteins.

inferenceAvg |>
  na.exclude() |>
  filter(adjPval<0.05) |>
  arrange(pval)  |>
  knitr::kable()
logFC se df t pval adjPval Protein
P08590 6.8718531 0.3268358 9.066700 21.025398 0.0000000 0.0000115 P08590
P12883 3.3364301 0.2448303 9.108910 13.627520 0.0000002 0.0002510 P12883
P06858 2.8398932 0.2374666 9.148010 11.959126 0.0000007 0.0004333 P06858
P10916 5.1197819 0.3298586 7.093703 15.521141 0.0000010 0.0004333 P10916
Q6UWY5 -2.4874855 0.2202460 9.147279 -11.294126 0.0000011 0.0004333 Q6UWY5
P14854 1.8149997 0.1603557 9.066347 11.318585 0.0000012 0.0004333 P14854
P21810 -2.4248168 0.2462386 9.230160 -9.847427 0.0000034 0.0010530 P21810
P05546 -1.4760026 0.1599979 9.089659 -9.225140 0.0000065 0.0014784 P05546
P51888 -2.0189161 0.2221104 9.246559 -9.089697 0.0000065 0.0014784 P51888
O94875-10 1.9967297 0.2201929 9.230071 9.068093 0.0000068 0.0014784 O94875-10
P02776 -1.8558833 0.2131785 9.240633 -8.705771 0.0000094 0.0017739 P02776
Q00G26 1.8347517 0.2121126 9.271814 8.649893 0.0000097 0.0017739 Q00G26
O75368 -1.5357360 0.1791590 9.191185 -8.571914 0.0000111 0.0017804 O75368
P29622 -1.4206134 0.1659883 9.113749 -8.558513 0.0000119 0.0017804 P29622
P46821 -1.4186399 0.1668418 9.150270 -8.502903 0.0000122 0.0017804 P46821
P13533 -3.1538958 0.3885644 9.431456 -8.116790 0.0000149 0.0019214 P13533
P23434 1.5413943 0.1870234 9.231040 8.241720 0.0000149 0.0019214 P23434
P48163 -1.7231690 0.2140965 9.233764 -8.048562 0.0000181 0.0021010 P48163
P08294 -1.8748304 0.2345078 9.307183 -7.994747 0.0000182 0.0021010 P08294
Q8N474 -2.2252733 0.2593043 8.190289 -8.581705 0.0000227 0.0022887 Q8N474
Q6YN16 1.5505133 0.1989270 9.276732 7.794385 0.0000229 0.0022887 Q6YN16
O43677 -2.2818039 0.2956909 9.401692 -7.716855 0.0000230 0.0022887 O43677
P54652 -1.7712555 0.2302085 9.325197 -7.694137 0.0000247 0.0023452 P54652
P24298 1.8983921 0.2490507 9.384519 7.622512 0.0000257 0.0023452 P24298
P18428 -1.4437423 0.1912298 9.299328 -7.549776 0.0000293 0.0025615 P18428
Q9P2B2 -1.5665103 0.2098477 9.357789 -7.464987 0.0000310 0.0026079 Q9P2B2
A6NDG6 1.4132236 0.1893082 9.276465 7.465199 0.0000325 0.0026334 A6NDG6
P15924 1.1865540 0.1629059 9.110066 7.283678 0.0000436 0.0034053 P15924
Q14764 -1.1170292 0.1559709 9.165409 -7.161778 0.0000482 0.0036399 Q14764
P04004 -1.3605995 0.1939297 9.270800 -7.015941 0.0000536 0.0039063 P04004
P24844 -1.6335444 0.2377501 9.433933 -6.870845 0.0000580 0.0040359 P24844
P11586 1.4562651 0.2117089 9.333226 6.878620 0.0000606 0.0040359 P11586
Q5NDL2 -1.9157423 0.2823635 9.517072 -6.784667 0.0000615 0.0040359 Q5NDL2
P60468 -1.4985317 0.2011269 8.191586 -7.450677 0.0000642 0.0040359 P60468
Q69YU5 2.4754644 0.3685265 9.579396 6.717195 0.0000646 0.0040359 Q69YU5
Q9BW30 -1.9177197 0.2869707 9.534123 -6.682633 0.0000688 0.0041828 Q9BW30
Q15113 -1.6629972 0.2516710 9.536113 -6.607821 0.0000752 0.0043619 Q15113
P04196 -1.2482839 0.1860851 9.273169 -6.708133 0.0000762 0.0043619 P04196
Q9Y4W6 1.0879384 0.1622163 9.235088 6.706716 0.0000778 0.0043619 Q9Y4W6
P19429 2.2476195 0.3448005 9.586884 6.518608 0.0000818 0.0043619 P19429
P02747 -1.4838172 0.2247118 9.364965 -6.603201 0.0000823 0.0043619 P02747
P02775 -1.5738214 0.2400296 9.442684 -6.556779 0.0000837 0.0043619 P02775
Q9UGT4 -1.7582895 0.2716919 9.601092 -6.471630 0.0000861 0.0043791 Q9UGT4
P02743 -1.4372727 0.2184192 9.259904 -6.580340 0.0000891 0.0044302 P02743
Q9NRG4 2.4292862 0.3537262 8.484155 6.867703 0.0000974 0.0047344 Q9NRG4
Q06828 -3.2878219 0.5231368 9.730028 -6.284823 0.0001024 0.0048709 Q06828
P01031 -1.0556049 0.1655342 9.289894 -6.376960 0.0001120 0.0052117 P01031
Q9HCB6 -1.5477618 0.2477605 9.478445 -6.247007 0.0001203 0.0054690 Q9HCB6
P05997 -1.9099570 0.3080473 9.559204 -6.200206 0.0001230 0.0054690 P05997
P02452 -1.6021991 0.2593204 9.585356 -6.178453 0.0001250 0.0054690 P02452
P48681 -1.0102767 0.1617208 9.238145 -6.247042 0.0001343 0.0057622 P48681
O14967 -1.2432881 0.2005888 9.248331 -6.198193 0.0001419 0.0059021 O14967
O00180 -3.4637187 0.5425840 8.732659 -6.383747 0.0001457 0.0059021 O00180
P23142 -1.8311086 0.3022843 9.545973 -6.057572 0.0001483 0.0059021 P23142
Q9HAT2 1.7914105 0.2921487 9.296680 6.131845 0.0001507 0.0059021 Q9HAT2
P10109 1.1498726 0.1893963 9.462988 6.071252 0.0001511 0.0059021 P10109
Q13011 1.1833804 0.1965056 9.478440 6.022121 0.0001597 0.0061310 Q13011
Q8N142 1.2230990 0.2045058 9.488674 5.980754 0.0001677 0.0063249 Q8N142
Q9UNW9 3.0434487 0.5128825 9.536696 5.934007 0.0001745 0.0063613 Q9UNW9
Q9NRX4 1.5004765 0.2535304 9.576287 5.918329 0.0001752 0.0063613 Q9NRX4
A6NMZ7 -1.9793762 0.3373225 9.707078 -5.867904 0.0001773 0.0063613 A6NMZ7
P23786 0.9013183 0.1492546 9.134748 6.038796 0.0001818 0.0064157 P23786
Q04760 1.5360653 0.2602426 9.441126 5.902435 0.0001892 0.0064537 Q04760
Q15327 -1.2106705 0.2054754 9.451901 -5.892044 0.0001909 0.0064537 Q15327
P17540 1.0681187 0.1809634 9.408560 5.902403 0.0001918 0.0064537 P17540
O95865 -1.1476181 0.1943037 9.360986 -5.906311 0.0001947 0.0064537 O95865
Q5JPH6 3.4628551 0.4565052 6.397711 7.585577 0.0002001 0.0065352 Q5JPH6
P51884 -1.3320008 0.2297628 9.576049 -5.797287 0.0002052 0.0066034 P51884
P04083 -0.9538541 0.1644374 9.318065 -5.800712 0.0002269 0.0071964 P04083
P30711 -1.3399014 0.2344998 9.492387 -5.713869 0.0002368 0.0073379 P30711
Q7L4S7 -1.7163672 0.2582743 7.332929 -6.645522 0.0002381 0.0073379 Q7L4S7
O95980 -1.3087089 0.2312413 9.498408 -5.659495 0.0002539 0.0077157 O95980
P00325 -1.1271284 0.1975885 9.177945 -5.704422 0.0002721 0.0081541 P00325
Q9UKS6 1.1555353 0.2067472 9.488160 5.589122 0.0002799 0.0081958 Q9UKS6
P28066 -1.1657717 0.2083507 9.457629 -5.595237 0.0002809 0.0081958 P28066
Q9ULD0 -1.6081853 0.2436750 7.104538 -6.599713 0.0002855 0.0082204 Q9ULD0
Q86VU5 1.3909551 0.2522544 9.638787 5.514095 0.0002927 0.0083166 Q86VU5
P14923 0.8397640 0.1494538 9.171357 5.618887 0.0003046 0.0085450 P14923
Q6PCB0 -1.3159366 0.2380883 9.378729 -5.527094 0.0003170 0.0087809 Q6PCB0
P24311 1.3296314 0.2409955 9.362276 5.517246 0.0003232 0.0088407 P24311
Q9HBL0 1.3884913 0.2368572 8.180895 5.862145 0.0003467 0.0092101 Q9HBL0
Q9BXV9 1.4577749 0.2727782 9.805041 5.344177 0.0003488 0.0092101 Q9BXV9
Q9ULL5-3 -1.7449314 0.3044597 8.505757 -5.731239 0.0003494 0.0092101 Q9ULL5-3
P61925 1.5601183 0.2689974 8.266540 5.799751 0.0003583 0.0093340 P61925
P23083 -2.6793814 0.4331242 7.380951 -6.186173 0.0003652 0.0094013 P23083
O75489 0.9977662 0.1872393 9.598971 5.328829 0.0003829 0.0096301 O75489
P63316 0.9567764 0.1785086 9.478611 5.359834 0.0003829 0.0096301 P63316
P07195 0.9985697 0.1874549 9.501868 5.326987 0.0003972 0.0098767 P07195
P35625 -2.1754244 0.3893925 8.562007 -5.586713 0.0004068 0.0100014 P35625
P17174 1.0116885 0.1913267 9.468860 5.287755 0.0004242 0.0103116 P17174
O15230 -0.8483405 0.1605308 9.335882 -5.284596 0.0004465 0.0107248 O15230
Q6PI78 1.4696344 0.2848643 9.822092 5.159069 0.0004510 0.0107248 Q6PI78
Q9BXN1 -1.6449225 0.3195425 9.779246 -5.147743 0.0004648 0.0109342 Q9BXN1
Q9Y6X5 -0.9114382 0.1743822 9.378833 -5.226669 0.0004760 0.0110806 Q9Y6X5
Q16647 -1.3098697 0.2578596 9.742336 -5.079778 0.0005184 0.0119394 Q16647
P21399 0.7733919 0.1506412 9.280176 5.134001 0.0005594 0.0127385 P21399
P51970 0.8453891 0.1662355 9.453305 5.085491 0.0005647 0.0127385 P51970
Q96RP7 -3.2449509 0.4617082 5.483644 -7.028142 0.0006117 0.0136580 Q96RP7
Q9Y3B4 -0.8676537 0.1727639 9.303225 -5.022194 0.0006482 0.0143256 Q9Y3B4
P50453 -0.7669119 0.1527314 9.251199 -5.021311 0.0006602 0.0144443 P50453
Q6DKK2 1.0463979 0.2120923 9.412715 4.933692 0.0007089 0.0153565 Q6DKK2
P02748 -0.9028985 0.1827215 9.328466 -4.941392 0.0007201 0.0154467 P02748
Q8TBQ9 -1.2020325 0.2459524 9.446801 -4.887257 0.0007494 0.0159192 Q8TBQ9
Q9NZ01 -1.2126719 0.2534197 9.896186 -4.785231 0.0007618 0.0160279 Q9NZ01
P12110 -0.9713556 0.2007269 9.536991 -4.839189 0.0007814 0.0161672 P12110
P12814 -1.3411812 0.2670430 8.741139 -5.022341 0.0007832 0.0161672 P12814
P04003 -1.0633338 0.2197570 9.469818 -4.838681 0.0007979 0.0163158 P04003
Q53FA7 1.5581378 0.3239189 9.564359 4.810272 0.0008082 0.0163742 Q53FA7
P00748 -1.1346342 0.2388519 9.766668 -4.750367 0.0008324 0.0167096 P00748
P14550 -0.7790248 0.1630649 9.485559 -4.777390 0.0008677 0.0171682 P14550
Q9UBB5 1.4018949 0.2313680 6.082236 6.059156 0.0008710 0.0171682 Q9UBB5
P11766 0.7921470 0.1663652 9.467043 4.761494 0.0008928 0.0172923 P11766
P06732 0.7828169 0.1641535 9.430106 4.768811 0.0008931 0.0172923 P06732
Q12996 -1.1900567 0.2231950 7.365790 -5.331914 0.0009195 0.0176483 Q12996
Q8TDB4 -1.8274152 0.3415970 7.269123 -5.349622 0.0009413 0.0178003 Q8TDB4
P36021 -1.7395850 0.3577586 8.828176 -4.862455 0.0009437 0.0178003 P36021
Q92604 -1.3127130 0.2837564 9.947381 -4.626197 0.0009548 0.0178558 Q92604
Q9Y3D0 1.6486483 0.2959909 6.670659 5.569929 0.0009933 0.0184189 Q9Y3D0
Q6P1L8 0.8425184 0.1817397 9.707064 4.635853 0.0010038 0.0184563 Q6P1L8
Q6UWS5 1.7220515 0.3040600 6.427167 5.663526 0.0010290 0.0186969 Q6UWS5
Q6SZW1 -1.4203348 0.2792520 7.758278 -5.086212 0.0010392 0.0186969 Q6SZW1
P51151 0.7775244 0.1671543 9.434716 4.651537 0.0010578 0.0186969 P51151
Q53GQ0 -1.1175142 0.2430407 9.718619 -4.598054 0.0010590 0.0186969 Q53GQ0
P30405 -1.0553674 0.2310692 9.892316 -4.567322 0.0010596 0.0186969 P30405
P36955 -1.0735843 0.2345867 9.772134 -4.576493 0.0010784 0.0187156 P36955
P35754 1.0604547 0.2323092 9.831307 4.564842 0.0010807 0.0187156 P35754
Q14195-2 -1.5280180 0.3350901 9.839393 -4.560022 0.0010863 0.0187156 Q14195-2
Q5VUM1 0.9061256 0.1979080 9.589831 4.578519 0.0011288 0.0192961 Q5VUM1
P13667 -0.7220582 0.1596332 9.557580 -4.523233 0.0012366 0.0209741 P13667
Q9NQ50 0.8058197 0.1789252 9.618824 4.503668 0.0012531 0.0209972 Q9NQ50
Q00688 0.8160471 0.1814802 9.643216 4.496617 0.0012584 0.0209972 Q00688
O95182 0.8368612 0.1862421 9.636518 4.493405 0.0012667 0.0209972 O95182
Q8WZ42-6 0.6975568 0.1542241 9.391830 4.523007 0.0012931 0.0212722 Q8WZ42-6
Q9ULC3 -0.8541539 0.1918910 9.768835 -4.451245 0.0013056 0.0213180 Q9ULC3
O94919 -0.6975347 0.1543245 9.271008 -4.519921 0.0013422 0.0215226 O94919
Q9UKX3 1.2122113 0.2751758 9.937136 4.405225 0.0013445 0.0215226 Q9UKX3
P46060 -1.2255778 0.2663746 8.847600 -4.600957 0.0013476 0.0215226 P46060
Q86VP6 -0.8480596 0.1934764 9.783783 -4.383271 0.0014437 0.0228894 Q86VP6
P80723 -1.1832701 0.2565319 8.478989 -4.612565 0.0014826 0.0231809 P80723
P13073 0.7406291 0.1679121 9.498815 4.410813 0.0014863 0.0231809 P13073
Q63HM9 0.8199517 0.1857191 9.454228 4.415009 0.0014938 0.0231809 Q63HM9
Q5M9N0 -1.6991316 0.3824461 9.175048 -4.442800 0.0015423 0.0235916 Q5M9N0
P25940 -0.9714255 0.2245491 9.837648 -4.326117 0.0015568 0.0235916 P25940
O14949 1.0203567 0.2356108 9.770618 4.330688 0.0015706 0.0235916 O14949
Q5T481 0.7543759 0.1728985 9.531949 4.363114 0.0015840 0.0235916 Q5T481
Q5JUQ0 2.3923426 0.5129180 8.057125 4.664182 0.0015843 0.0235916 Q5JUQ0
Q12988 -0.7197658 0.1648005 9.502808 -4.367499 0.0015850 0.0235916 Q12988
P14543 -0.8237268 0.1917450 9.883607 -4.295950 0.0016142 0.0238635 P14543
P15848 0.9610948 0.2235365 9.748276 4.299497 0.0016569 0.0242835 P15848
P48047 0.7986239 0.1848261 9.569504 4.320948 0.0016733 0.0242835 P48047
P35052 -0.9094996 0.2011858 8.489597 -4.520695 0.0016759 0.0242835 P35052
P54296 0.6696406 0.1553053 9.514121 4.311768 0.0017199 0.0247578 P54296
Q15274 -1.2921878 0.2941256 9.004136 -4.393319 0.0017352 0.0247789 Q15274
O60503 1.4353510 0.2857061 6.681999 5.023872 0.0017484 0.0247789 O60503
P01034 -0.9479850 0.2241187 9.971490 -4.229835 0.0017554 0.0247789 P01034
Q96H79 -1.4874612 0.2966075 6.634593 -5.014914 0.0018028 0.0251508 Q96H79
O14980 -0.6307124 0.1452780 9.144414 -4.341416 0.0018047 0.0251508 O14980
Q9BUF5 -1.0681765 0.2531304 9.885958 -4.219867 0.0018176 0.0251700 Q9BUF5
P07585 -1.3970312 0.3326320 9.953888 -4.199930 0.0018475 0.0253056 P07585
P01042 -1.8796814 0.4047966 7.668959 -4.643520 0.0018550 0.0253056 P01042
P24752 0.8147048 0.1944004 9.964493 4.190859 0.0018699 0.0253056 P24752
Q8WY22 -0.9915506 0.2269833 8.852505 -4.368386 0.0018736 0.0253056 Q8WY22
Q86SX6 0.7916631 0.1879923 9.736249 4.211146 0.0019058 0.0255827 Q86SX6
Q86WV6 -0.8075576 0.1910670 9.427466 -4.226569 0.0019992 0.0264446 Q86WV6
Q9H479 0.7577083 0.1810834 9.704367 4.184305 0.0020016 0.0264446 Q9H479
Q53GG5 1.1080565 0.2648156 9.694276 4.184258 0.0020063 0.0264446 Q53GG5
Q9BTV4 -0.9341181 0.2278205 10.185239 -4.100238 0.0020631 0.0270307 Q9BTV4
Q9NNX1 2.9099745 0.6201504 7.088545 4.692369 0.0021547 0.0280621 Q9NNX1
P09619 -0.6550082 0.1570063 9.413215 -4.171860 0.0021806 0.0282314 P09619
Q8WWA0 -2.7659862 0.6806045 10.156219 -4.064014 0.0022007 0.0283248 Q8WWA0
P49770 0.8269399 0.2038694 9.944656 4.056224 0.0023268 0.0295944 P49770
Q02127 1.2039706 0.2657171 7.401140 4.531024 0.0023416 0.0295944 Q02127
I3L505 1.1067201 0.2753159 10.197875 4.019819 0.0023451 0.0295944 I3L505
Q9H3K6 0.6971910 0.1711668 9.728454 4.073168 0.0023699 0.0295944 Q9H3K6
P01024 -0.6372280 0.1551230 9.468507 -4.107887 0.0023758 0.0295944 P01024
O43920 0.7719300 0.1911945 9.980424 4.037406 0.0023805 0.0295944 O43920
P02461 -1.9014625 0.4756591 10.174961 -3.997532 0.0024428 0.0301970 P02461
P31930 0.6341469 0.1559075 9.566010 4.067457 0.0024760 0.0304356 P31930
Q13541 1.0054699 0.2514700 10.012338 3.998369 0.0025188 0.0305275 Q13541
Q9NQR4 0.8497236 0.2125583 10.005294 3.997603 0.0025254 0.0305275 Q9NQR4
Q9Y287 -0.8805657 0.2201911 9.991693 -3.999097 0.0025262 0.0305275 Q9Y287
P06727 -0.7439890 0.1815376 9.225409 -4.098264 0.0025471 0.0305275 P06727
P00352 -0.6833984 0.1679840 9.400007 -4.068236 0.0025644 0.0305275 P00352
P40939 0.6064551 0.1482708 9.244856 4.090186 0.0025672 0.0305275 P40939
P55039 1.0233844 0.2291242 7.304575 4.466505 0.0026220 0.0310109 P55039
Q04721 1.0485452 0.2622467 9.688477 3.998316 0.0026896 0.0315505 Q04721
P49458 -1.1981452 0.2798019 7.977794 -4.282120 0.0026970 0.0315505 P49458
P04209 0.8634678 0.2148474 9.495921 4.018982 0.0027109 0.0315505 P04209
Q15773 0.7582360 0.1916223 9.910180 3.956931 0.0027477 0.0316513 Q15773
P02790 -0.7830751 0.1991261 10.113121 -3.932558 0.0027485 0.0316513 P02790
P07451 -0.7379038 0.1847289 9.585814 -3.994523 0.0027639 0.0316617 P07451
Q53T59 -1.1978861 0.2885746 8.466573 -4.151045 0.0028322 0.0322757 Q53T59
P03928 0.7362835 0.1874644 9.915745 3.927591 0.0028776 0.0326230 P03928
Q9UBG0 -1.0333426 0.2624451 9.768266 -3.937366 0.0029158 0.0328547 Q9UBG0
Q96MM6 1.3370336 0.3311082 9.013572 4.038057 0.0029281 0.0328547 Q96MM6
Q9UMR3 -1.2814546 0.2782321 6.538023 -4.605703 0.0029441 0.0328657 Q9UMR3
Q92681 -1.0478169 0.2621030 9.184710 -3.997730 0.0029961 0.0331615 Q92681
Q02318 -1.0543306 0.2224723 6.136433 -4.739155 0.0030083 0.0331615 Q02318
Q9BX66-5 1.0070024 0.2303104 7.236228 4.372370 0.0030161 0.0331615 Q9BX66-5
O60760 -1.4579900 0.3648196 9.077881 -3.996469 0.0030733 0.0335841 O60760
Q14353 0.9837112 0.2560148 10.277210 3.842400 0.0030937 0.0335841 Q14353
O76031 0.5944195 0.1511239 9.488222 3.933326 0.0031046 0.0335841 O76031
P21953 0.9232656 0.2355721 9.579770 3.919248 0.0031159 0.0335841 P21953
P17050 -0.6604019 0.1688233 9.576790 -3.911793 0.0031547 0.0338359 P17050
O00264 -0.7449556 0.1928619 9.921595 -3.862637 0.0031930 0.0339604 O00264
P13671 -0.8057348 0.2080950 9.834309 -3.871957 0.0031974 0.0339604 P13671
P08574 0.6109407 0.1574461 9.718140 3.880317 0.0032258 0.0340964 P08574
Q8IUX7 -1.7161917 0.4188348 8.138665 -4.097538 0.0033255 0.0349822 Q8IUX7
Q96CS3 -0.7036507 0.1837872 9.754773 -3.828616 0.0034805 0.0364366 Q96CS3
Q9BZH6 1.9481367 0.5201485 10.412295 3.745347 0.0035553 0.0368470 Q9BZH6
P28070 -0.6488737 0.1693730 9.617848 -3.831034 0.0035579 0.0368470 P28070
Q3ZCW2 -0.8754815 0.2241369 9.021266 -3.906013 0.0035702 0.0368470 Q3ZCW2
P09874 0.5848364 0.1521501 9.439573 3.843813 0.0036089 0.0370716 P09874
Q9UBV8 -0.7235789 0.1896245 9.594672 -3.815851 0.0036613 0.0372603 Q9UBV8
Q8WZA9 -0.6588659 0.1724436 9.553531 -3.820763 0.0036613 0.0372603 Q8WZA9
P40261 -0.9050994 0.2361125 9.424468 -3.833340 0.0036798 0.0372752 P40261
Q16762 0.6896826 0.1829052 9.857793 3.770710 0.0037509 0.0376724 Q16762
O15195-2 1.3363469 0.3118800 6.902306 4.284811 0.0037535 0.0376724 O15195-2
Q9HAN9 1.1414801 0.2876745 8.335372 3.967958 0.0038043 0.0380082 Q9HAN9
Q8TBP6 -0.8031089 0.2158104 10.178944 -3.721363 0.0038477 0.0382674 Q8TBP6
Q07507 -0.7241081 0.1920710 9.608284 -3.770002 0.0039297 0.0389061 Q07507
P46940 -0.6392674 0.1713924 9.877120 -3.729847 0.0039957 0.0393808 P46940
O95202 0.5779418 0.1532531 9.434554 3.771159 0.0040519 0.0396103 O95202
P08603 -0.6992781 0.1878961 9.869474 -3.721621 0.0040552 0.0396103 P08603
Q8TAL6 -1.8063108 0.4072295 6.166509 -4.435608 0.0041152 0.0400182 Q8TAL6
Q9HB40 -1.1127203 0.2527625 6.220567 -4.402237 0.0041808 0.0404764 Q9HB40
O75190-3 1.1109397 0.3052132 10.314084 3.639881 0.0043145 0.0415862 O75190-3
Q9H511 0.6668969 0.1811347 9.812109 3.681773 0.0043711 0.0419470 Q9H511
Q07954 -0.6207211 0.1703226 9.863946 -3.644385 0.0046065 0.0440134 Q07954
Q9NQZ5 1.1063978 0.3078097 10.264418 3.594421 0.0046943 0.0446157 Q9NQZ5
Q16654 0.8623740 0.2320437 9.094702 3.716429 0.0047103 0.0446157 Q16654
Q2TAA5 -0.6977539 0.1932119 10.009057 -3.611340 0.0047506 0.0448030 Q2TAA5
Q5JVS0 -1.3591318 0.2824461 5.020511 -4.812004 0.0047804 0.0448910 Q5JVS0
Q0VAK6 0.9271117 0.2605769 10.498467 3.557919 0.0048217 0.0450849 Q0VAK6
P07686 0.8405364 0.2229746 8.526947 3.769651 0.0048703 0.0450865 P07686
Q13636 -1.5458451 0.3698208 6.499442 -4.179984 0.0048744 0.0450865 Q13636
P15374 0.7806300 0.2186997 10.281794 3.569416 0.0048837 0.0450865 P15374
P78406 -1.1054909 0.2599367 6.222935 -4.252923 0.0049379 0.0452520 P78406
P20774 -1.0939518 0.3086286 10.484917 -3.544558 0.0049430 0.0452520 P20774
Q08945 -0.8371286 0.2282277 9.153856 -3.667954 0.0050247 0.0458089 Q08945
Q99766 0.5801784 0.1601546 9.531547 3.622615 0.0050489 0.0458380 Q99766
P49207 -0.5839141 0.1637478 10.075594 -3.565936 0.0050702 0.0458413 P49207
P22695 0.6160228 0.1729941 10.018999 3.560947 0.0051578 0.0464413 P22695
Q9UI09 0.6403409 0.1794697 9.918617 3.567961 0.0051791 0.0464419 Q9UI09
P07357 -0.6143868 0.1718693 9.780942 -3.574733 0.0052357 0.0467581 P07357
Q16082 -0.6322050 0.1787586 10.109135 -3.536641 0.0052977 0.0469738 Q16082
Q8IXM3 0.6461790 0.1818011 9.886801 3.554318 0.0053241 0.0469738 Q8IXM3
Q92901 0.7836579 0.2227719 10.288945 3.517759 0.0053243 0.0469738 Q92901
Q13825 1.1245424 0.3221135 10.534211 3.491137 0.0053793 0.0472691 Q13825
Q9NQT8 -1.1513781 0.2915578 7.066202 -3.949056 0.0054352 0.0472891 Q9NQT8
P01604 -1.2896968 0.2795691 5.116835 -4.613159 0.0054481 0.0472891 P01604
Q6ZVF9 -1.0955862 0.2709835 6.656492 -4.042999 0.0054588 0.0472891 Q6ZVF9
P01699 -1.6297466 0.3943727 6.325570 -4.132503 0.0054681 0.0472891 P01699
Q16795 0.6450332 0.1840663 10.157955 3.504353 0.0055524 0.0478297 Q16795
Q9UI47 0.5900888 0.1676449 9.800459 3.519874 0.0057127 0.0490175 Q9UI47
O75947 0.6002336 0.1718265 10.023771 3.493253 0.0057709 0.0492302 O75947
P62760 -0.9670022 0.2804990 10.519281 -3.447436 0.0058116 0.0492302 P62760
Q04446 0.8608184 0.2481309 10.234155 3.469211 0.0058272 0.0492302 Q04446
Q9BSD7 1.4315921 0.3832730 7.936523 3.735176 0.0058275 0.0492302 Q9BSD7
Q9HC36 0.9665091 0.2423551 6.638088 3.987987 0.0058727 0.0494208 Q9HC36
P11182 0.5859546 0.1675892 9.821787 3.496374 0.0059193 0.0495347 P11182
O15118 -1.3330724 0.3463625 7.225517 -3.848778 0.0059315 0.0495347 O15118
Q9UHP9 0.7222478 0.2074836 9.910771 3.480988 0.0059908 0.0497354 Q9UHP9
Q96JM3 -0.9006783 0.2426905 7.958417 -3.711222 0.0060010 0.0497354 Q96JM3

6.7.8 Interaction

Let us retrieve the result table from the rowData. The second column contains the results for the interaction contrast.

inferenceInt <- rowData(pe[["proteins"]])[[colnames(L)[4]]]
inferenceInt$Protein <- rownames(inferenceInt)
head(inferenceInt)
             logFC           se        df          t      pval adjPval Protein
A0PJW6  0.00000000 7.792300e-10 14.003000  0.0000000 1.0000000       1  A0PJW6
A0PJZ3          NA           NA        NA         NA        NA      NA  A0PJZ3
A0PK00          NA           NA  5.826529         NA        NA      NA  A0PK00
A1A4S6  0.01527264 1.258090e-01 13.324734  0.1213955 0.9051894       1  A1A4S6
A1A5D9          NA           NA        NA         NA        NA      NA  A1A5D9
A1IGU5 -0.28917988 2.871906e-01 10.934858 -1.0069267 0.3357322       1  A1IGU5

Volcano plot

Volcano plots are straightforward to generate from the inference table above. We also use ggrepel to annotate the 20 most significant proteins.

ggplot(inferenceInt) +
  aes(x = logFC, y = -log10(pval), color = adjPval < 0.05) +
  geom_point() +
  geom_text_repel(data = slice_min(inferenceInt, adjPval, n = 20),
                  aes(label = Protein)) +
  scale_color_manual(values = alpha(c("black", "red"), 0.5)) + 
  ggtitle("log2 FC V-A Right",
          paste("Hypothesis test:", colnames(L)[2], "= 0"))

As there are no significant features, we do not return a top table and do not make a heatmap for this contrast.

6.8 Conclusion

In this chapter, we illustrated the analysis of a label-free proteomics data set with technical replication. We followed the workflow described in the previous chapters with minimal changes.

The experiment presented in this chapter presents a complex design and is an excellent illustration on how to model data with main effects, interactions and block effects. We could investigate:

  1. The difference in protein abundance between the atrium and ventriculum in the left heart compartment.
  2. The difference in protein abundance between the atrium and ventriculum in the right heart compartment.
  3. The difference in protein abundance between the atrium and ventriculum, averaged over the right and left heart compartment.
  4. The interaction, whether the difference between atrium and ventriculum is affected by whether we focus on the left heart or the right heart compartment.

  1. Note that we use the same heatmap annotations so we don’t need to generate it again.↩︎