LFQDataPlotter: LFQDataPlotter – Create various visualization of the LFQdata

In wolski/prolfqua: Proteomics Label Free Quantification

LFQDataPlotter —- Create various visualization of the LFQdata

Description

LFQDataPlotter —- Create various visualization of the LFQdata

LFQDataPlotter —- Create various visualization of the LFQdata

Public fields

lfq

LFQData object

prefix

prefix to figure names when writing, e.g. protein_

file_paths_pdf

with paths to figures

file_paths_html

with paths to figures

Methods

Public methods

• LFQDataPlotter$new()

• LFQDataPlotter$raster()

• LFQDataPlotter$heatmap()

• LFQDataPlotter$heatmap_cor()

• LFQDataPlotter$pca()

• LFQDataPlotter$pca_plotly()

• LFQDataPlotter$boxplots()

• LFQDataPlotter$missigness_histogram()

• LFQDataPlotter$NA_heatmap()

• LFQDataPlotter$intensity_distribution_density()

• LFQDataPlotter$intensity_distribution_violin()

• LFQDataPlotter$pairs_smooth()

• LFQDataPlotter$sample_correlation()

• LFQDataPlotter$upset_missing()

• LFQDataPlotter$write_boxplots()

• LFQDataPlotter$write_pltly()

• LFQDataPlotter$write_pdf()

• LFQDataPlotter$write()

• LFQDataPlotter$clone()

---

Method new()

create LFQDataPlotter

Usage

LFQDataPlotter$new(lfqdata, prefix = "ms_")

Arguments

lfqdata

LFQData

prefix

will be prepended to outputs written

---

Method raster()

plot intensities in raster

Usage

LFQDataPlotter$raster(
  arrange = c("mean", "var"),
  not_na = FALSE,
  rownames = FALSE
)

Arguments

arrange

arrange by either mean or var

not_na

TRUE arrange by number of NA's, FALSE by arrange by intensity

rownames

show rownames (default FALSE - do not show.)

Returns

ggplot

---

Method heatmap()

heatmap of intensities - columns are samples, rows are proteins or peptides.

The abundances of each protein (row) are z-scored. Afterward, the mean abundance for each protein is zero, and the standard variation is one. z-scoring allows to compare (cluster) the proteins according to the difference in the expression in the samples. Without the z-scoring, the proteins would group according to their abundance, e.g., high abundant proteins would be one cluster.

Usage

LFQDataPlotter$heatmap(na_fraction = 0.3, rownames = FALSE)

Arguments

na_fraction

max fraction of NA's per row

rownames

show rownames (default FALSE - do not show.)

Returns

pheatmap

---

Method heatmap_cor()

heatmap of sample correlations.

The Spearman correlation among all samples is computed. Then the euclidean distance is used to compute the distances.

Usage

LFQDataPlotter$heatmap_cor()

Returns

pheatmap

---

Method pca()

PCA plot

A PCA is applied and the first and second principal component are shown.

Usage

LFQDataPlotter$pca(PC = c(1, 2), add_txt = TRUE)

Arguments

PC

default c(1,2) - first and second principal component

add_txt

show sample names

Returns

ggplot

---

Method pca_plotly()

pca plot

Usage

LFQDataPlotter$pca_plotly(PC = c(1, 2), add_txt = FALSE)

Arguments

PC

default c(1,2) - first and second principal component

add_txt

show sample names

Returns

plotly

---

Method boxplots()

boxplots for all proteins

Usage

LFQDataPlotter$boxplots(facet = TRUE)

Arguments

facet

enable facet wrap if hierarchy_depth less then hierarchy lenght.

Returns

tibble with column boxplots containing ggplot objects

---

Method missigness_histogram()

histogram of intensities given number of missing in conditions

Usage

LFQDataPlotter$missigness_histogram()

Returns

ggplot

---

Method NA_heatmap()

heatmap of features with missing values

Usage

LFQDataPlotter$NA_heatmap()

Returns

ggplot

---

Method intensity_distribution_density()

density distribution of intensities

Usage

LFQDataPlotter$intensity_distribution_density(legend = TRUE)

Arguments

legend

show legend TRUE, FALSE do not show.

Returns

ggplot

---

Method intensity_distribution_violin()

Violinplot showing distribution of intensities in all samples

Usage

LFQDataPlotter$intensity_distribution_violin()

Returns

ggplot

---

Method pairs_smooth()

pairsplot of intensities

Usage

LFQDataPlotter$pairs_smooth(max = 10)

Arguments

max

maximal number of samples to show

Returns

NULL

---

Method sample_correlation()

plot of sample correlations

Usage

LFQDataPlotter$sample_correlation()

Returns

NULL

---

Method upset_missing()

upset plot based on presence absence information

Usage

LFQDataPlotter$upset_missing()

Returns

plot

---

Method write_boxplots()

write boxplots to file

Usage

LFQDataPlotter$write_boxplots(path_qc, filename = NULL, width = 6, height = 6)

Arguments

path_qc

path to write to

filename

file to write into

width

fig width

height

fig height

---

Method write_pltly()

write pltly figures to path_qc

Usage

LFQDataPlotter$write_pltly(fig, path_qc, fig_name)

Arguments

fig

pltly figure

path_qc

path to write to

fig_name

file name (without extension)

Returns

path the file was written to.

---

Method write_pdf()

write figure to pdf

Usage

LFQDataPlotter$write_pdf(fig, path_qc, fig_name, width = 7, height = 7)

Arguments

fig

ggplot or pheatmap

path_qc

path to write to

fig_name

name of figure (no extension)

width

figure width

height

figure height

Returns

path the file was written to

---

Method write()

write heatmaps and pca plots to files

Usage

LFQDataPlotter$write(path_qc)

Arguments

path_qc

path to write to

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LFQDataPlotter$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

See Also

plot_heatmap_cor

plot_pca

Other LFQData: LFQData, LFQDataAggregator, LFQDataStats, LFQDataSummariser, LFQDataToSummarizedExperiment(), LFQDataWriter

Examples

istar <- sim_lfq_data_peptide_config()

lfqdata <- LFQData$new(
 istar$data,
 istar$config)
#LFQDataPlotter$debug("boxplots")
LFQDataPlotter$debug("pairs_smooth")
lfqplotter <- lfqdata$get_Plotter()

stopifnot(class(lfqplotter$heatmap()) == "pheatmap")
stopifnot(class(lfqplotter$heatmap_cor()) == "pheatmap")
stopifnot("ggplot" %in% class(lfqplotter$pca()))
stopifnot("plotly" %in%  class(lfqplotter$pca_plotly()))
tmp <- lfqplotter$boxplots()
stopifnot("ggplot" %in%  class(tmp$boxplot[[1]]))
stopifnot("ggplot" %in% class(lfqplotter$missigness_histogram()))

stopifnot(class(lfqplotter$NA_heatmap()) == "pheatmap")
class(lfqplotter$intensity_distribution_density())
class(lfqplotter$intensity_distribution_violin())
## Not run: 
  stopifnot(is.null(lfqplotter$pairs_smooth()))

## End(Not run)
stopifnot(class(lfqplotter$sample_correlation()) == "list")
stopifnot(class(lfqplotter$raster()) == "pheatmap")
stopifnot("upset" == class(lfqplotter$upset_missing()))
stopifnot(class(prolfqua::plot_sample_correlation(istar$data, istar$config)) == "list")

wolski/prolfqua documentation built on Oct. 31, 2024, 9:22 a.m.