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).

2. 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).

3. 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() |>
  dplyr::filter(!is.na(value)) |>
  group_by(location, tissue, sampleId) |>
  summarise(Peptides = 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 of Ratios method of DESeq2, which can correct for differences in overall loading across runs as well as for differences in sample composition.

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")

2. Normalisation with Median of Ratios method.

pe <- sweep( #Subtract log2 norm factor column-by-column (MARGIN = 2)
  pe,
  MARGIN = 2,
  STATS = nfLogMedianOfRatios(pe,"peptides_log"),
  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()`).

3. 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  0.8110076 0.5582594 8.232881  1.4527432 0.1833196 0.4315783
tissueV.A0PJZ3  0.0000000        NA       NA         NA        NA        NA
tissueV.A0PK00         NA        NA       NA         NA        NA        NA
tissueV.A1A4S6  0.3414583 0.2915523 8.788831  1.1711734 0.2723013 0.5281864
tissueV.A1A5D9         NA        NA       NA         NA        NA        NA
tissueV.A1IGU5 -0.1769270 0.2957357 9.006075 -0.5982605 0.5644065 0.7825253
               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 |> 
    dplyr::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 ( 83 significant proteins)

logFC	se	df	t	pval	adjPval	contrast	feature
-3.916945	0.7774717	8.101862	-5.038055	0.0009663	0.0365295	tissueV	O00180
-2.044625	0.3197126	8.804095	-6.395195	0.0001388	0.0191572	tissueV	O14967
-2.379147	0.4456184	8.826306	-5.338979	0.0005008	0.0288660	tissueV	O43677
-3.280059	0.6214703	8.101862	-5.277902	0.0007179	0.0325933	tissueV	O60760
-1.844500	0.2861789	9.073544	-6.445269	0.0001146	0.0191572	tissueV	O75368
2.258082	0.4942284	8.774199	4.568904	0.0014405	0.0452361	tissueV	O75394
1.827648	0.4030056	8.779069	4.535045	0.0015085	0.0452361	tissueV	O75629
2.743133	0.3461991	8.120196	7.923570	0.0000430	0.0124629	tissueV	O94875-10
-3.763413	0.5445649	5.101862	-6.910862	0.0008961	0.0356154	tissueV	O95631
-1.693278	0.3104372	8.856414	-5.454493	0.0004265	0.0284673	tissueV	O95865
-2.004354	0.4284177	8.619427	-4.678506	0.0012971	0.0427182	tissueV	O95980
-1.756089	0.3039540	8.302260	-5.777484	0.0003619	0.0283628	tissueV	P00325
-3.927443	0.6640256	6.032136	-5.914595	0.0010200	0.0365295	tissueV	P01699
-2.337834	0.4095929	8.838508	-5.707700	0.0003118	0.0276131	tissueV	P02452
-1.774119	0.3373370	9.101862	-5.259188	0.0005024	0.0288660	tissueV	P02743
-2.585440	0.3854336	7.897172	-6.707874	0.0001607	0.0191572	tissueV	P02747
-1.663394	0.3725967	9.101862	-4.464329	0.0015243	0.0452361	tissueV	P02775
-1.240956	0.2705673	9.023664	-4.586498	0.0013066	0.0427182	tissueV	P04083
-1.527743	0.3012650	8.961183	-5.071094	0.0006799	0.0320498	tissueV	P04196
1.699180	0.3451659	8.933576	4.922794	0.0008395	0.0347288	tissueV	P04209
-1.613522	0.2579277	9.032661	-6.255714	0.0001463	0.0191572	tissueV	P05546
-2.676909	0.4847729	8.765040	-5.521987	0.0004058	0.0283628	tissueV	P05997
2.084096	0.3632129	9.054278	5.737947	0.0002743	0.0276131	tissueV	P06858
-2.327282	0.3654797	8.772312	-6.367746	0.0001455	0.0191572	tissueV	P08294
-1.503653	0.3135321	8.951760	-4.795852	0.0009943	0.0365295	tissueV	P08582
8.356406	0.4777654	9.101862	17.490606	0.0000000	0.0000522	tissueV	P08590
7.258000	0.5018287	7.101862	14.463104	0.0000016	0.0010667	tissueV	P10916
4.827314	0.3783951	8.961545	12.757335	0.0000005	0.0004817	tissueV	P12883
-3.717134	0.6517771	8.840526	-5.703075	0.0003133	0.0276131	tissueV	P13533
2.399958	0.2580900	8.792113	9.298920	0.0000077	0.0038924	tissueV	P14854
1.082278	0.2467290	8.919253	4.386503	0.0017928	0.0478160	tissueV	P14923
1.538482	0.2799160	8.430567	5.496229	0.0004803	0.0288660	tissueV	P15924
1.288628	0.2919866	8.820698	4.413310	0.0017719	0.0478160	tissueV	P17540
-1.863036	0.3057383	8.780541	-6.093565	0.0001999	0.0225106	tissueV	P18428
2.871955	0.5253356	8.763791	5.466897	0.0004354	0.0284673	tissueV	P19429
-2.710880	0.4059821	8.628473	-6.677339	0.0001105	0.0191572	tissueV	P21810
-3.889514	0.6196084	7.101862	-6.277375	0.0003896	0.0283628	tissueV	P23083
1.494665	0.2951584	9.101862	5.063941	0.0006543	0.0320498	tissueV	P23434
1.663250	0.3786441	8.628797	4.392646	0.0019244	0.0485353	tissueV	P24298
2.128221	0.3717094	9.098897	5.725498	0.0002735	0.0276131	tissueV	P24311
-2.192662	0.3779436	8.616156	-5.801559	0.0003057	0.0276131	tissueV	P24844
-1.592623	0.2746297	8.293419	-5.799164	0.0003542	0.0283628	tissueV	P29622
1.686612	0.3782844	8.965037	4.458583	0.0015958	0.0455579	tissueV	P35754
-2.753027	0.5278982	8.101862	-5.215072	0.0007754	0.0329507	tissueV	P36021
-1.981308	0.3817763	8.909723	-5.189710	0.0005907	0.0307913	tissueV	P36955
-1.787446	0.2693518	9.011034	-6.636101	0.0000947	0.0191572	tissueV	P46821
-1.832345	0.3660075	8.971643	-5.006306	0.0007397	0.0325933	tissueV	P51884
-2.724376	0.3550803	8.604573	-7.672565	0.0000396	0.0124629	tissueV	P51888
1.552464	0.3293267	9.101862	4.714056	0.0010650	0.0365295	tissueV	Q00G26
-4.100799	0.7941661	8.668380	-5.163653	0.0006672	0.0320498	tissueV	Q06828
1.488165	0.3158395	9.072420	4.711777	0.0010779	0.0365295	tissueV	Q13011
-1.193748	0.2538072	9.101862	-4.703366	0.0010813	0.0365295	tissueV	Q14764
-2.161374	0.3945623	9.101862	-5.477903	0.0003761	0.0283628	tissueV	Q15113
-2.216355	0.4171324	8.846076	-5.313313	0.0005139	0.0288660	tissueV	Q16647
-2.075418	0.3906257	9.101862	-5.313060	0.0004675	0.0288660	tissueV	Q53GQ0
-3.200391	0.6862475	8.101862	-4.663610	0.0015622	0.0452361	tissueV	Q5M9N0
-1.939256	0.4295019	8.357497	-4.515127	0.0017571	0.0478160	tissueV	Q5NDL2
-2.264859	0.4740676	7.101862	-4.777502	0.0019395	0.0485353	tissueV	Q6SZW1
-2.945962	0.3469489	8.720976	-8.491055	0.0000167	0.0067726	tissueV	Q6UWY5
1.475862	0.3198670	7.948428	4.613985	0.0017532	0.0478160	tissueV	Q6YN16
1.797598	0.3999124	8.884228	4.494979	0.0015492	0.0452361	tissueV	Q86VU5
-2.936360	0.3985650	7.809917	-7.367329	0.0000889	0.0191572	tissueV	Q8N474
-2.356973	0.3822117	9.101862	-6.166668	0.0001578	0.0191572	tissueV	Q8TBQ9
-5.327875	1.0071883	8.210719	-5.289850	0.0006772	0.0320498	tissueV	Q8WWA0
1.115818	0.2589823	9.055231	4.308472	0.0019384	0.0485353	tissueV	Q8WZ42-6
-2.963445	0.6453650	8.615707	-4.591890	0.0014627	0.0452361	tissueV	Q92736-2
1.902891	0.4221313	8.109690	4.507818	0.0019146	0.0485353	tissueV	Q96FJ2
-2.720096	0.5270415	6.074530	-5.161065	0.0020162	0.0494858	tissueV	Q96H79
-2.008136	0.4155078	8.657933	-4.832969	0.0010385	0.0365295	tissueV	Q96LL9
-2.401282	0.4502703	8.713511	-5.332979	0.0005269	0.0288660	tissueV	Q9BW30
-2.290433	0.5234304	8.815856	-4.375812	0.0018722	0.0485353	tissueV	Q9BXN1
1.948862	0.4216617	8.590524	4.621862	0.0014144	0.0452361	tissueV	Q9BXV9
-5.672898	0.8334705	5.101862	-6.806358	0.0009622	0.0365295	tissueV	Q9NVN8
-1.941685	0.4098143	9.101862	-4.737964	0.0010296	0.0365295	tissueV	Q9NZ01
-1.795959	0.3303163	9.101862	-5.437090	0.0003968	0.0283628	tissueV	Q9P2B2
-2.069165	0.4166569	8.975693	-4.966113	0.0007803	0.0329507	tissueV	Q9UBG0
-1.876394	0.4176923	8.947941	-4.492288	0.0015276	0.0452361	tissueV	Q9UGT4
1.752743	0.3405073	8.450170	5.147447	0.0007385	0.0325933	tissueV	Q9UKS6
-2.223787	0.4233928	7.732112	-5.252302	0.0008612	0.0349144	tissueV	Q9UL18
-3.081151	0.4319493	7.941247	-7.133131	0.0001024	0.0191572	tissueV	Q9ULL5-3
3.972042	0.7627201	8.835699	5.207732	0.0005924	0.0307913	tissueV	Q9UNW9
1.125299	0.2635103	9.101862	4.270416	0.0020263	0.0494858	tissueV	Q9Y4W6
-3.786973	0.8566210	8.933416	-4.420827	0.0016996	0.0478160	tissueV	Q9Y5U8

---

Contrast: tissueV + locationR:tissueV = 0 ( 30 significant proteins)

logFC	se	df	t	pval	adjPval	contrast	feature
1.678458	0.3082596	8.392832	5.444948	0.0005197	0.0436305	tissueV + locationR:tissueV	A6NDG6
-2.762249	0.5254530	8.894987	-5.256890	0.0005434	0.0438673	tissueV + locationR:tissueV	A6NMZ7
-2.664358	0.4550109	8.826306	-5.855592	0.0002610	0.0365264	tissueV + locationR:tissueV	O43677
-2.489397	0.3308801	8.701032	-7.523561	0.0000433	0.0227146	tissueV + locationR:tissueV	P02776
-1.693975	0.3125539	8.874482	-5.419786	0.0004428	0.0387292	tissueV + locationR:tissueV	P04004
-1.412195	0.2564006	9.032661	-5.507767	0.0003716	0.0383466	tissueV + locationR:tissueV	P05546
3.783171	0.3617464	9.054278	10.458074	0.0000023	0.0024639	tissueV + locationR:tissueV	P06858
5.569744	0.4777654	9.101862	11.657906	0.0000009	0.0018731	tissueV + locationR:tissueV	P08590
3.170748	0.5018287	7.101862	6.318387	0.0003743	0.0383466	tissueV + locationR:tissueV	P10916
2.378103	0.3652651	7.776293	6.510622	0.0002106	0.0362022	tissueV + locationR:tissueV	P11586
2.030950	0.3737246	8.961545	5.434351	0.0004202	0.0383466	tissueV + locationR:tissueV	P12883
-3.482735	0.6360381	8.840526	-5.475670	0.0004176	0.0383466	tissueV + locationR:tissueV	P13533
-2.489020	0.3907055	8.628473	-6.370578	0.0001559	0.0306390	tissueV + locationR:tissueV	P21810
1.735443	0.2951584	9.101862	5.879700	0.0002248	0.0362022	tissueV + locationR:tissueV	P23434
1.255834	0.2441717	9.097047	5.143243	0.0005883	0.0457376	tissueV + locationR:tissueV	P23786
2.526379	0.3973414	8.628797	6.358208	0.0001580	0.0306390	tissueV + locationR:tissueV	P24298
-1.851478	0.3311495	8.961056	-5.591062	0.0003435	0.0383466	tissueV + locationR:tissueV	P28066
-2.816136	0.3513588	8.796550	-8.014987	0.0000249	0.0174516	tissueV + locationR:tissueV	P48163
-2.444671	0.3552466	9.101862	-6.881617	0.0000683	0.0238876	tissueV + locationR:tissueV	P54652
-2.095858	0.3486929	7.846387	-6.010614	0.0003449	0.0383466	tissueV + locationR:tissueV	P60468
2.325532	0.3293267	9.101862	7.061474	0.0000558	0.0234447	tissueV + locationR:tissueV	Q00G26
2.535090	0.4161457	8.386668	6.091832	0.0002415	0.0362022	tissueV + locationR:tissueV	Q04760
-1.793274	0.3314168	9.097783	-5.410932	0.0004113	0.0383466	tissueV + locationR:tissueV	Q15327
-2.439944	0.4651083	8.357497	-5.245969	0.0006751	0.0472362	tissueV + locationR:tissueV	Q5NDL2
3.630809	0.5753011	8.767180	6.311145	0.0001558	0.0306390	tissueV + locationR:tissueV	Q69YU5
-2.215743	0.3340970	8.720976	-6.632034	0.0001107	0.0306390	tissueV + locationR:tissueV	Q6UWY5
1.879984	0.3450203	7.948428	5.448910	0.0006231	0.0467105	tissueV + locationR:tissueV	Q6YN16
2.846025	0.4962872	8.101862	5.734634	0.0004167	0.0383466	tissueV + locationR:tissueV	Q9NRG4
-2.159706	0.4234569	8.947941	-5.100179	0.0006568	0.0472362	tissueV + locationR:tissueV	Q9UGT4
-3.155415	0.3785848	6.009860	-8.334763	0.0001606	0.0306390	tissueV + locationR:tissueV	Q9ULD0

---

Contrast: tissueV + 0.5 * locationR:tissueV = 0 ( 268 significant proteins)

logFC	se	df	t	pval	adjPval	contrast	feature
-1.8305581	0.4517810	7.322692	-4.051871	0.0044214	0.0406481	tissueV + 0.5 * locationR:tissueV	A5D6W6
1.5090670	0.2197832	8.392832	6.866161	0.0001027	0.0054800	tissueV + 0.5 * locationR:tissueV	A6NDG6
-2.3762212	0.3750414	8.894987	-6.335891	0.0001422	0.0064463	tissueV + 0.5 * locationR:tissueV	A6NMZ7
1.5175637	0.3523305	7.502814	4.307216	0.0030080	0.0320906	tissueV + 0.5 * locationR:tissueV	I3L505
0.7853044	0.2182987	9.101862	3.597384	0.0056669	0.0450418	tissueV + 0.5 * locationR:tissueV	O00151
-4.1654985	0.5938038	8.101862	-7.014941	0.0001044	0.0054800	tissueV + 0.5 * locationR:tissueV	O00180
-1.0261781	0.2612224	8.460940	-3.928370	0.0039113	0.0382260	tissueV + 0.5 * locationR:tissueV	O00264
1.8997136	0.4532858	6.953558	4.190984	0.0041418	0.0397259	tissueV + 0.5 * locationR:tissueV	O14531
1.2215442	0.2704104	9.062228	4.517371	0.0014272	0.0231479	tissueV + 0.5 * locationR:tissueV	O14949
-1.3275191	0.2276053	8.804095	-5.832549	0.0002711	0.0088410	tissueV + 0.5 * locationR:tissueV	O14967
-0.6500399	0.1696013	9.101862	-3.832752	0.0039280	0.0382260	tissueV + 0.5 * locationR:tissueV	O14980
-1.8040348	0.4150532	6.101862	-4.346514	0.0046539	0.0414146	tissueV + 0.5 * locationR:tissueV	O15118
-0.9243018	0.1962366	8.377248	-4.710141	0.0013424	0.0224557	tissueV + 0.5 * locationR:tissueV	O15230
-1.7434130	0.4573774	8.768121	-3.811760	0.0043455	0.0406481	tissueV + 0.5 * locationR:tissueV	O43175
-2.5217527	0.3184474	8.826306	-7.918899	0.0000269	0.0031935	tissueV + 0.5 * locationR:tissueV	O43677
0.9596508	0.2692947	8.791603	3.563571	0.0063182	0.0479267	tissueV + 0.5 * locationR:tissueV	O43678
1.0309632	0.2361313	9.050921	4.366059	0.0017833	0.0251723	tissueV + 0.5 * locationR:tissueV	O43920
1.7231972	0.3275796	6.101862	5.260392	0.0018043	0.0251723	tissueV + 0.5 * locationR:tissueV	O60503
-1.9045043	0.4110640	8.101862	-4.633109	0.0016260	0.0245912	tissueV + 0.5 * locationR:tissueV	O60760
1.5653993	0.3653890	9.101862	4.284199	0.0019854	0.0257664	tissueV + 0.5 * locationR:tissueV	O75190-3
-1.6228085	0.2021681	9.073544	-8.027026	0.0000205	0.0031935	tissueV + 0.5 * locationR:tissueV	O75368
1.3495773	0.3551325	8.774199	3.800208	0.0044164	0.0406481	tissueV + 0.5 * locationR:tissueV	O75394
1.1492265	0.2176706	8.920092	5.279659	0.0005225	0.0130293	tissueV + 0.5 * locationR:tissueV	O75489
-1.0383752	0.2707398	9.101862	-3.835325	0.0039124	0.0382260	tissueV + 0.5 * locationR:tissueV	O75828
0.7705458	0.2128445	9.098897	3.620229	0.0054701	0.0447714	tissueV + 0.5 * locationR:tissueV	O75947
0.6667616	0.1811330	9.101862	3.681061	0.0049706	0.0434929	tissueV + 0.5 * locationR:tissueV	O76031
2.1115014	0.2566404	8.120196	8.227469	0.0000327	0.0035070	tissueV + 0.5 * locationR:tissueV	O94875-10
-0.7426045	0.1803776	9.056240	-4.116944	0.0025754	0.0296989	tissueV + 0.5 * locationR:tissueV	O94919
0.9552882	0.2175959	9.009316	4.390194	0.0017407	0.0251649	tissueV + 0.5 * locationR:tissueV	O95182
0.6495623	0.1815071	8.988609	3.578715	0.0059548	0.0462289	tissueV + 0.5 * locationR:tissueV	O95202
-2.1673562	0.5721715	9.060729	-3.787949	0.0042442	0.0401285	tissueV + 0.5 * locationR:tissueV	O95445
-1.2481692	0.2207943	8.856414	-5.653087	0.0003314	0.0100162	tissueV + 0.5 * locationR:tissueV	O95865
-1.5817805	0.2988204	8.619427	-5.293415	0.0005747	0.0139815	tissueV + 0.5 * locationR:tissueV	O95980
-1.1718628	0.2249369	8.302260	-5.209741	0.0007218	0.0158243	tissueV + 0.5 * locationR:tissueV	P00325
-0.7676860	0.2016194	8.688856	-3.807600	0.0044465	0.0406916	tissueV + 0.5 * locationR:tissueV	P00352
-1.4371418	0.2875003	8.761433	-4.998749	0.0008027	0.0165426	tissueV + 0.5 * locationR:tissueV	P00748
-1.1792764	0.3195537	9.101862	-3.690386	0.0048988	0.0430532	tissueV + 0.5 * locationR:tissueV	P01008
-0.7051905	0.1908749	8.633485	-3.694517	0.0053324	0.0446980	tissueV + 0.5 * locationR:tissueV	P01024
-1.1289301	0.1940125	8.812190	-5.818851	0.0002748	0.0088410	tissueV + 0.5 * locationR:tissueV	P01031
-1.1935163	0.2686859	8.634765	-4.442050	0.0017921	0.0251723	tissueV + 0.5 * locationR:tissueV	P01034
-2.1723594	0.4400460	7.101862	-4.936664	0.0016127	0.0245912	tissueV + 0.5 * locationR:tissueV	P01042
-1.8448050	0.2885990	8.838508	-6.392279	0.0001369	0.0063507	tissueV + 0.5 * locationR:tissueV	P02452
-2.5362556	0.5847876	8.060977	-4.337054	0.0024441	0.0288176	tissueV + 0.5 * locationR:tissueV	P02461
-1.5151210	0.2385333	9.101862	-6.351822	0.0001262	0.0059959	tissueV + 0.5 * locationR:tissueV	P02743
-1.6498886	0.2569348	7.897172	-6.421430	0.0002161	0.0081350	tissueV + 0.5 * locationR:tissueV	P02747
-0.9752747	0.2168852	8.443832	-4.496733	0.0017558	0.0251723	tissueV + 0.5 * locationR:tissueV	P02748
-1.7257058	0.2634656	9.101862	-6.550022	0.0000999	0.0054800	tissueV + 0.5 * locationR:tissueV	P02775
-1.9398419	0.2382578	8.701032	-8.141778	0.0000235	0.0031935	tissueV + 0.5 * locationR:tissueV	P02776
-1.0189097	0.2469688	8.850829	-4.125662	0.0026700	0.0304017	tissueV + 0.5 * locationR:tissueV	P02790
0.9530644	0.2332480	8.886052	4.086056	0.0028073	0.0309427	tissueV + 0.5 * locationR:tissueV	P03928
-1.3389431	0.3391139	8.992472	-3.948358	0.0033686	0.0344635	tissueV + 0.5 * locationR:tissueV	P03950
-1.1775644	0.2448676	9.046926	-4.808984	0.0009476	0.0181773	tissueV + 0.5 * locationR:tissueV	P04003
-1.4339388	0.2187316	8.874482	-6.555699	0.0001114	0.0056974	tissueV + 0.5 * locationR:tissueV	P04004
-1.0216923	0.1906788	9.023664	-5.358185	0.0004534	0.0119008	tissueV + 0.5 * locationR:tissueV	P04083
-1.3295815	0.2117123	8.961183	-6.280132	0.0001471	0.0065195	tissueV + 0.5 * locationR:tissueV	P04196
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0.8761177	0.2070404	8.044852	4.231626	0.0028340	0.0309603	tissueV + 0.5 * locationR:tissueV	P06732
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1.2652262	0.2160210	9.076619	5.856959	0.0002338	0.0081853	tissueV + 0.5 * locationR:tissueV	P10109
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3.4291320	0.2659196	8.961545	12.895371	0.0000004	0.0004324	tissueV + 0.5 * locationR:tissueV	P12883
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1.9659315	0.4998205	8.843281	3.933275	0.0035611	0.0358804	tissueV + 0.5 * locationR:tissueV	Q6UXG3
1.6779231	0.2348892	7.948428	7.143466	0.0001009	0.0054800	tissueV + 0.5 * locationR:tissueV	Q6YN16
-1.8502103	0.3037069	6.749506	-6.092092	0.0005699	0.0139815	tissueV + 0.5 * locationR:tissueV	Q7L4S7
0.9781437	0.2370295	8.259053	4.126674	0.0030936	0.0324825	tissueV + 0.5 * locationR:tissueV	Q86SX6
-1.0312130	0.2347496	8.417349	-4.392821	0.0020424	0.0258671	tissueV + 0.5 * locationR:tissueV	Q86VP6
1.6232779	0.2800089	8.884228	5.797236	0.0002736	0.0088410	tissueV + 0.5 * locationR:tissueV	Q86VU5
-0.8964932	0.2191198	8.782030	-4.091338	0.0028555	0.0309603	tissueV + 0.5 * locationR:tissueV	Q86WV6
-2.3013725	0.4773629	5.822849	-4.821013	0.0031869	0.0332868	tissueV + 0.5 * locationR:tissueV	Q8IUX7
-2.4620725	0.5870544	6.101862	-4.193943	0.0055149	0.0447714	tissueV + 0.5 * locationR:tissueV	Q8IYU8
1.3896976	0.2370730	8.522844	5.861897	0.0002964	0.0092409	tissueV + 0.5 * locationR:tissueV	Q8N142
-2.3399412	0.2985644	7.809917	-7.837309	0.0000578	0.0048064	tissueV + 0.5 * locationR:tissueV	Q8N474
1.5502308	0.4158262	8.206023	3.728073	0.0055467	0.0447714	tissueV + 0.5 * locationR:tissueV	Q8NDY3
-1.1074458	0.2770091	8.353807	-3.997868	0.0036279	0.0362843	tissueV + 0.5 * locationR:tissueV	Q8TBP6
-1.3305900	0.2702645	9.101862	-4.923288	0.0007942	0.0165426	tissueV + 0.5 * locationR:tissueV	Q8TBQ9
-1.9589178	0.4274832	5.339988	-4.582444	0.0050412	0.0434999	tissueV + 0.5 * locationR:tissueV	Q8TDB4
-3.9115197	0.6989605	8.210719	-5.596196	0.0004672	0.0121043	tissueV + 0.5 * locationR:tissueV	Q8WWA0
-0.8768085	0.2506686	9.101862	-3.497879	0.0066302	0.0493558	tissueV + 0.5 * locationR:tissueV	Q8WWQ0
-1.2271315	0.2697670	8.056883	-4.548857	0.0018435	0.0251896	tissueV + 0.5 * locationR:tissueV	Q8WY22
0.7582849	0.1827664	9.055231	4.148930	0.0024556	0.0288176	tissueV + 0.5 * locationR:tissueV	Q8WZ42-6
-0.7637153	0.2072568	8.608757	-3.684874	0.0054375	0.0447714	tissueV + 0.5 * locationR:tissueV	Q8WZA9
-1.6517697	0.3264187	8.941812	-5.060278	0.0006945	0.0155679	tissueV + 0.5 * locationR:tissueV	Q92604
-1.4147847	0.3276653	7.376123	-4.317774	0.0030879	0.0324825	tissueV + 0.5 * locationR:tissueV	Q92681
1.1332866	0.2755797	8.666893	4.112374	0.0028475	0.0309603	tissueV + 0.5 * locationR:tissueV	Q92901
-0.8363213	0.2302598	8.258460	-3.632077	0.0063158	0.0479267	tissueV + 0.5 * locationR:tissueV	Q96CS3
-1.8012295	0.3486975	6.074530	-5.165594	0.0020073	0.0257664	tissueV + 0.5 * locationR:tissueV	Q96H79
-1.1141081	0.2889528	7.098897	-3.855675	0.0060804	0.0466550	tissueV + 0.5 * locationR:tissueV	Q96JM3
1.8556796	0.3949248	8.101862	4.698818	0.0014919	0.0238106	tissueV + 0.5 * locationR:tissueV	Q96MM6
-2.5116227	0.6199425	6.575046	-4.051380	0.0055444	0.0447714	tissueV + 0.5 * locationR:tissueV	Q99983
1.7602364	0.4235658	6.838224	4.155756	0.0044883	0.0407921	tissueV + 0.5 * locationR:tissueV	Q9BSD7
-1.2713736	0.2809809	8.941955	-4.524769	0.0014607	0.0235012	tissueV + 0.5 * locationR:tissueV	Q9BTV4
-1.4197138	0.3064904	8.993595	-4.632165	0.0012352	0.0216161	tissueV + 0.5 * locationR:tissueV	Q9BUF5
-2.1874881	0.3192589	8.713511	-6.851769	0.0000872	0.0054347	tissueV + 0.5 * locationR:tissueV	Q9BW30
-1.9946476	0.3752326	8.815856	-5.315763	0.0005182	0.0130293	tissueV + 0.5 * locationR:tissueV	Q9BXN1
1.8082293	0.3011429	8.590524	6.004556	0.0002427	0.0083427	tissueV + 0.5 * locationR:tissueV	Q9BXV9
2.8606788	0.6435677	8.260875	4.445032	0.0019900	0.0257664	tissueV + 0.5 * locationR:tissueV	Q9BZH6
0.8381193	0.2055612	8.812072	4.077225	0.0028947	0.0312154	tissueV + 0.5 * locationR:tissueV	Q9H3K6
0.8813365	0.2131791	9.101862	4.134253	0.0024826	0.0289634	tissueV + 0.5 * locationR:tissueV	Q9H479
0.7995998	0.2181824	8.839222	3.664823	0.0053579	0.0447086	tissueV + 0.5 * locationR:tissueV	Q9H511
1.5929612	0.3391585	6.585224	4.696804	0.0026091	0.0299151	tissueV + 0.5 * locationR:tissueV	Q9HAN9
1.9276046	0.3181520	8.868696	6.058753	0.0002001	0.0076763	tissueV + 0.5 * locationR:tissueV	Q9HAT2
1.2452221	0.3510809	8.861040	3.546824	0.0064031	0.0482045	tissueV + 0.5 * locationR:tissueV	Q9HBI1
1.4231716	0.2623353	8.027106	5.425011	0.0006199	0.0143800	tissueV + 0.5 * locationR:tissueV	Q9HBL0
-1.7641936	0.2986716	8.117006	-5.906801	0.0003395	0.0100769	tissueV + 0.5 * locationR:tissueV	Q9HCB6
3.7169599	0.6745865	6.101862	5.509983	0.0014200	0.0231479	tissueV + 0.5 * locationR:tissueV	Q9NNX1
0.9464329	0.2124863	8.595434	4.454089	0.0017820	0.0251723	tissueV + 0.5 * locationR:tissueV	Q9NQ50
1.0885171	0.2536324	9.001568	4.291711	0.0020142	0.0257664	tissueV + 0.5 * locationR:tissueV	Q9NQR4
1.6061320	0.3742759	8.676389	4.291304	0.0021939	0.0270175	tissueV + 0.5 * locationR:tissueV	Q9NQZ5
2.6879508	0.3790456	8.101862	7.091365	0.0000967	0.0054800	tissueV + 0.5 * locationR:tissueV	Q9NRG4
1.7110343	0.2798815	8.960305	6.113424	0.0001796	0.0070454	tissueV + 0.5 * locationR:tissueV	Q9NRX4
-1.0908770	0.3017781	8.549613	-3.614831	0.0061151	0.0467421	tissueV + 0.5 * locationR:tissueV	Q9NY15
-1.4909414	0.2897825	9.101862	-5.145036	0.0005859	0.0140350	tissueV + 0.5 * locationR:tissueV	Q9NZ01
-1.6955954	0.2335689	9.101862	-7.259509	0.0000449	0.0040759	tissueV + 0.5 * locationR:tissueV	Q9P2B2
-1.2345189	0.2942474	8.975693	-4.195513	0.0023360	0.0280057	tissueV + 0.5 * locationR:tissueV	Q9UBG0
-0.8421670	0.2248605	8.909531	-3.745286	0.0046708	0.0414146	tissueV + 0.5 * locationR:tissueV	Q9UBV8
-2.0180500	0.2973981	8.947941	-6.785685	0.0000826	0.0053657	tissueV + 0.5 * locationR:tissueV	Q9UGT4
0.8314862	0.2264309	8.165689	3.672141	0.0060708	0.0466550	tissueV + 0.5 * locationR:tissueV	Q9UI09
-2.3879560	0.6221547	7.915904	-3.838203	0.0050597	0.0434999	tissueV + 0.5 * locationR:tissueV	Q9UKR5
1.3104912	0.2330254	8.450170	5.623814	0.0004076	0.0111713	tissueV + 0.5 * locationR:tissueV	Q9UKS6
1.6515471	0.3473727	8.373938	4.754395	0.0012669	0.0217881	tissueV + 0.5 * locationR:tissueV	Q9UKX3
-1.0404703	0.2326813	8.625838	-4.471654	0.0017236	0.0251649	tissueV + 0.5 * locationR:tissueV	Q9ULC3
-1.6367984	0.3032371	6.009860	-5.397752	0.0016587	0.0248805	tissueV + 0.5 * locationR:tissueV	Q9ULD0
-1.9400017	0.3345045	7.941247	-5.799628	0.0004167	0.0111713	tissueV + 0.5 * locationR:tissueV	Q9ULL5-3
-1.4540488	0.3295815	5.855752	-4.411804	0.0047807	0.0422009	tissueV + 0.5 * locationR:tissueV	Q9UMR3
3.3929715	0.5461803	8.835699	6.212182	0.0001692	0.0068895	tissueV + 0.5 * locationR:tissueV	Q9UNW9
-1.0976046	0.2582386	9.101862	-4.250351	0.0020875	0.0258671	tissueV + 0.5 * locationR:tissueV	Q9Y287
-0.9420327	0.2092972	8.112523	-4.500933	0.0019306	0.0257664	tissueV + 0.5 * locationR:tissueV	Q9Y3B4
1.9053794	0.3421964	6.040851	5.568087	0.0013903	0.0229232	tissueV + 0.5 * locationR:tissueV	Q9Y3D0
1.1408963	0.1863299	9.101862	6.122989	0.0001664	0.0068895	tissueV + 0.5 * locationR:tissueV	Q9Y4W6
-2.1759766	0.6075687	8.933416	-3.581449	0.0059889	0.0463092	tissueV + 0.5 * locationR:tissueV	Q9Y5U8
1.2471920	0.3401055	9.101862	3.667074	0.0050804	0.0434999	tissueV + 0.5 * locationR:tissueV	Q9Y6G9
-0.9923430	0.2010924	8.989012	-4.934762	0.0008110	0.0165426	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 |> dplyr::filter(contrast == "tissueV + 0.5 * locationR:tissueV") |> plotVolcano() + ggtitle("V - A")

pn1 <- pe[, , "peptides_norm"] |>
  longForm(colvars = colnames(colData(pe))) |> 
  data.frame() |> 
  dplyr::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() |> 
  dplyr::filter(!is.na(value)) |>
  ggplot() + 
  aes(x = value,
      colour = sampleId,
      group = colname) +
  geom_density() +
  theme_minimal() +
    theme(legend.position = "NULL")+
   labs(subtitle = "Peptide intensities (common)\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() |> 
  dplyr::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() |> 
  dplyr::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 (common)\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() |> 
  dplyr::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() |> 
  dplyr::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)

pn1 + pn1c

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]] |> 
                       dplyr::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.

2 Interactive visualisation is possible with the altSEE app.

All the code chunks below are set at eval=FALSE. So you have to run these manually as they allow interactive visualization. Note, that the qfeatures object also needs to be imported in your session: So run all code chunks below before the interactive visualisation.

The following packages are needed for the visualization. The R chunk below is not evaluated by default. If you need/want to install the packages run the code chunk below manually.

bioc_pkgs <- c("scater", "iSEE", "iSEEu")
missing_bioc <- bioc_pkgs[!vapply(bioc_pkgs, requireNamespace, logical(1), quietly = TRUE)]
if (length(missing_bioc) > 0) {
  if (!requireNamespace("BiocManager", quietly = TRUE)) install.packages("BiocManager")
  BiocManager::install(missing_bioc)
}

if (!requireNamespace("altSEE", quietly = TRUE)) {
  if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
  remotes::install_github("statomics/altSEE")
}

We first convert the QFeatures object in a SingleCellExperiment

1. We get the selected set with colData, convert it to a SingleCellExperiment object and add a reduced dimensionality plot.
2. Unpack inference columns

library(scater)
library(QFeatures)
sce <- getWithColData(pe, i = "proteins") |> 
   as("SingleCellExperiment") |> 
   runMDS(exprs_values = 1) # 1. 
assayNames(sce)[1] <- "proteins"
rowData(sce) <- rowData(sce) |> 
  as.data.frame() |> 
  dplyr::select(where(is.atomic)) #2.  

Add altExps with peptide data.

1. We get the peptide sets, convert them to a SingleCellExperiment object and add a reduced dimensionality plot.
2. Unpack inference columns

setNames <- names(pe)
setNames <- setNames[setNames != "proteins"]
for (i in setNames)
{
altExp(sce,i) <- pe[[i]] |> 
     as("SingleCellExperiment") |> 
   runMDS(exprs_values = 1) # 1. 
assayNames(altExp(sce,i))[1] <- i
rowData(altExp(sce,i)) <- rowData(altExp(sce,i)) |> 
  as.data.frame() |> 
  dplyr::select(where(is.atomic)) #2.  
}

Setup iSEE panels

library(iSEE)
library(iSEEu)
library(altSEE)

rdp <- ReducedDimensionPlot(PointSize = 3, PanelWidth = 6L)
ardp <- AltReducedDimensionPlot(PointSize = 3, Experiment = "peptides_norm", PanelWidth = 6L)
vp <- VolcanoPlot(PointSize = 3, PanelWidth = 6L)
avp <- AltVolcanoPlot(PointSize = 3, Experiment = "peptides_norm", PanelWidth = 6L)
rdt <- RowDataTable(RowSelectionSource = "VolcanoPlot1", PanelWidth = 6L)
ardt <- AltRowDataTable(Experiment = "peptides_norm",
                        RowSelectionSource = "AltVolcanoPlot1", 
                           PanelWidth = 6L)
mlfap <- LinkedFeaturesAssayPlot(
  SelectionExperiment = "(Main)",
  Experiment = "(Main)",
  AltAssay = "proteins",
  YAxisFeatureSource = "RowDataTable1",
  PlotType = "Scatter + lines",
  XAxis = "Column data",
  XAxisColumnData = "sampleId",
  PanelWidth = 6L,
  LookupColumn       = "Proteins",
  MapColumn          = "Proteins"
  )

alfap <- LinkedFeaturesAssayPlot(
  YAxisFeatureSource = "RowDataTable1",
  SelectionExperiment = "(Main)",
  PlotType = "Scatter + lines",
  XAxis = "Column data",
  XAxisColumnData = "sampleId",
  PanelWidth = 6L,
  LookupColumn = "Proteins",
  MapColumn = "Proteins",
  Experiment = "peptides_norm",
  AltAssay = "peptides_norm"
  )

initial_panels <- list(rdp, ardp, vp, avp, rdt, ardt, mlfap, alfap)

Launch the app

app <- iSEE(
    sce,
    initial = initial_panels
)

if (interactive()) {
    shiny::runApp(app, launch.browser = TRUE)
}

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.