Diabetes data

Description

A data.frame with a design and transcriptomic data.

Usage

data(Diabetes)

Details

Clinical study on humans was performed as a 2-way factorial design with two factors both on two levels: bariatric surgery on two levels (before and after the bariatric surgery) and type 2 diabetes (T2D) on two levels (with and without T2D). There were 8 patients without T2D and 7 with T2D. It was discovered that the patients with T2D would be separated in two groups: 3 patients in the group called T2D1 and 4 patients in the group called T2D2. The experiment can therefore also analysed as 2 way factorial design where the disease factor is on three levels. All patients were obese before bariatric surgery (BMI >45). Transcriptome in the subcutaneous adipose tissue were obtained before and one year after bariatric surgery.

Author(s)

Ellen Færgestad Mosleth

References

Dankel et al. 2010. Switch from Stress Response to Homeobox Transcription Factors in Adipose Tissue After Profound Fat Loss. Plos One 5.

Examples

data(Diabetes)
str(Diabetes)

Effect + Residual Modelling

Description

Effect + Residual Modelling

Usage

ER(formula, data)

## S3 method for class 'ER'
print(x, ...)

## S3 method for class 'ER'
plot(
  x,
  y = 1,
  what = "raw",
  col = NULL,
  pch = NULL,
  model.line = (what %in% c("raw")),
  ylim = NULL,
  ylab = "",
  xlab = "",
  main = NULL,
  ...
)

tableER(object, variable)

Arguments

Value

ER returns an object of class ER containing effects, ER values, fitted values, residuals, features, coefficients, dummy design, symbolic design, dimensions, highest level interaction and feature names.

References

* Mosleth et al. (2021) Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis. Scientific Report, 11,4087. <doi:10.1038/s41598-021-82388-w>

* E.F. Mosleth et al. (2020). Comprehensive Chemometrics, 2nd edition; Brown, S., Tauler, R., & Walczak, B. (Eds.). Chapter 4.22. Analysis of Megavariate Data in Functional Omics. Elsevier. <doi:10.1016/B978-0-12-409547-2.14882-6>

See Also

Analyses using ER: elastic and pls. Confidence interval plots confints.

Examples

## Multiple Sclerosis
data(MS, package = "ER")
er <- ER(proteins ~ MS * cluster, data = MS)
print(er)
plot(er)                                       # Raw data, first feature
plot(er,2)                                     # Raw data, numbered feature
plot(er,'Q76L83', col='MS', pch='cluster')     # Selected colour and plot character
plot(er,'Q76L83', what='effect MS',
     model.line='effect cluster')              # Comparison of factors (points and lines)

  # Example compound plot
  old.par <- par(c("mfrow", "mar"))
  # on.exit(par(old.par))
  par(mfrow = c(3,3), mar = c(2,4,4,1))
  plot(er,'Q76L83')                                  # Raw data, named feature
  plot(er,'Q76L83', what='fits')                     # Fitted values
  plot(er,'Q76L83', what='residuals')                # Residuals
  plot(er,'Q76L83', what='effect MS')                # Effect levels
  plot(er,'Q76L83', what='effect cluster')           # ----||----
  plot(er,'Q76L83', what='effect MS:cluster')        # ----||----
  plot(er,'Q76L83', what='MS')                       # ER values
  plot(er,'Q76L83', what='cluster')                  # --------||---------
  plot(er,'Q76L83', what='MS:cluster')               # --------||---------
  par(old.par)


# Complete overview of ER
tab <- tableER(er, 1)

# In general there can be more than two, effects, more than two levels, and continuous effects:
# MS$three <- factor(c(rep(1:3,33),1:2))
# er3    <- ER(proteins ~ MS * cluster + three, data = MS)


## Lactobacillus
data(Lactobacillus, package = "ER")
erLac <- ER(proteome ~ strain * growthrate, data = Lactobacillus)
print(erLac)
plot(erLac)                            # Raw data, first feature
plot(erLac,2)                          # Raw data, numbered feature
plot(erLac,'P.LSA0316', col='strain',
    pch='growthrate')                  # Selected colour and plot character
plot(erLac,'P.LSA0316', what='strain',
    model.line='growthrate')           # Selected model.line


## Diabetes
data(Diabetes, package = "ER")
erDia <- ER(transcriptome ~ surgery * T2D, data = Diabetes)
print(erDia)
plot(erDia)                            # Raw data, first feature
plot(erDia,2)                          # Raw data, numbered feature
plot(erDia,'ILMN_1720829', col='surgery',
    pch='T2D')                         # Selected colour and plot character

Lactobacillus data

Description

A data.frame with a design and proteomic data, transcriptomic data and phenotypic data.

Usage

data(Lactobacillus)

Details

Experiment on Lactobacillus sakei was performed as a 2-way factorial design with two factors both on two levels: strain (L. sakei strains LS25 and 23K) (factor A) and growth condition (high and low glucose availability) (factor B) both on two levels, and their interaction term (factor AB). There were three biological replicates within each group. Transcriptome, proteome and end product profile (lactate, formate, acetate and ethanol) were observed.

Author(s)

Ellen Færgestad Mosleth

References

McLeod et al. 2017. Effects of glucose availability in Lactobacillus sakei; metabolic change and regulation of the proteome and transcriptome. Plos One 12, e0187542.

Examples

data(Lactobacillus)
str(Lactobacillus)

Multiple Sclerosis data

Description

A data.frame with a design and proteomic data.

Usage

data(MS)

Details

Data from biobank are analysed a study population of 101 patients, 37 were diagnosed with multiple sclerosis, and 64 without multiple sclerosis. Of the patients without multiple sclerosis, 50 were diagnosed with other neurological disorders and 14 were neurologically healthy patients who had undergone spinal anaesthesia for orthopaedic surgery on the knee or ankle, i.e. neurologically healthy controls. Unless otherwise stated, all the patients without multiple sclerosis were considered as controls for this study. All patients with multiple sclerosis had relapsing remitting multiple sclerosis. The proteome were obtained on cerebrospinal fluid samples from all patients prior medical treatment for multiple sclerosis. It was discovered the patients separated into two clusters, called cluster 1 and cluster 2. This is utilised in the data analysis by considering the data as 2-way factorial design with the two factors: MS and clusters both on two levels.

Author(s)

Ellen Færgestad Mosleth

References

* Opsahl, J.A. et al. Label-free analysis of human cerebrospinal fluid addressing various normalization strategies and revealing protein groups affected by multiple sclerosis. Proteomics 16, 1154-1165 (2016).

* Ellen Færgestad Mosleth, Christian Alexander Vedeler, Kristian Hovde Liland, Anette McLeod, Gerd Haga Bringland, Liesbeth Kroondijk, Frode Berven, Artem Lysenko, Christopher J. Rawlings, Karim El-Hajj Eid, Jill Anette Opsahl, Bjørn Tore Gjertsen, Kjell-Morten Myhr and Sonia Gavasso, Cerebrospinal fluid proteome shows disrupted neuronal development in multiple sclerosis. Scientific Reports – Nature 11(4087), (2021).

Examples

data(MS)
str(MS)

Confidence Intervals of Effect Differences

Description

Confidence Intervals of Effect Differences

Usage

confints(X1, X2, confidence = 0.95, df.used = 0)

## S3 method for class 'confints'
plot(
  x,
  y,
  xlab = "",
  ylab = "normalised log2",
  sorted = TRUE,
  labels = FALSE,
  nonZero = FALSE,
  xlim = NULL,
  ylim = NULL,
  text.pt = 12,
  ...
)

Arguments

Value

An object of class confints, which holds the information needed to perform statistics or plot the confidence intervals is returned from confints. The plotting routine returns a ggplot structure for plotting.

See Also

ER, elastic and pls.

Examples

data(MS)
# Compare MS and non-MS patients within cluster 1
conf <- with(MS, confints(proteins[MS == "yes" & cluster == 1,],
                          proteins[MS == "no"  & cluster == 1,]))
p1 <- plot(conf)
p2 <- plot(conf, nonZero = TRUE) # Only intervals without 0.
grid.arrange(p1,p2)

# Shorter plot with labels
confShort <- conf[1:10,]
p1 <- plot(confShort, labels = TRUE)
p2 <- plot(confShort, labels = TRUE, nonZero = TRUE)
grid.arrange(p1,p2)

Elastic-net modeling of ER objects.

Description

Elastic-net modeling of ER objects.

Usage

elastic(er, ...)

## S3 method for class 'ER'
elastic(
  er,
  effect,
  alpha = 0.5,
  newdata = NULL,
  validation,
  segments = NULL,
  measure = measure,
  family = family,
  ...
)

Arguments

See Also

ER, pls and confints.

Examples

## Multiple Sclerosis data
data(MS, package = "ER")
er <- ER(proteins ~ MS * cluster, data = MS)
elasticMod <- elastic(er, 'MS', validation = "CV")
sum(elasticMod$classes == MS$MS)
plot(elasticMod)            # Model fit
plot(elasticMod$glmnet.fit) # Coefficient trajectories

# Select all proteins with non-zeros coefficients
coefs     <- coef(elasticMod,s='lambda.min',exact=TRUE)
(selected <- rownames(coefs[[1]])[unique(unlist(lapply(coefs,
                      function(x)which(as.vector(x) != 0))))][-1])


## Diabetes data
data(Diabetes, package = "ER")
er.Dia <- ER(transcriptome ~ surgery * T2D, data = Diabetes)
elasticMod <- elastic(er.Dia, 'T2D', validation = "LOO")

Partial Least Squares modelling of ER objects.

Description

The output of ER is used as input to a PLS classification with the selected effect as response. It is possible to compare two models using the er2 argument. Variable selection is available through Jackknifing (from package pls) and Shaving (from package plsVarSel).

Usage

pls(er, ...)

## S3 method for class 'ER'
pls(
  er,
  effect,
  ncomp,
  newdata = NULL,
  er2,
  validation,
  jackknife = NULL,
  shave = NULL,
  df.used = NULL,
  ...
)

Arguments

Details

If using the shave options, the segment type is given as type instead of segment.type (see examples).

See Also

ER, elastic and confints.

Examples

data(MS, package = "ER")
er <- ER(proteins ~ MS * cluster, data = MS[-1,])

# Simple PLS using interleaved cross-validation
plsMod <- pls(er, 'MS', 6, validation = "CV",
              segment.type = "interleaved", length.seg = 25)
scoreplot(plsMod, labels = "names")

# PLS with shaving of variables (mind different variable for cross-validation type)
plsModS <- pls(er, 'MS', 6, validation = "CV",
              type = "interleaved", length.seg=25, shave = TRUE)
# Error as a function of remaining variables
plot(plsModS$shave)
# Selected variables for minimum error
with(plsModS$shave, colnames(X)[variables[[min.red+1]]])

 # Time consuming due to leave-one-out cross-validation
plsModJ <- pls(er, 'MS', 5, validation = "LOO",
              jackknife = TRUE)
colSums(plsModJ$classes == as.numeric(MS$MS[-1]))
# Jackknifed coefficient P-values (sorted)
plot(sort(plsModJ$jack[,1,1]), pch = '.', ylab = 'P-value')
abline(h=c(0.01,0.05),col=2:3)

scoreplot(plsModJ)
scoreplot(plsModJ, comps=c(1,3))   # Selected components
# Use MS categories for colouring and clusters for plot characters.
scoreplot(plsModJ, col = er$symbolicDesign[['MS']],
                  pch = 20+as.numeric(er$symbolicDesign[['cluster']]))
loadingplot(plsModJ, scatter=TRUE) # scatter=TRUE for scatter plot

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

gridExtra

grid.arrange

pls

R2, explvar, loadingplot, loadings, mvrValstats, scoreplot, scores