This document contains the statistical analysis performed in the publication Pathobiochemical signatures of cholestatic liver disease in bile duct ligated mice.

A comprehensive data set of serum markers, histological parameters and transcript profiles was compiled at 8 time points after bile duct ligation (BDL) in mice, comprising different stages of the disease. The data set consists of \(N_{r}=5\) repeats (\(N_{r}=3\) for CTGF, \(\alpha\)-SMA and S100a4) for \(N_{t}=8\) time points denoted by \(t_1,..., t_{N_t}\) consisting of a total \(N_{f}=153\) measured parameters in the following referred to as factors (Fluidigm gene expression, biochemical markers, and (immuno-)histochemical measurements).

The following naming conventions are used

The main steps of the analysis comprise

The HTML version of this document is available from
All data sets, source code and documentation are provided at

All results of this analysis are written to the results directory defined via the BDL_RESULTS environment variable. To reproduce this analysis create the respective variable and run the Koenig_BDL_analysis.Rmd file.

# read results directory from environment variable
resultsPath <- Sys.getenv("BDL_RESULTS")
if (identical(resultsPath, "")){
  stop("No results directory defined, set the BDL_RESULTS environment variable")
# create directory to store data sets of analysis
dir.create(file.path(resultsPath, 'data'), showWarnings=FALSE)

Explorative data analysis

Data import

In a first step the processed data sets are loaded from the data folder: These are the time course data for all factors (BDLdata), additional information for the factors (BDLfactors), the sample definition, i.e. the assignment of sample ids to respective time point and repeat (BDLsamples), and a mapping of the Fluidigm (gene) probe ids to UniProt identifiers and names (BDLprobes).

dir.create(file.path(resultsPath, 'control'), showWarnings=FALSE)
# path definition
baseLoc <- system.file(package="BDLanalysis")
extPath <- file.path(baseLoc, "extdata")
# load data
# counters
Nr <- 5  # repeats
Nt <- length(levels(BDLsamples$time_fac))  # Nt=8 time points
# store all data sets in the results folder
save(BDLdata, file=file.path(resultsPath, "data", "BDLdata.Rdata"))
save(BDLsamples, file=file.path(resultsPath, "data", "BDLsamples.Rdata"))
save(BDLfactors, file=file.path(resultsPath, "data", "BDLfactors.Rdata"))
save(BDLprobes, file=file.path(resultsPath, "data", "BDLprobes.Rdata"))

In addition to the individual sample data, the mean data averaged over the \(N_{r}\) repeats per time points is used in parts of the analysis. The mean factor data set is calculated once via

BDLmean <- bdl_mean_data(BDLdata, BDLsamples)
BDLmean.time <- as.numeric(levels(as.factor(BDLsamples$time)))

In total 153 factors were measured in the this BDL study falling in the categories: Biochemistry, GE_ADME, GE_Cytokines, GE_Fibrosis, Histochemistry. The majority of factors belongs hereby to the 3 fluidigm chips with 47 probes per chip (one probe was filtered from GE fibrosis during preprocessing of the chips).

An overview of the number of factors per category is provided in the following table

cat_table <-$ftype))
colnames(cat_table) <- c("Category", "Freq")
set.caption(sub(".", " ", "Factors per category", fixed = TRUE))
Factors per category
Category Freq
Biochemistry 4
GE_Cytokines 47
GE_Fibrosis 46
Histochemistry 9

Gene Probes

In the following an overview of the gene probes is given providing full names and links to UniProt.

# create data frame with probe information
create_probe_df <- function() {
  # get the gene factors
  f_names <- colnames(BDLdata)[BDLfactors$ftype.short == ""]
  # get the probe information
  Nf = length(f_names)
  probe_names <- rep(NA, length=Nf)
  probe_uniprot <- rep(NA, length=Nf)
  probe_genes <- rep(NA, length=Nf)
  names(probe_names) <- f_names
  names(probe_uniprot) <- f_names
  names(probe_genes) <- f_names
  for (name in colnames(BDLdata)){
      probe_info <- ProbeInformation(name)
      if (!is.null(probe_info)){
        if (!is.null(probe_info${
          probe_names[name] <- probe_info$
          probe_uniprot[name] <- probe_info$Entry
          probe_genes[name] <- probe_info$  
  probe.df <- data.frame(name=probe_names, genes=probe_genes, uniprot=probe_uniprot)
  # sort
  probe.df <- probe.df[order(rownames(probe.df)), ]
  # remove the double Act probes
  probe.df <- probe.df[!(rownames(probe.df) %in% c("Actb.x", "Actb.y")) , ]  
# print probe information
probe.df <- create_probe_df()

                                           name                                    genes uniprot
Abcb1a          Multidrug resistance protein 1A  Abcb1a, Abcb4, Mdr1a, Mdr3, Pgy-3, Pgy3  P21447
Abcc2    Canalicular multispecific organic a...                                    Abcc2  Q8VI47
Abcg2    ATP-binding cassette sub-family G m...                       Abcg2, Abcp, Bcrp1  Q7TMS5
Acta2               Actin, aortic smooth muscle                      Acta2, Actsa, Actvs  P62737
Actb                       Actin, cytoplasmic 1                                     Actb  P60710
Ahr                   Aryl hydrocarbon receptor                                      Ahr  P30561
Bad      Bcl2-associated agonist of cell dea...                                Bad, Bbc6  Q61337
Bak1         Bcl-2 homologous antagonist/killer                                Bak1, Bak  O08734
Bax                     Apoptosis regulator BAX                                      Bax  Q07813
Bcl2l11                   Bcl-2-like protein 11                             Bcl2l11, Bim  O54918
Birc5    Baculoviral IAP repeat-containing p...                        Birc5, Api4, Iap4  O70201
Ccl2                      C-C motif chemokine 2                    Ccl2, Je, Mcp1, Scya2  P10148
Ccl3                      C-C motif chemokine 3                       Ccl3, Mip1a, Scya3  P10855
Ccl4                      C-C motif chemokine 4                       Ccl4, Mip1b, Scya4  P14097
Ccl5                      C-C motif chemokine 5                              Ccl5, Scya5  P30882
Ccl7                      C-C motif chemokine 7                   Ccl7, Fic, Mcp3, Scya7  Q03366
Ccl8                      C-C motif chemokine 8                        Ccl8, Mcp2, Scya8  Q9Z121
Ccr2              C-C chemokine receptor type 2                             Ccr2, Cmkbr2  P51683
Ccr3     Probable C-C chemokine receptor typ...                   Ccr3, Cmkbr1l2, Cmkbr3  P51678
Ccr5              C-C chemokine receptor type 5                             Ccr5, Cmkbr5  P51682
Cd14     Monocyte differentiation antigen CD...                                     Cd14  P10810
Cd69              Early activation antigen CD69                                     Cd69  P37217
Cd86     T-lymphocyte activation antigen CD8...                                     Cd86  P42082
Cdh1                                 Cadherin-1                                     Cdh1  P09803
Cdh2                                 Cadherin-2                                     Cdh2  P15116
Cebpa    CCAAT/enhancer-binding protein alph...                              Cebpa, Cebp  P53566
Cebpb       CCAAT/enhancer-binding protein beta                                    Cebpb  P28033
Cebpd    CCAAT/enhancer-binding protein delt...                              Cebpd, Crp3  Q00322
Ch25h                Cholesterol 25-hydroxylase                                    Ch25h  Q9Z0F5
Col1a1                Collagen alpha-1(I) chain                            Col1a1, Cola1  P11087
Col3a1              Collagen alpha-1(III) chain                                   Col3a1  P08121
Col4a3               Collagen alpha-3(IV) chain                                   Col4a3  Q9QZS0
Col6a6               Collagen alpha-6(VI) chain                                   Col6a6  Q8C6K9
Col8a1             Collagen alpha-1(VIII) chain                                   Col8a1  Q00780
Ctgf            Connective tissue growth factor       Ctgf, Ccn2, Fisp-12, Fisp12, Hcs24  P29268
Cxcl1            Growth-regulated alpha protein            Cxcl1, Gro, Gro1, Mgsa, Scyb1  P12850
Cxcl15                 C-X-C motif chemokine 15                           Cxcl15, Scyb15  Q9WVL7
Cxcl2                   C-X-C motif chemokine 2                Cxcl2, Mip-2, Mip2, Scyb2  P10889
Cxcl3                   C-X-C motif chemokine 3                     Cxcl3, Dcip1, Gm1960  Q6W5C0
Cxcl5                   C-X-C motif chemokine 5                             Cxcl5, Scyb5  P50228
Cxcr1           C-X-C chemokine receptor type 1                             Cxcr1, Il8ra  Q810W6
Cxcr2           C-X-C chemokine receptor type 2             Cxcr2, Cmkar2, Gpcr16, Il8rb  P35343
Cyp1a2                      Cytochrome P450 1A2                          Cyp1a2, Cyp1a-2  P00186
Cyp24a1  1,25-dihydroxyvitamin D(3) 24-hydro...                   Cyp24a1, Cyp-24, Cyp24  Q64441
Cyp2b10                    Cytochrome P450 2B10               Cyp2b10, Cyp2b-10, Cyp2b20  P12791
Cyp2c29                    Cytochrome P450 2C29                                  Cyp2c29  Q64458
Cyp2c37                    Cytochrome P450 2C37                                  Cyp2c37  P56654
Cyp2c39                    Cytochrome P450 2C39                                  Cyp2c39  P56656
Cyp2e1                      Cytochrome P450 2E1                   Cyp2e1, Cyp2e, Cyp2e-1  Q05421
Cyp3a11                    Cytochrome P450 3A11                        Cyp3a11, Cyp3a-11  Q64459
Cyp4a10                    Cytochrome P450 4A10                        Cyp4a10, Cyp4a-10  O88833
Cyp7a1        Cholesterol 7-alpha-monooxygenase                             Cyp7a1, Cyp7  Q64505
Dpyd     Dihydropyrimidine dehydrogenase [NA...                                     Dpyd  Q8CHR6
Edn1                               Endothelin-1                                     Edn1  P22387
Egf                 Pro-epidermal growth factor                                      Egf  P01132
Egfr           Epidermal growth factor receptor                                     Egfr  Q01279
Fasl     Tumor necrosis factor ligand superf... Faslg, Apt1lg1, Cd95l, Fasl, gld, Tnfsf6  P41047
Fn1                                 Fibronectin                                      Fn1  P11276
Gdf2            Growth/differentiation factor 2                               Gdf2, Bmp9  Q9WV56
Gsta2              Glutathione S-transferase A2                                    Gsta2  P10648
Gstm1            Glutathione S-transferase Mu 1                                    Gstm1  P10649
Gstp1             Glutathione S-transferase P 1                            Gstp1, Gstpib  P19157
Hgf           Hepatocyte growth factor receptor                                      Met  P16056
Hk2                                Hexokinase-2                                      Hk2  O08528
Hmox1                          Heme oxygenase 1                                    Hmox1  P14901
Hnf4a         Hepatocyte nuclear factor 4-alpha         Hnf4a, Hnf-4, Hnf4, Nr2a1, Tcf14  P49698
Ifna1                        Interferon alpha-1                                    Ifna1  P01572
Ifnar1         Interferon alpha/beta receptor 1                      Ifnar1, Ifar, Ifnar  P33896
Ifnar2         Interferon alpha/beta receptor 2                                   Ifnar2  O35664
Ifnb1                           Interferon beta                         Ifnb1, Ifb, Ifnb  P01575
Ifng                           Interferon gamma                                     Ifng  P01580
Igf1               Insulin-like growth factor I                              Igf1, Igf-1  P05017
Il10                             Interleukin-10                              Il10, Il-10  P18893
Il10ra   Interleukin-10 receptor subunit alp...                            Il10ra, Il10r  Q61727
Il10rb   Interleukin-10 receptor subunit bet...                            Il10rb, Crfb4  Q61190
Il13                             Interleukin-13                              Il13, Il-13  P20109
Il17a                           Interleukin-17A                       Il17a, Ctla8, Il17  Q62386
Il1b                         Interleukin-1 beta                                     Il1b  P10749
Il1rn    Interleukin-1 receptor antagonist p...                            Il1rn, Il-1ra  P25085
Il2                               Interleukin-2                                Il2, Il-2  P04351
Il28b                       Interferon lambda-3                       Ifnl3, Il28, Il28b  Q8CGK6
Il4                               Interleukin-4                                Il4, Il-4  P07750
Il6                               Interleukin-6                                Il6, Il-6  P08505
Il6st       Interleukin-6 receptor subunit beta                                    Il6st  Q00560
Lama1                   Laminin subunit alpha-1                      Lama1, Lama, Lama-1  P19137
Met           Hepatocyte growth factor receptor                                      Met  P16056
Mki67    MKI67 FHA domain-interacting nucleo...                            Nifk, Mki67ip  Q91VE6
Mrc1              Macrophage mannose receptor 1                                     Mrc1  Q61830
Nes                                      Nestin                                      Nes  Q6P5H2
Nfkbia               NF-kappa-B inhibitor alpha                             Nfkbia, Ikba  Q9Z1E3
Nos2           Nitric oxide synthase, inducible                              Nos2, Inosl  P29477
Notch1   Neurogenic locus notch homolog prot...                            Notch1, Motch  Q01705
Notch3   Neurogenic locus notch homolog prot...                                   Notch3  Q61982
Nr0b2    Nuclear receptor subfamily 0 group ...                               Nr0b2, Shp  Q62227
Nr1h3             Oxysterols receptor LXR-alpha                              Nr1h3, Lxra  Q9Z0Y9
Nr1i2    Nuclear receptor subfamily 1 group ...                               Nr1i2, Pxr  O54915
Nr1i3    Nuclear receptor subfamily 1 group ...                               Nr1i3, Car  O35627
Nr2f1               COUP transcription factor 1                   Nr2f1, Erbal3, Tfcoup1  Q60632
Nr2f2               COUP transcription factor 2      Nr2f2, Aporp1, Arp-1, Arp1, Tfcoup2  P43135
Nr3c1                   Glucocorticoid receptor                         Nr3c1, Grl, Grl1  P06537
Osm                                Oncostatin-M                                      Osm  P53347
Osmr     Oncostatin-M-specific receptor subu...                              Osmr, Osmrb  O70458
Pde4a    cAMP-specific 3',5'-cyclic phosphod...                                    Pde4a  O89084
Pde4b    cAMP-specific 3',5'-cyclic phosphod...                                    Pde4a  O89084
Pde4d    cAMP-specific 3',5'-cyclic phosphod...                                    Pde4d  Q01063
Pdgfb    Platelet-derived growth factor subu...                               Pdgfb, Sis  P31240
Por            NADPH--cytochrome P450 reductase                                      Por  P37040
Ppara    Peroxisome proliferator-activated r...                       Ppara, Nr1c1, Ppar  P23204
Pparg    Peroxisome proliferator-activated r...                             Pparg, Nr1c3  P37238
Prom1                                Prominin-1                      Prom1, Prom, Proml1  O54990
Pten     Phosphatidylinositol 3,4,5-trisphos...                              Pten, Mmac1  O08586
Ptgs2              Prostaglandin G/H synthase 2        Ptgs2, Cox-2, Cox2, Pghs-b, Tis10  Q05769
Rarres1            Cytosolic carboxypeptidase 2                              Agbl2, Ccp2  Q8CDK2
Rps18                 40S ribosomal protein S18                                    Rps18  P62270
Rxra           Retinoic acid receptor RXR-alpha                              Rxra, Nr2b1  P28700
Slc10a1          Sodium/bile acid cotransporter                            Slc10a1, Ntcp  O08705
Smad6    Mothers against decapentaplegic hom...              Smad6, Madh6, Madh7, Msmad6  O35182
Smad7    Mothers against decapentaplegic hom...                      Smad7, Madh7, Madh8  O35253
Socs1        Suppressor of cytokine signaling 1                       Socs1, Cish1, Ssi1  O35716
Socs3        Suppressor of cytokine signaling 3                       Socs3, Cis3, Cish3  O35718
Sod2     Superoxide dismutase [Mn], mitochon...                              Sod2, Sod-2  P09671
Sparc                                     SPARC                                    Sparc  P07214
Sult1a1                    Sulfotransferase 1A1                Sult1a1, St1a1, Stp, Stp1  P52840
Sult1b1  Sulfotransferase family cytosolic 1...                                  Sult1b1  Q9QWG7
Tgfb1         Transforming growth factor beta-1                                    Tgfb1  P04202
Tgfb2         Transforming growth factor beta-2                                    Tgfb2  P27090
Tgfbr2                 TGF-beta receptor type-2                                   Tgfbr2  Q62312
Timp1             Metalloproteinase inhibitor 1                      Timp1, Timp, Timp-1  P12032
Timp2             Metalloproteinase inhibitor 2                            Timp2, Timp-2  P25785
Tnc                                    Tenascin                                 Tnc, Hxb  Q80YX1
Tnf                       Tumor necrosis factor                        Tnf, Tnfa, Tnfsf2  P06804
Tnfrsf1a Tumor necrosis factor receptor supe...                  Tnfrsf1a, Tnfr-1, Tnfr1  P25118
Tnfrsf1b Tumor necrosis factor receptor supe...                  Tnfrsf1b, Tnfr-2, Tnfr2  P25119
Ugt1a1          UDP-glucuronosyltransferase 1-1                             Ugt1a1, Ugt1  Q63886
Vdr                         Vitamin D3 receptor                               Vdr, Nr1i1  P48281
Wisp1    WNT1-inducible-signaling pathway pr...                        Wisp1, Ccn4, Elm1  O54775
Xiap           E3 ubiquitin-protein ligase XIAP            Xiap, Aipa, Api3, Birc4, Miha  Q60989

Data visualization

Single chip data

Create heatmap of the mean chip data


data_ADME = t(as.matrix(BDLmean[BDLfactors$ftype == "GE_ADME"]))
data_ADME <- data_ADME[rownames(data_ADME) != "Actb.x", ]

data_Cytokines = t(as.matrix(BDLmean[BDLfactors$ftype == "GE_Cytokines"]))
data_Cytokines <- data_Cytokines[rownames(data_Cytokines) != "Actb.y", ]

data_Fibrosis = t(as.matrix(BDLmean[BDLfactors$ftype == "GE_Fibrosis"]))
data_Fibrosis <- data_Fibrosis[rownames(data_Fibrosis) != "Actb", ]

# fold changes relative to sham mice (0 h) displayed in log2 scale
log2_to_control <- function(data){
  data <- data[sort(rownames(data)), ]  # sort by name
  reldata <- data[, 2:ncol(data)]/data[, 1]
  log2_reldata <- log2(reldata)  

data_list <- list(ADME=data_ADME, 
for (name in names(data_list)){
  data <- data_list[[name]]
  cor_colors <- HeatmapColors() # color palette for correlation (red - white - blue)
  hmap_colors <- cor_colors(100)
  # plot to file
  pdf(file.path(resultsPath, "control", paste("chip_heatmap_", name, ".pdf", sep="")), 
      width=8, height=10, pointsize=12) 
  heatmap.2(log2_to_control(data), col=hmap_colors, breaks=seq(from=-2, to=2, length.out=length(hmap_colors)+1),
            key=TRUE, symkey=FALSE, key.xlab = "log2",
            trace="none", cexRow=0.8, cexCol=0.8,
  "none", dendrogram="none", 
            Rowv=NULL, Colv=NULL, 

Time course of single factors

To get an overview over the BDL data set plots of the raw and mean data for all individual factors over time were generated. These are available in the resultsPath/factors folder under the respective names of the factors

# Create figures for all factors
factors_path <- file.path(resultsPath, 'factors')
dir.create(factors_path, showWarnings=FALSE)

An example plot for a single factor is depicted for bilirubin. A continous increase in bilirubin can be observed after BDL.

plot_single_factor('bilirubin', path=NULL)
Figure: Bilrubin timecourse. Plot of the raw time course data for bilirubin. On the left the data is plotted against the time [h], on the right against the different time classes. Individual data points are depecticed in blue with the respective sample number shown next to the data points. The mean averaged of the repeats per time point are depicted in red.

Figure: Bilrubin timecourse. Plot of the raw time course data for bilirubin. On the left the data is plotted against the time [h], on the right against the different time classes. Individual data points are depecticed in blue with the respective sample number shown next to the data points. The mean averaged of the repeats per time point are depicted in red.

Time course of all factors

Next the heatmap overview of the complete data set is generated, i.e. the data of all factors for of all time points and repeats. This provides a first overview over the complete data set.

# define horizontal and vertical helper lines
v_lines <- ((1:Nt)*Nr+0.5)
factor_types <- c("Histochemistry", "Biochemistry", 
                  "GE_Fibrosis", "GE_Cytokines", "GE_ADME")
factor_table <- table(BDLfactors$ftype)
h_lines <- 0.5 + cumsum(factor_table[factor_types])

timecourse_heatmap <- function(){
  # create better row names
  dtmp <- BDLdata
  rownames(dtmp) <- paste(rownames(BDLsamples), BDLsamples$time_fac, sep=" ")
  # heatmap colors 
  hmap_colors <- HeatmapColors()
  # colors for factor groups
  colorset <- brewer.pal(length(factor_types), "Set2") <- function(factor_id) {
  factorColors <- unlist(lapply(BDLfactors$id,
  # heatmap
  heatmap.2(t(as.matrix(dtmp)), col=hmap_colors(100), scale="row", 
            dendrogram="none", Rowv=NULL, Colv=NULL,
            key=TRUE, trace="none", cexRow=0.5, keysize=0.8,"none",
            add.expr=abline(v=v_lines, h=h_lines, col="black", lwd=0.5),
            main="Heatmap of BDL time course data")
            # xlab="sample", ylab="factor")
  # legend
      legend = rev(factor_types), # category labels
      col = rev(colorset),  # color key
      lty= 1, lwd = 10, cex = 0.7, bty="n"
# plot to file
pdf(file.path(resultsPath, "control", "timecourse_heatmap.pdf"), 
    width=10, height=10, pointsize=12) 
Figure: Timecourse heatmap of all factors. Rows correspond to individual factors, with factor order corresponding to the original data set: `GE_ADME`, `GE_Cytokines`, `GE_Fibrosis`, `Biochemistry`, `Histochemistry`. Columns correspond to the 40 samples with 5 subsequent samples belonging to one of the 8 time points. The data is row scaled, i.e. every individual factor is scaled to have mean zero and standard deviation one.

Figure: Timecourse heatmap of all factors. Rows correspond to individual factors, with factor order corresponding to the original data set: GE_ADME, GE_Cytokines, GE_Fibrosis, Biochemistry, Histochemistry. Columns correspond to the 40 samples with 5 subsequent samples belonging to one of the 8 time points. The data is row scaled, i.e. every individual factor is scaled to have mean zero and standard deviation one.

Results: Various patterns are visible in the plotted raw data set:

  • Two main classes of time course response are observed. One class with an increase in the early phase up to 6h after BDL (many of the ADME genes fall in this class) and a second class increasing in the later stage after 2-5 days after BDL. Many of the genes on the Cytokines and Fibrosis chips as well as some of the biochemical and (immuno-)histochemical factors fall in this second class.
  • The individual animals show heterogeneous responses to BDL. Within one time point the 5 repeats can show very different patterns. For instance at time 6h after BDL 3/5 of the mice show a marked increase in the ADME genes, whereas 2/5 do not show such a marked increase. Another example is the mice sample 27 at time 2d, with a high increase in the genes on the Fibrosis chip, which is not observed in the other 4 samples at time 2d.

Actb quality control

Actb (Actin, cytoplasmic 1) probes were included on all Fluidigm chips (GE_ADME, GE_Cytokines, GE_Fibrosis) and not used in the normalization of the transcription data. Hence, ActB can serve as quality control for the technical reproducibility of the Fluidigm chips. If the data is reproducible between chips the pairwise correlation between all individual Actb measurements should have high correlation coefficients close to 1. Plotting the data of the Actb measurements of two chips against each other should lie on a straight line.

# Actb control figure
plot_actb_control <- function(){
  plot_cor_pair("Actb", "Actb.x", single_plots=FALSE)
  plot_cor_pair("Actb", "Actb.y", single_plots=FALSE)
  plot_cor_pair("Actb.x", "Actb.y", single_plots=FALSE)

# plot to file
pdf(file.path(resultsPath, "control", "Actb_control.pdf"), 
    width=10, height=6, pointsize=12)  

# calculate Spearman and Pearson correlation coefficients on N=8*5=40 data points 
actb.spearman <- cor(data.frame(Actb=BDLdata$Actb, 
                                Actb.y=BDLdata$Actb.y), method="spearman")
actb.pearson <- cor(data.frame(Actb=BDLdata$Actb, 
                               Actb.y=BDLdata$Actb.y), method="pearson")

# table of correlation coefficients
set.caption(sub(".", " ", "Spearman correlation of Actb controls", fixed = TRUE))
pander(round(actb.spearman, digits=3))
Spearman correlation of Actb controls
  Actb Actb.x Actb.y
Actb 1 0.908 0.944
Actb.x 0.908 1 0.917
Actb.y 0.944 0.917 1
set.caption(sub(".", " ", "Pearson correlation of Actb controls", fixed = TRUE))
pander(round(actb.pearson, digits=3))
Pearson correlation of Actb controls
  Actb Actb.x Actb.y
Actb 1 0.945 0.938
Actb.x 0.945 1 0.935
Actb.y 0.938 0.935 1