Author
Affiliation

Lieven Clement

Ghent University

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

1.1 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.

1.2 Load packages

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

suppressPackageStartupMessages({
library("QFeatures")  
library("dplyr") 
library("tidyr")
library("ggplot2")
library("msqrob2")    
library("stringr")
library("ExploreModelMatrix")
library("kableExtra")
library("ComplexHeatmap")
library("scater")
library("patchwork")
})

We also configure the parallelisation framework.

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

1.3 Load Data

1.3.1 Getting the data

The data were searched with MaxQuant 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 msqrob2data.

library("BiocFileCache")
Loading required package: dbplyr

Attaching package: 'dbplyr'
The following objects are masked from 'package:dplyr':

    ident, sql, sql_escape_ident, sql_escape_string
bfc <- BiocFileCache()
peptideFile <- bfcrpath(bfc, "https://github.com/statOmics/msqrob2data/raw/refs/heads/main/dda/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 each quantification column (that starts with "Intensity") represents a single sample.

peptides <- data.table::fread(peptideFile, check.names = TRUE, integer64 = "double")
quantcols <- grep("Intensity[.]", names(peptides), 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(sampleId = gsub("Intensity[.]", "", quantcols), 
         location  = substr(sampleId, 1, 1), # heart region left-right
         tissue  = substr(sampleId, 2, 2), # tissue Atrium-Ventriculum
         patient  = substr(sampleId, 3, 3)) # patient id
quantCols sampleId location tissue patient
Intensity.LA3 LA3 L A 3
Intensity.LA4 LA4 L A 4
Intensity.LA8 LA8 L A 8
Intensity.LV3 LV3 L V 3
Intensity.LV4 LV4 L V 4
Intensity.LV8 LV8 L V 8

1.3.2 The QFeatures data class

We combine the two tables into a QFeatures object.

(pe <- readQFeatures(
  peptides, colData = coldata, fnames = "Sequence", name = "peptides"
))
Checking arguments.
Loading data as a 'SummarizedExperiment' object.
Formatting sample annotations (colData).
Formatting data as a 'QFeatures' object.
Setting assay rownames.
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).

1.4 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).

1.4.1 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()
`stat_bin()` using `bins = 30`. Pick better value `binwidth`.

1.4.2 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
'Proteins' found in 1 out of 1 assay(s).
  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 != "+")
'Reverse' found in 1 out of 1 assay(s).'Potential.contaminant' found in 1 out of 1 assay(s).
  1. Remove highly missing peptides.

We keep peptides that were observed at least 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.

1.4.3 Identifications per sample

pe[,,"peptides"] |> 
  longForm(colvars = colnames(colData(pe)), 
           rowvars= c("Sequence", 
                      "Proteins")) |>
  data.frame() |>
  filter(!is.na(value)) |>
  group_by(location, tissue, sampleId) |>
  summarise(Precursors = length(unique(Sequence)),
            `Protein Groups` = length(unique(Proteins))) |> 
  pivot_longer(-(1:3),
               names_to = "Feature",
               values_to = "IDs") |> 
  ggplot(aes(x = sampleId, y = IDs, fill = interaction(location,tissue))) +
  geom_col() +
  #scale_fill_observable() +
  facet_wrap(~Feature,
             scales = "free_y") +
  labs(title = "Peptide and protein group identifications per sample",
       x = "Sample",
       y = "Identifications") +
  theme_bw() + 
  theme(axis.text.x = element_text(angle = 90))
`summarise()` has regrouped the output.
ℹ Summaries were computed grouped by location, tissue, and sampleId.
ℹ Output is grouped by location and tissue.
ℹ Use `summarise(.groups = "drop_last")` to silence this message.
ℹ Use `summarise(.by = c(location, tissue, sampleId))` for per-operation
  grouping (`?dplyr::dplyr_by`) instead.

There are rather large variations in the peptide and protein identifications across samples. Therefore we will normalise the data using the median method, which centers each sample by subtracting its median log2 intensity so that all samples are aligned to a common location.

1.4.4 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 method.
pe <- sweep( #Subtract log2 norm factor column-by-column (MARGIN = 2)
  pe,
  MARGIN = 2,
  STATS = nfLogMedian(pe,i = "peptides_log",na.rm = TRUE),
  i = "peptides_log",
  name = "peptides_norm"
)
This function aims to calculate norm factors on a log scale, 
          the input data are assumed to be on the log-scale!

Upon the normalisation the density curves should be nicely centred. To confirm this, we will plot the intensity distributions for each biorepeat (patient). 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")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 12 sampleMap rows not in names(experiments)
Warning: Removed 108454 rows containing non-finite outside the scale range
(`stat_density()`).

  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. As the robust summarisation takes a while we shift to medianPolish in this dataset.

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

  |                                                                            
  |                                                                      |   0%

  |                                                                            
  |======================================================================| 100%
The following messages occurred during aggregation:

Your quantitative and row data contain missing values. Please read the
relevant section(s) in the aggregateFeatures manual page regarding the
effects of missing values on data aggregation.

Occurred during the aggregation of set(s): peptides

1.5 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) 
Warning: 'experiments' dropped; see 'drops()'
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.

1.6 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

1.6.1 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
)

1.7 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`

1.7.1 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.

1.7.2 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:

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

1.7.3 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

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

1.7.4 Research question 4: does the effect of tissue differ 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

\[ \begin{array}{rcl} \log_2 FC_{V-A}^R- \log_2 FC_{V-A}^L &=& \beta_V + \beta_{R:V}-\beta_V \\ &=& \beta_{R:V} \end{array} \] ### 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
L <- makeContrast(
  c(
    "tissueV = 0",
    "tissueV + locationR:tissueV = 0",
    "tissueV + 0.5*locationR:tissueV = 0",
    "locationR:tissueV = 0"
  ),
  parameterNames = colnames(vd$designmatrix)
  )

We can now falsify the null hypothesis of each contrast:

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

1.7.5 Results tables for significant proteins

We first collect the results for all contrasts.

inferences <- 
  msqrobCollect(pe[["proteins"]], L) 
head(inferences)
                     logFC        se       df          t      pval   adjPval
tissueV.A0PJW6  0.46100784 0.5817309 8.425498  0.7924761 0.4498194 0.6603534
tissueV.A0PJZ3  0.00000000        NA       NA         NA        NA        NA
tissueV.A0PK00          NA        NA       NA         NA        NA        NA
tissueV.A1A4S6  0.06833739 0.3014249 8.867620  0.2267145 0.8257856 0.9069296
tissueV.A1A5D9          NA        NA       NA         NA        NA        NA
tissueV.A1IGU5 -0.48805525 0.3373626 9.433948 -1.4466786 0.1803821 0.4005632
               contrast feature
tissueV.A0PJW6  tissueV  A0PJW6
tissueV.A0PJZ3  tissueV  A0PJZ3
tissueV.A0PK00  tissueV  A0PK00
tissueV.A1A4S6  tissueV  A1A4S6
tissueV.A1A5D9  tissueV  A1A5D9
tissueV.A1IGU5  tissueV  A1IGU5

Next, we will return tables of proteins for which the contrasts are significant at the 5% FDR level.

  1. We set the significance level
  2. We loop over the contrasts
  3. We filter the significant results for the contrast from the table
  4. We print the table
alpha <- 0.05 #1.
for (contrasti in colnames(L)) #2. 
{
  sigList <- inferences |> 
    filter(contrast == contrasti & adjPval < alpha) #3.
  cat("**Contrast:**", contrasti, "= 0 (", nrow(sigList), " significant proteins)\n\n") #4.
  print(kable(sigList, row.names = FALSE) |>
      kable_styling(full_width = FALSE) |>
      scroll_box(height = "250px")
      ) #4.
  cat("\n\n\n---\n\n") #4.
}

Contrast: tissueV = 0 ( 142 significant proteins)

logFC se df t pval adjPval contrast feature
-2.256330 0.5389209 9.433948 -4.186756 0.0021212 0.0392162 tissueV A6NMZ7
-4.247351 0.7727317 8.433948 -5.496541 0.0004795 0.0194140 tissueV O00180
-1.861782 0.4070922 9.135478 -4.573367 0.0012896 0.0299875 tissueV O00264
-2.378569 0.3418984 9.391022 -6.956947 0.0000537 0.0091190 tissueV O14967
-3.292729 0.7152737 6.433948 -4.603453 0.0030760 0.0457557 tissueV O15116
-1.256215 0.3169899 9.273478 -3.962951 0.0031005 0.0457831 tissueV O15230
-1.883608 0.3699968 9.284261 -5.090877 0.0005929 0.0218082 tissueV O43464
-2.699229 0.4650152 8.518026 -5.804603 0.0003181 0.0162008 tissueV O43677
-1.531385 0.3672999 8.696175 -4.169304 0.0026023 0.0425067 tissueV O60262
-3.570879 0.6574253 8.433948 -5.431611 0.0005195 0.0202110 tissueV O60760
-2.155661 0.3044861 9.433948 -7.079669 0.0000456 0.0091190 tissueV O75368
-1.554408 0.3592891 8.577180 -4.326343 0.0021438 0.0392162 tissueV O75746
-1.566930 0.3820714 9.433948 -4.101144 0.0024192 0.0418099 tissueV O75828
2.385249 0.3316354 9.243472 7.192384 0.0000446 0.0091190 tissueV O94875-10
-4.138829 0.5582860 5.433948 -7.413455 0.0004872 0.0194140 tissueV O95631
-2.025355 0.2966728 9.433948 -6.826897 0.0000610 0.0093350 tissueV O95865
-2.236033 0.4604501 8.773904 -4.856190 0.0009688 0.0296708 tissueV O95980
-2.086787 0.3237275 9.069401 -6.446122 0.0001147 0.0100149 tissueV P00325
-1.741370 0.4140971 9.414309 -4.205220 0.0020717 0.0392162 tissueV P00748
-1.284520 0.2891255 8.801787 -4.442777 0.0017079 0.0352561 tissueV P01031
-1.750032 0.4337484 8.975541 -4.034670 0.0029687 0.0454460 tissueV P01034
-4.295755 0.6894564 6.342953 -6.230640 0.0006379 0.0226383 tissueV P01699
-2.657562 0.3896795 8.743391 -6.819867 0.0000888 0.0100149 tissueV P02452
-3.564882 0.8495938 8.799915 -4.195985 0.0024387 0.0418099 tissueV P02461
-2.099587 0.3844806 6.430982 -5.460840 0.0012525 0.0298563 tissueV P02671
-2.112036 0.3870179 9.433948 -5.457205 0.0003410 0.0162008 tissueV P02743
-2.850800 0.3943786 8.539630 -7.228587 0.0000646 0.0093350 tissueV P02747
-1.997378 0.3978599 9.433948 -5.020305 0.0006229 0.0225012 tissueV P02775
-1.735360 0.3933610 8.119921 -4.411620 0.0021716 0.0392162 tissueV P02776
-1.556362 0.3648036 9.194266 -4.266301 0.0019923 0.0391312 tissueV P04003
-1.517126 0.3542999 9.145077 -4.282037 0.0019702 0.0390758 tissueV P04004
-1.518034 0.2785473 9.120935 -5.449825 0.0003874 0.0174147 tissueV P04083
-1.804481 0.3172697 9.223290 -5.687529 0.0002730 0.0155388 tissueV P04196
1.368775 0.3196811 9.206632 4.281688 0.0019412 0.0388807 tissueV P04209
-1.274317 0.2699387 9.299220 -4.720766 0.0009948 0.0296708 tissueV P05455
-1.949580 0.2812867 9.433948 -6.930937 0.0000541 0.0091190 tissueV P05546
-3.018297 0.5371294 9.096272 -5.619311 0.0003137 0.0162008 tissueV P05997
-1.463249 0.3124907 8.957420 -4.682537 0.0011629 0.0297511 tissueV P06727
1.752066 0.3917293 9.347782 4.472644 0.0014095 0.0320392 tissueV P06858
-1.631352 0.3207822 9.433948 -5.085545 0.0005683 0.0212903 tissueV P07451
-2.637230 0.3823298 8.399318 -6.897790 0.0000990 0.0100149 tissueV P08294
-1.361480 0.3305696 9.175922 -4.118588 0.0024982 0.0420967 tissueV P08311
-1.798944 0.3440090 9.358777 -5.229354 0.0004776 0.0194140 tissueV P08582
8.036265 0.4629613 9.433948 17.358393 0.0000000 0.0000359 tissueV P08590
-1.350145 0.3081306 9.433948 -4.381730 0.0015785 0.0339706 tissueV P09619
6.922748 0.5214615 7.433948 13.275665 0.0000019 0.0012899 tissueV P10916
-1.867042 0.3482977 7.669308 -5.360478 0.0007793 0.0254273 tissueV P12110
4.461958 0.3796098 9.187547 11.754064 0.0000008 0.0007741 tissueV P12883
-4.100825 0.6525065 9.248948 -6.284726 0.0001276 0.0100149 tissueV P13533
2.046070 0.2907913 8.566705 7.036214 0.0000777 0.0100149 tissueV P14854
1.258738 0.2850555 9.433948 4.415767 0.0014999 0.0333444 tissueV P15924
-2.170042 0.3408322 9.024453 -6.366892 0.0001287 0.0100149 tissueV P18428
2.492603 0.5246211 8.902075 4.751244 0.0010744 0.0297511 tissueV P19429
-3.051810 0.4208869 9.150157 -7.250903 0.0000441 0.0091190 tissueV P21810
-4.196948 0.5794387 7.433948 -7.243126 0.0001276 0.0100149 tissueV P23083
-2.090902 0.4792699 7.813467 -4.362681 0.0025429 0.0421671 tissueV P23142
1.804561 0.3887018 9.397850 4.642532 0.0010830 0.0297511 tissueV P24311
-2.471756 0.3920447 9.375855 -6.304780 0.0001174 0.0100149 tissueV P24844
-1.691952 0.4076402 9.167692 -4.150601 0.0023850 0.0415934 tissueV P25940
-1.936652 0.3108895 9.430983 -6.229391 0.0001256 0.0100149 tissueV P29622
-1.471516 0.3804343 9.430983 -3.867991 0.0034800 0.0497996 tissueV P30405
-1.546391 0.2998396 9.198832 -5.157393 0.0005571 0.0212645 tissueV P31994-2
-3.083432 0.5058778 8.433948 -6.095212 0.0002351 0.0148630 tissueV P36021
-2.308191 0.3959201 9.376964 -5.829941 0.0002133 0.0143521 tissueV P36955
-2.181803 0.3690247 9.123321 -5.912350 0.0002138 0.0143521 tissueV P40261
-1.466407 0.3525420 8.584923 -4.159525 0.0027147 0.0429047 tissueV P41240
-1.219130 0.2854213 9.433948 -4.271337 0.0018648 0.0377242 tissueV P45877
-1.913372 0.4035815 8.433948 -4.740979 0.0012646 0.0298563 tissueV P46060
-1.371089 0.2922254 8.630232 -4.691889 0.0012692 0.0298563 tissueV P46063
-2.121460 0.2591890 9.364258 -8.184993 0.0000145 0.0058565 tissueV P46821
-1.302496 0.2765851 9.433445 -4.709204 0.0009728 0.0296708 tissueV P48681
-2.246934 0.5140350 7.305919 -4.371170 0.0029523 0.0454460 tissueV P49458
-2.167143 0.3407613 9.306778 -6.359710 0.0001134 0.0100149 tissueV P51884
-3.001450 0.3929629 7.331433 -7.637999 0.0000964 0.0100149 tissueV P51888
-1.660463 0.3953137 9.134394 -4.200369 0.0022310 0.0395247 tissueV P54652
-2.065701 0.4223520 8.640036 -4.890947 0.0009663 0.0296708 tissueV P56539
-1.399853 0.3235338 8.058362 -4.326760 0.0024803 0.0420967 tissueV P60468
-1.651194 0.4106767 8.371926 -4.020667 0.0034956 0.0497996 tissueV P80723
1.218408 0.2936481 9.433948 4.149212 0.0022468 0.0395247 tissueV Q00G26
-4.460432 0.7543698 9.057111 -5.912791 0.0002199 0.0143521 tissueV Q06828
-1.459927 0.3169175 9.433948 -4.606648 0.0011300 0.0297511 tissueV Q07954
-2.019978 0.4483416 8.331057 -4.505443 0.0017947 0.0366739 tissueV Q08945
-1.291628 0.3071943 9.365148 -4.204596 0.0020979 0.0392162 tissueV Q12988
-3.181977 0.6528202 6.123404 -4.874201 0.0026295 0.0425067 tissueV Q13636
-2.202856 0.5219677 9.329768 -4.220292 0.0020658 0.0392162 tissueV Q14195-2
-1.537379 0.2811853 9.433948 -5.467495 0.0003363 0.0162008 tissueV Q14764
-2.495413 0.4384584 9.342420 -5.691333 0.0002589 0.0154074 tissueV Q15113
-1.912840 0.4025134 7.829846 -4.752238 0.0015290 0.0336204 tissueV Q15274
-2.520408 0.4240490 9.326424 -5.943670 0.0001884 0.0141140 tissueV Q16647
-1.417031 0.2973845 8.661483 -4.764978 0.0011375 0.0297511 tissueV Q2TAA5
-2.878220 0.6045542 8.490435 -4.760897 0.0012088 0.0298224 tissueV Q53GG5-2
-2.410808 0.4281842 9.022510 -5.630305 0.0003187 0.0162008 tissueV Q53GQ0
3.531043 0.6897770 5.646679 5.119107 0.0026083 0.0425067 tissueV Q5JPH6
-3.493505 0.7237501 8.409135 -4.826948 0.0011373 0.0297511 tissueV Q5M9N0
-2.269662 0.4803869 8.710526 -4.724653 0.0011833 0.0297511 tissueV Q5NDL2
-2.532305 0.4912922 7.433948 -5.154377 0.0010937 0.0297511 tissueV Q6SZW1
-3.244810 0.3566296 9.204920 -9.098544 0.0000067 0.0033855 tissueV Q6UWY5
-1.687268 0.3249699 7.134260 -5.192074 0.0011912 0.0297511 tissueV Q7L4S7
-2.843950 0.7026784 9.356224 -4.047300 0.0026741 0.0425966 tissueV Q7LBR1
-3.285237 0.3968335 8.177077 -8.278630 0.0000300 0.0091190 tissueV Q8N474
-1.975649 0.4685331 9.109710 -4.216669 0.0021905 0.0392162 tissueV Q8N5M1
-1.261701 0.2998202 9.433948 -4.208193 0.0020529 0.0392162 tissueV Q8NAT1
-1.741046 0.4153039 8.295061 -4.192221 0.0027947 0.0434905 tissueV Q8TBP6
-2.737884 0.4150798 8.665816 -6.596041 0.0001186 0.0100149 tissueV Q8TBQ9
-1.992816 0.4195501 7.661178 -4.749889 0.0016280 0.0343074 tissueV Q8TDB6
-5.643390 0.9984357 8.512093 -5.652232 0.0003835 0.0174147 tissueV Q8WWA0
-1.630769 0.3469138 9.389443 -4.700791 0.0009973 0.0296708 tissueV Q8WWQ0
-1.773409 0.3770680 8.433948 -4.703155 0.0013304 0.0305835 tissueV Q8WY22
-1.475739 0.2832538 9.433948 -5.209953 0.0004780 0.0194140 tissueV Q8WZA9
-2.108757 0.5248919 9.430983 -4.017508 0.0027551 0.0432065 tissueV Q92604
-1.565917 0.3608201 9.433948 -4.339882 0.0016811 0.0350601 tissueV Q92621
-2.167922 0.4525726 8.252958 -4.790219 0.0012583 0.0298563 tissueV Q92681
-3.280388 0.6007115 8.761038 -5.460837 0.0004392 0.0193159 tissueV Q92736-2
-1.451275 0.3320240 9.433948 -4.370995 0.0016041 0.0341598 tissueV Q96C86
-1.364459 0.3429721 9.265880 -3.978338 0.0030327 0.0454460 tissueV Q96CS3
-3.029621 0.5647951 6.293415 -5.364106 0.0014786 0.0332368 tissueV Q96H79
-2.361327 0.4035900 8.932520 -5.850806 0.0002507 0.0153692 tissueV Q96LL9
-1.878895 0.4255144 9.291579 -4.415584 0.0015567 0.0338619 tissueV Q9BUF5
-1.718281 0.4146180 8.773709 -4.144250 0.0026475 0.0425067 tissueV Q9BVC6
-2.741837 0.4804051 9.128851 -5.707344 0.0002765 0.0155388 tissueV Q9BW30
-2.640220 0.5299183 9.164115 -4.982314 0.0007171 0.0241776 tissueV Q9BXN1
-2.319468 0.5021885 9.433948 -4.618718 0.0011102 0.0297511 tissueV Q9BXR6
-1.949302 0.4614800 9.083904 -4.224022 0.0021804 0.0392162 tissueV Q9HAV4
-1.809596 0.4513496 8.704945 -4.009301 0.0032818 0.0481091 tissueV Q9HCB6
-6.032857 0.8726963 5.433948 -6.912895 0.0006902 0.0236658 tissueV Q9NVN8
-2.031408 0.4253134 9.217589 -4.776263 0.0009416 0.0296708 tissueV Q9NY15
-2.243179 0.4803304 9.433948 -4.670075 0.0010297 0.0297511 tissueV Q9NZ01
-2.073522 0.3804634 9.433948 -5.449990 0.0003444 0.0162008 tissueV Q9P2B2
2.497119 0.3842174 6.388237 6.499235 0.0004894 0.0194140 tissueV Q9UBB5
-2.465177 0.4133925 9.167649 -5.963284 0.0001968 0.0142195 tissueV Q9UBG0
-3.179975 0.6206302 7.587704 -5.123783 0.0010639 0.0297511 tissueV Q9UBI9
-2.213608 0.4404746 9.283303 -5.025506 0.0006494 0.0226516 tissueV Q9UGT4
-1.780248 0.4219707 7.433948 -4.218890 0.0034437 0.0497608 tissueV Q9UK22
1.451100 0.3513905 9.071616 4.129594 0.0025179 0.0420967 tissueV Q9UKS6
-2.515028 0.4313431 8.300164 -5.830689 0.0003403 0.0162008 tissueV Q9UL18
-1.507428 0.3747988 8.994177 -4.021967 0.0030131 0.0454460 tissueV Q9ULC3
-3.388504 0.4005353 7.767311 -8.459939 0.0000348 0.0091190 tissueV Q9ULL5-3
3.641721 0.7396510 9.210665 4.923566 0.0007666 0.0254228 tissueV Q9UNW9
-1.716245 0.4408551 9.433948 -3.892991 0.0033441 0.0486693 tissueV Q9Y2Z0
-4.091536 0.8902676 9.322352 -4.595850 0.0011843 0.0297511 tissueV Q9Y5U8
-3.148958 0.7542502 9.433948 -4.174952 0.0021599 0.0392162 tissueV Q9Y623
-1.047476 0.2647522 9.433948 -3.956438 0.0030280 0.0454460 tissueV Q9Y646

Contrast: tissueV + locationR:tissueV = 0 ( 64 significant proteins)

logFC se df t pval adjPval contrast feature
-3.065032 0.5389209 9.433948 -5.687351 0.0002510 0.0282361 tissueV + locationR:tissueV A6NMZ7
-4.593177 0.8922737 8.433948 -5.147723 0.0007427 0.0379705 tissueV + locationR:tissueV O00180
-2.950348 0.4990711 8.518026 -5.911678 0.0002800 0.0282361 tissueV + locationR:tissueV O43677
-1.700876 0.3044861 9.433948 -5.586056 0.0002870 0.0282361 tissueV + locationR:tissueV O75368
-1.833663 0.4147809 9.414309 -4.420798 0.0014962 0.0496983 tissueV + locationR:tissueV P00748
-1.578510 0.2711827 8.801787 -5.820837 0.0002753 0.0282361 tissueV + locationR:tissueV P01031
-2.302934 0.5044105 9.433948 -4.565594 0.0012004 0.0484338 tissueV + locationR:tissueV P01611
-2.091860 0.3978599 9.433948 -5.257781 0.0004474 0.0296715 tissueV + locationR:tissueV P02775
-2.793851 0.3463263 8.119921 -8.067107 0.0000378 0.0158307 tissueV + locationR:tissueV P02776
-2.047760 0.3641322 9.145077 -5.623672 0.0003059 0.0282361 tissueV + locationR:tissueV P04004
-1.408970 0.3112669 9.223290 -4.526565 0.0013468 0.0496983 tissueV + locationR:tissueV P04196
-1.723477 0.2812867 9.433948 -6.127119 0.0001426 0.0271686 tissueV + locationR:tissueV P05546
3.469745 0.3888225 9.347782 8.923725 0.0000071 0.0074344 tissueV + locationR:tissueV P06858
-2.045966 0.3683270 8.399318 -5.554754 0.0004530 0.0296715 tissueV + locationR:tissueV P08294
5.257584 0.4629613 9.433948 11.356422 0.0000008 0.0017178 tissueV + locationR:tissueV P08590
-1.528856 0.3109247 9.433948 -4.917126 0.0007210 0.0377788 tissueV + locationR:tissueV P08603
2.937439 0.5214615 7.433948 5.633089 0.0006378 0.0354002 tissueV + locationR:tissueV P10916
2.051245 0.3516054 8.110318 5.833941 0.0003701 0.0286031 tissueV + locationR:tissueV P11586
1.744750 0.3710205 9.187547 4.702570 0.0010553 0.0476655 tissueV + locationR:tissueV P12883
-3.784455 0.6416964 9.248948 -5.897579 0.0002064 0.0282361 tissueV + locationR:tissueV P13533
-2.808609 0.4097458 9.150157 -6.854515 0.0000686 0.0179779 tissueV + locationR:tissueV P21810
-2.900441 0.5261490 7.813467 -5.512584 0.0006135 0.0354002 tissueV + locationR:tissueV P23142
1.430989 0.3080982 9.433948 4.644586 0.0010688 0.0476655 tissueV + locationR:tissueV P23434
2.178790 0.3982033 9.120492 5.471551 0.0003765 0.0286031 tissueV + locationR:tissueV P24298
-1.742948 0.3901045 9.375855 -4.467901 0.0014090 0.0496983 tissueV + locationR:tissueV P24844
-2.179456 0.3419612 9.090582 -6.373403 0.0001237 0.0259273 tissueV + locationR:tissueV P28066
-1.671125 0.3109664 9.430983 -5.373973 0.0003821 0.0286031 tissueV + locationR:tissueV P29622
-1.807166 0.3805284 9.430983 -4.749097 0.0009187 0.0431140 tissueV + locationR:tissueV P30405
-2.179021 0.4035646 8.985606 -5.399435 0.0004357 0.0296715 tissueV + locationR:tissueV P30711
-2.611009 0.5505872 7.971322 -4.742225 0.0014742 0.0496983 tissueV + locationR:tissueV P34947
-1.951026 0.3860592 7.586870 -5.053696 0.0011566 0.0484338 tissueV + locationR:tissueV P35052
-3.960182 0.7263558 8.218284 -5.452124 0.0005537 0.0341326 tissueV + locationR:tissueV P35625
-1.507126 0.2590543 9.364258 -5.817801 0.0002178 0.0282361 tissueV + locationR:tissueV P46821
-3.053992 0.3876192 9.023372 -7.878847 0.0000246 0.0129048 tissueV + locationR:tissueV P48163
-1.496251 0.2765967 9.433445 -5.409505 0.0003638 0.0286031 tissueV + locationR:tissueV P48681
-1.880772 0.3680519 7.331433 -5.110072 0.0012016 0.0484338 tissueV + locationR:tissueV P51888
-2.672245 0.4061774 9.134394 -6.579009 0.0000950 0.0221284 tissueV + locationR:tissueV P54652
-2.436732 0.3775460 8.058362 -6.454132 0.0001913 0.0282361 tissueV + locationR:tissueV P60468
1.958699 0.4118700 7.839050 4.755625 0.0015175 0.0496983 tissueV + locationR:tissueV P61925
2.026358 0.2936481 9.433948 6.900633 0.0000560 0.0167753 tissueV + locationR:tissueV Q00G26
2.273811 0.4647191 8.761218 4.892871 0.0009256 0.0431140 tissueV + locationR:tissueV Q04760
-4.145946 0.7267723 9.057111 -5.704601 0.0002858 0.0282361 tissueV + locationR:tissueV Q06828
-1.452674 0.2811853 9.433948 -5.166251 0.0005078 0.0322528 tissueV + locationR:tissueV Q14764
-2.147198 0.3709191 9.433948 -5.788858 0.0002198 0.0282361 tissueV + locationR:tissueV Q15327
1.717175 0.3868178 9.353448 4.439236 0.0014789 0.0496983 tissueV + locationR:tissueV Q53GG5
3.331585 0.5228045 5.646679 6.372524 0.0008888 0.0431140 tissueV + locationR:tissueV Q5JPH6
-2.688728 0.5189001 8.710526 -5.181592 0.0006418 0.0354002 tissueV + locationR:tissueV Q5NDL2
3.295173 0.5463832 8.829754 6.030883 0.0002106 0.0282361 tissueV + locationR:tissueV Q69YU5
-2.286869 0.4042612 8.828638 -5.656911 0.0003336 0.0286031 tissueV + locationR:tissueV Q6PCB0
-2.509725 0.3491828 9.204920 -7.187425 0.0000459 0.0160224 tissueV + locationR:tissueV Q6UWY5
1.577165 0.3411881 8.647659 4.622567 0.0013885 0.0496983 tissueV + locationR:tissueV Q6YN16
-2.105119 0.4475548 8.177077 -4.703600 0.0014459 0.0496983 tissueV + locationR:tissueV Q8N474
-2.798426 0.4685906 7.606532 -5.972005 0.0004058 0.0293306 tissueV + locationR:tissueV Q92930
-2.417661 0.5162749 8.221037 -4.682894 0.0014646 0.0496983 tissueV + locationR:tissueV Q96KC8
2.765902 0.5768298 8.233783 4.795005 0.0012586 0.0496983 tissueV + locationR:tissueV Q96MM6
-1.418829 0.3138073 9.219733 -4.521338 0.0013584 0.0496983 tissueV + locationR:tissueV Q9BUH6
-2.338278 0.4775458 9.128851 -4.896448 0.0008173 0.0407848 tissueV + locationR:tissueV Q9BW30
-2.212861 0.3988381 7.433948 -5.548269 0.0007002 0.0376316 tissueV + locationR:tissueV Q9H1K0
-2.320764 0.4877645 8.704945 -4.757960 0.0011326 0.0484338 tissueV + locationR:tissueV Q9HCB6
2.541571 0.5293417 8.433948 4.801381 0.0011667 0.0484338 tissueV + locationR:tissueV Q9NRG4
-1.918222 0.3804634 9.433948 -5.041804 0.0006043 0.0354002 tissueV + locationR:tissueV Q9P2B2
-2.487492 0.4464145 9.283303 -5.572158 0.0003098 0.0282361 tissueV + locationR:tissueV Q9UGT4
-4.044123 0.8399174 7.986165 -4.814905 0.0013365 0.0496983 tissueV + locationR:tissueV Q9UKR5
-3.542883 0.3728374 7.433948 -9.502489 0.0000203 0.0129048 tissueV + locationR:tissueV Q9ULD0

Contrast: tissueV + 0.5 * locationR:tissueV = 0 ( 350 significant proteins)

logFC se df t pval adjPval contrast feature
-2.1927240 0.4837180 7.926523 -4.533062 0.0019622 0.0214759 tissueV + 0.5 * locationR:tissueV A5D6W6
1.2114019 0.2428222 9.274354 4.988843 0.0006855 0.0118741 tissueV + 0.5 * locationR:tissueV A6NDG6
-2.6606813 0.3810746 9.433948 -6.982048 0.0000510 0.0028543 tissueV + 0.5 * locationR:tissueV A6NMZ7
-1.1005760 0.2935127 9.433948 -3.749671 0.0041928 0.0333733 tissueV + 0.5 * locationR:tissueV E5RK69
1.2111617 0.3544488 8.642235 3.417028 0.0081355 0.0480888 tissueV + 0.5 * locationR:tissueV I3L505
-4.4202642 0.5901836 8.433948 -7.489643 0.0000529 0.0028543 tissueV + 0.5 * locationR:tissueV O00180
-1.3552284 0.2838544 9.135478 -4.774378 0.0009680 0.0139724 tissueV + 0.5 * locationR:tissueV O00264
-0.8647700 0.2420959 9.433948 -3.572014 0.0055693 0.0385539 tissueV + 0.5 * locationR:tissueV O00299
-1.6656967 0.2413196 9.391022 -6.902452 0.0000572 0.0028543 tissueV + 0.5 * locationR:tissueV O14967
-0.9109848 0.1889470 8.847888 -4.821379 0.0009920 0.0141144 tissueV + 0.5 * locationR:tissueV O14980
-0.9026269 0.2312626 9.147322 -3.903038 0.0034921 0.0309166 tissueV + 0.5 * locationR:tissueV O15031
-2.1158849 0.4221948 6.433948 -5.011632 0.0019793 0.0214912 tissueV + 0.5 * locationR:tissueV O15118
-1.3134093 0.3561296 9.234798 -3.688010 0.0047965 0.0357762 tissueV + 0.5 * locationR:tissueV O15121
-1.2226333 0.2257651 9.273478 -5.415510 0.0003828 0.0091867 tissueV + 0.5 * locationR:tissueV O15230
-0.6912533 0.1906767 9.417533 -3.625263 0.0051277 0.0370424 tissueV + 0.5 * locationR:tissueV O43143
-2.0591957 0.4456408 9.215627 -4.620752 0.0011772 0.0152009 tissueV + 0.5 * locationR:tissueV O43175
-2.8247882 0.3409207 8.518026 -8.285763 0.0000233 0.0023652 tissueV + 0.5 * locationR:tissueV O43677
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-0.9422642 0.2565740 8.145297 -3.672485 0.0060940 0.0405863 tissueV + 0.5 * locationR:tissueV Q9Y5U9
-2.0100375 0.5333354 9.433948 -3.768806 0.0040675 0.0328035 tissueV + 0.5 * locationR:tissueV Q9Y623
-0.7351368 0.1872081 9.433948 -3.926843 0.0031713 0.0292465 tissueV + 0.5 * locationR:tissueV Q9Y646
-0.6895844 0.1946355 9.433948 -3.542953 0.0058361 0.0396776 tissueV + 0.5 * locationR:tissueV Q9Y678
-1.2956793 0.2567103 7.676207 -5.047244 0.0011250 0.0147436 tissueV + 0.5 * locationR:tissueV Q9Y6X5

Contrast: locationR:tissueV = 0 ( 0 significant proteins)

logFC se df t pval adjPval contrast feature
NA NA NA NA NA NA NA NA
—–: –: –: –: —-: ——-: :——– :——-

Note, that no significant interactions are returned. This is not unexpected. We do not have a lot of subjects in the study and the power to pick up interaction terms is typically lower than to pick up main effects.

1.7.6 Volcanoplots

A volcano plot is a common visualisation that provides an overview of the hypothesis testing results, plotting the \(-\log_{10}\) p-value1 as a function of the estimated log fold change. Volcano plots can be used to highlight the most interesting proteins that have large fold changes and/or are highly significant. We can use the table above directly to build a volcano plot using ggplot2 functionality. We also highlight which proteins are UPS standards, known to be differentially abundant by experimental design.

Experienced users can make the plot themselves. However, in msqrob2 we also provide the plotVolcano function that generates volcanoplots based on msqrob2 inference tables generated with the hypothesisTest function.

Since the inference table contains multiple contrasts, we use the facet_wrap function to make a separate volcano plot for each contrast.

volcanoplots <- inferences |> 
  plotVolcano() +
  facet_wrap(~contrast)
volcanoplots

 inferences |> filter(contrast == "tissueV + 0.5 * locationR:tissueV") |> plotVolcano() + ggtitle("Default median normalisation")

pn0 <- pe[, , "peptides_log"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities\nNonnormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn1 <- pe[, , "peptides_norm"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities\nNormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn1c <- 
  filterNA(pe[,,"peptides_norm"], i = "peptides_norm", pNA = 0) |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities (complete cases)\nNormalised log2 ")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn2 <-pe[, , "peptides_norm"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = sampleId,
      y = value,
      colour = sampleId,
      group = colname) +
  xlab("sample") +
  geom_boxplot() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities\nNormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn2c <-
  filterNA(pe[,,"peptides_norm"], i = "peptides_norm", pNA = 0) |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = sampleId,
      y = value,
      colour = sampleId,
      group = colname) +
  xlab("sample") +
  geom_boxplot() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities (complete cases)\nNormalised log2 ")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn3 <- pe[, , "proteins"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = sampleId,
      y = value,
      colour = sampleId,
      group = colname) +
  xlab("sample") +
  geom_boxplot() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Protein intensities\nNormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn4 <- pe[,,"proteins"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
  theme(legend.position = "NULL")+
   labs(subtitle = "Protein intensities\nNormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn4c <- filterNA(pe[,,"proteins"], i = "proteins", pNA = 0) |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
  theme(legend.position = "NULL")+
   labs(subtitle = "Protein intensities (complete cases)\nNormalised log2")
Warning: 'experiments' dropped; see 'drops()'
harmonizing input:
  removing 36 sampleMap rows not in names(experiments)
pn0 + pn1

pn1 + pn1c

pn4 + pn4c

1.7.7 Heatmaps

We use a nominal FDR level of 0.05

alpha <- 0.05

We conduct heatmaps for the significant metabolites for each contrast.

  1. We first extract the assay proteins along with its colData.
  2. We make an empty list heatmaps to store the plots.
  3. We will loop over the column names of the contrast matrix L
  4. We extract the names of the significant features for contrast i
  5. We extract the quant data for the significant metabolites
  6. We extract annotation
  7. We make the heatmap using the quants data. We do not cluster columns (samples) to keep them together according to the design.

We only produce heatmaps for the first three contrasts as no DA proteins were returned for the location x tissue interaction.

heatmaps <- lapply(colnames(L)[1:3],
                   function(contrast, se, alpha)
                   {
                     sig <- rowData(se)[[contrast]] |> 
                       filter(adjPval < alpha) |> 
                       rownames()
                     quants <- t(scale(t(assay(se[sig,]))))
                     colnames(quants) <- paste0(se$location, se$tissue, se$patient)
                     annotations <- columnAnnotation(condition = paste0(se$location, se$tissue))
                     set.seed(1234) ## annotation colours are randomly generated by default
                     return(
                       Heatmap(
                       quants, 
                       name = "log2 intensity",
                       top_annotation = annotations, 
                       column_title = paste0(contrast, " = 0")
                       )
                     )
                   },
                   se = getWithColData(pe, "proteins"),
                   alpha = alpha)
Warning: 'experiments' dropped; see 'drops()'
heatmaps
[[1]]


[[2]]


[[3]]

1.8 Conclusion

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

The experiment presented in this chapter has a complex design and is an excellent illustration of 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.

Footnotes

  1. Note, that upon this transformation a value of 1 represents a p-value of 0.1, 2 a p-value of 0.01, 3 a p-value of 0.001, etc.↩︎