tppccrNormalizeToReference: Normalize fold changes of TPP-CCR experiment to a reference...

In TPP: Analyze thermal proteome profiling (TPP) experiments

Description

Normalize fold changes of TPP-CCR experiment to a reference column (usually that with the lowest concentration) to ensure that the transformation by tppccrTransform yields values between 0 and 1.

Usage

tppccrNormalizeToReference(data, refCol = NULL)

Arguments

data	expressionSet object containing the data to be normalized
refCol	column number to use as a reference. Will contain only 1s after the normalization.

Value

List of expressionSet objects storing the normalized fold changes, as well as row and column metadata. In each expressionSet S, the fold changes can be accessed by Biobase::exprs(S). Protein expNames can be accessed by featureNames(S). Isobaric labels and the corresponding concentrations are returned by S$label and S$concentration.

Examples

data(hdacCCR_smallExample)
tppccrData <- tppccrImport(configTable=hdacCCR_config, data = hdacCCR_data)
tppccrNorm <- tppccrNormalize(data=tppccrData)
# Normalize to lowest concentration (in the first column):
tppccrNormToRef <- tppccrNormalizeToReference(data=tppccrNorm, refCol=1)
# Obtain results per replicate:
refTransf_replicate1 <- tppccrNormToRef$Panobinostat_1
head(Biobase::exprs(refTransf_replicate1))
# Perform transformation:
tppccrTransformed <- tppccrTransform(data=tppccrNormToRef)
# Obtain transformed measurements per replicate:
transf_replicate1 <- tppccrTransformed$Panobinostat_1
transf_replicate2 <- tppccrTransformed$Panobinostat_2
# Inspect transformed data in replicate 1:
effects_replicate1 <- Biobase::featureData(transf_replicate1)$compound_effect
newData_repl1 <- data.frame(Biobase::exprs(transf_replicate1), 
                              Type=effects_replicate1)[!is.na(effects_replicate1),]
                              

Example output

Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: ‘BiocGenerics’

The following objects are masked from ‘package:parallel’:

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from ‘package:stats’:

    IQR, mad, sd, var, xtabs

The following objects are masked from ‘package:base’:

    anyDuplicated, append, as.data.frame, basename, cbind, colnames,
    dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
    grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: dplyr

Attaching package: ‘dplyr’

The following object is masked from ‘package:Biobase’:

    combine

The following objects are masked from ‘package:BiocGenerics’:

    combine, intersect, setdiff, union

The following objects are masked from ‘package:stats’:

    filter, lag

The following objects are masked from ‘package:base’:

    intersect, setdiff, setequal, union

Loading required package: magrittr
Loading required package: tidyr

Attaching package: ‘tidyr’

The following object is masked from ‘package:magrittr’:

    extract

Importing data...

The following valid label columns were detected:
126, 127L, 127H, 128L, 128H, 129L, 129H, 130L, 130H, 131L.

Importing CCR dataset: Panobinostat_1
Removing duplicate identifiers using quality column 'qupm'...
507 out of 507 rows kept for further analysis.
  -> Panobinostat_1 contains 507 proteins.
  -> 494 out of 507 proteins (97.44%) suitable for curve fit (criterion: > 2 valid fold changes per protein).

Importing CCR dataset: Panobinostat_2
Removing duplicate identifiers using quality column 'qupm'...
507 out of 507 rows kept for further analysis.
  -> Panobinostat_2 contains 507 proteins.
  -> 494 out of 507 proteins (97.44%) suitable for curve fit (criterion: > 2 valid fold changes per protein).


Filtering CCR dataset: Panobinostat_1
Removed proteins with zero values in column(s) 'qssm':
	494 out of 507 proteins remaining.
Filtering CCR dataset: Panobinostat_2
Removed proteins with zero values in column(s) 'qssm':
	494 out of 507 proteins remaining.
Normalizing dataset: Panobinostat_1
Normalizing dataset: Panobinostat_2
Normalization complete.

Normalizing dataset: Panobinostat_1 to reference column 1
Normalizing dataset: Panobinostat_2 to reference column 1
       rel_fc_131L rel_fc_130H rel_fc_130L rel_fc_129H rel_fc_129L rel_fc_128H
AAMP             1   1.0805878   0.9300190   1.0249224   1.0592142   1.1483423
AARS             1   0.8079986   0.7314787   0.8035509   0.6017420   0.7222601
ACAA1            1   0.9674206   0.8828929   0.8965850   0.9630262   0.8590682
ACOT13           1   0.9945811   1.0088442   1.0573937   1.1031335   1.0224154
ACOX1            1   0.9486613   0.8112557   0.8421343   0.9136691   0.7722193
ADA              1   1.0055789   1.0098722   0.9875025   1.0228419   0.9942368
       rel_fc_128L rel_fc_127H rel_fc_127L rel_fc_126
AAMP     1.0271229   1.0074459   0.9311145  1.0541931
AARS     0.7318092   0.5439703   0.6971554  0.6491910
ACAA1    0.9720662   0.9949784   0.8925081  0.9296717
ACOT13   0.9631632   1.0376417   1.1104348  1.1111943
ACOX1    0.9051332   0.9052566   0.8342173  0.8567592
ADA      0.9469198   1.0021769   0.9807971  0.9679849
Transforming dataset: Panobinostat_1
Transforming dataset: Panobinostat_2
Transformation complete.

TPP documentation built on Nov. 8, 2020, 5:55 p.m.