Package 'lfproQC'

Best combination of normalization and imputation method

Description

This function will provide the best combinations of normalization and imputation methods for the user given dataset based on the intragroup variation evaluation parameters called PCV, PEV, PMAD and NRMSE.

Usage

best_combination(data_input, groups, data_type, aggr_method)

Arguments

data_input	Label-free proteomics expression data as a dataframe
groups	Group information about the input data
data_type	A character string specifying the type of data being used. Use "Peptide" if your dataset contains peptide information, where the first column represents peptide IDs and the second column represents protein IDs. Use "Protein" if your dataset consists of protein data, where the first column represents protein IDs. The function will handle the data accordingly based on this parameter.
aggr_method	A character string specifying the method for aggregating peptide data to corresponding protein values. Use "sum" to aggregate by the sum of peptide intensities, "mean" to aggregate by the mean of peptide intensities, or "median" to aggregate by the median of peptide intensities. This parameter is only applicable when "data_type" is set to "Peptide".

Details

Label-free LC-MS proteomics expression data is often affected by heterogeneity and missing values. Normalization and missing value imputation are the commonly used techniques to solve these issues and make the dataset suitable for further downstream analysis. This function provides the best combination of normalization and imputation methods for the dataset, choosing from the three normalization methods (vsn, loess, and rlr) and three imputation methods (knn, lls, svd). The intragroup variation evaluation measures named pooled co-efficient of variance (PCV), pooled estimate of variance (PEV) and pooled median absolute deviation (PMAD) are used for selecting the best combination of normalization and imputation method for the given dataset. It will return the best combinations based on each evaluation parameters of PCV, PEV, and PMAD.

Along with this, the user can get all three normalized datasets, nine combinations of normalized and missing values imputed datasets, and the PCV, PEV, and PMAD result values. The user can also obtain the Normalized Root Mean Square Error (NRMSE) values, calculated by comparing the normalized and imputed dataset with the original dataset across all nine combinations. These NRMSE values provide insight into the accuracy of the imputation and normalization processes.

Value

This function gives the list which consist of following results.

'Best Combinations' The best combinations based on each PCV, PEV and PMAD for the given dataset.

'PCV Result' Values of groupwise PCV, overall PCV, PCV mean, PCV median and PCV standard deviation for all combinations.

'PEV Result' Values of groupwise PEV, overall PEV, PEV mean, PEV median and PEV standard deviation for all combinations.

'PMAD Result' Values of groupwise PMAD, overall PMAD, PMAD mean, PMAD median and PMAD standard deviation for all combinations.

'NRMSE Result' NRMSE values calculated for the normalized and imputed dataset to the original dataset.

'vsn_data' The 'vsn' normalized dataset

'loess_data' The 'loess' normalized dataset

'rlr_data' The 'rlr' normalized dataset

'vsn_knn_data' The dataset normalized by 'vsn' method and missing values imputed by 'knn' method.

'vsn_lls_data' The dataset normalized by 'vsn' method and missing values imputed by 'lls' method.

'vsn_svd_data' The dataset normalized by 'vsn' method and missing values imputed by 'svd' method.

'loess_knn_data' The dataset normalized by 'loess' method and missing values imputed by 'knn' method.

'loess_lls_data' The dataset normalized by 'loess' method and missing values imputed by 'lls' method.

'loess_svd_data' The dataset normalized by 'loess' method and missing values imputed by 'svd' method.

'rlr_knn_data' The dataset normalized by 'rlr' method and missing values imputed by 'knn' method.

'rlr_lls_data' The dataset normalized by 'rlr' method and missing values imputed by 'lls' method.

'rlr_svd_data' The dataset normalized by 'rlr' method and missing values imputed by 'svd' method.

Author(s)

Dr Sudhir Srivastava ("[email protected]")

Kabilan S ("[email protected]")

Examples

result <- best_combination(yeast_data, yeast_groups, data_type = "Protein")
result$`Best combinations`
result$`PCV Result`
result$`PMAD Result`
result$`rlr_knn_data`

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Creating Boxplot for a dataset

Description

The box and whiskers plot displays the distribution of a continuous variable. It visualises five summary statistics (the median, two hinges and two whiskers), and all "outlying" points individually. The 'ggplot2' package is used here for creating the boxplot.

Usage

Boxplot_data(data)

Arguments

data	Proteomics expression dataset (original or normalized dataset)

Details

This can also be used for comparing the original dataset with the normalized dataset.

Value

Interactive box and whiskers plot

See Also

'geom_boxplot()'

Examples

Boxplot_data(yeast_data)
Boxplot_data(rlr_knn_yeast_data)

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Creating Correlation matrix plot for a dataset

Description

A graphical display of a correlation matrix.

Usage

Corrplot_data(data)

Arguments

data	Proteomics expression dataset (original or normalized dataset) along with the protein information

Details

This can also be used for comparing the original dataset with the normalized dataset.

Value

Interactive corrleation matrix plot

Examples

Corrplot_data(yeast_data)
Corrplot_data(rlr_knn_yeast_data)

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Creating Density plot for a dataset

Description

Computes and draws kernel density estimate, which is a smoothed version of the histogram. This is a useful alternative to the histogram for continuous data that comes from an underlying smooth distribution. The 'ggplot2' package is used here for creating the boxplot.

Usage

Densityplot_data(data)

Arguments

data	Proteomics expression dataset (original or normalized dataset) along with the protein information

Details

This can also be used for comparing the original dataset with the normalized dataset.

Value

Interactive column-wise density plot

See Also

'geom_density()'

Examples

Densityplot_data(yeast_data)
Densityplot_data(rlr_knn_yeast_data)

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Find out the Up and Down regulated proteins from MA plot

Description

MA plot is used for visualizing the differentially expressed proteins by plotting the log mean intensity data in x axis and log fold change values in y axis.

This function can be used for visualizing the up regulated, down regulated, and non-significant proteins along with their information.

Usage

MAplot_DE_fn(top_table, x1 = NULL, x2 = NULL, p = NULL)

Arguments

top_table	Top table information
x1	Cut-off limit for down-regulated proteins
x2	Cut-off limit for up-regulated proteins
p	Cut-off limit for p-values

Value

'Result' Top table along with up, down, significant and non-significant protein information.

'MA plot' Interacive MA plot with the details of up and down regulated proteins

'Up-regulated' Up-regulated protein information

'Down-regulated' Down-regulated protein information

'Non-significant' Non-significant protein information

Examples

result <- MAplot_DE_fn(yeast_top_table, -1, 1, 0.05)
result$`MA Plot`
result$`Result`
result$`Up-regulated`
result$`Down-regulated`
result$`Non-significant`

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Creating MDS plot for a dataset

Description

Multi-dimensional scaling (MDS) plots showing a 2-dimensional projection of distances between the dataset samples.

Usage

MDSplot_data(data)

Arguments

data	Normalized and imputed Proteomics expression dataset along with the protein information

Value

MDS plot

See Also

'mdsPlot'

Examples

MDSplot_data(rlr_knn_yeast_data)

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Creating QQ-Plot for a dataset

Description

A Q–Q plot (quantile-quantile plot) is a plot of the quantiles of two distributions against each other, or a plot based on estimates of the quantiles. The normality of the data can be understand by this plot.

Usage

QQplot_data(data)

Arguments

data	Proteomics expression dataset (original or normalized dataset)

Details

This can be used for comparing the original dataset with the normalized dataset.

Value

Interactive column-wise QQ-plot

Examples

qqplot <- QQplot_data(rlr_knn_yeast_data)

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Normalized and imputed complete yeast lysate - UPS1 benchmark dataset

Description

This is the groupwise normalized and missing values imputed dataset of the complete yeast lysate - UPS1 benchmark dataset. Normalization has been done by RLR normalization method and missing values imputation has been done by KNN imputation method.

Usage

rlr_knn_yeast_data

Format

A data frame with 835 rows and 7 variables:

Majority protein IDs

Protein ID information

A1

1st sample group, 1st technical replicate

A2

1st sample group, 2nd technical replicate

A3

1st sample group, 3rd technical replicate

B1

2nd sample group, 1st technical replicate

B2

2nd sample group, 2nd technical replicate

B3

2nd sample group, 3rd technical replicate

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Creating the top table

Description

Top table can be used for identifying the pairwise differential abundance analysis of proteins in the dataset.

Usage

top_table_fn(data, groups, ch_gr1, ch_gr2)

Arguments

data	Normalized and missing values imputed expression dataset containing protein information
groups	Group information about the input data
ch_gr1	Group number of the dataset for pairwise comparison with the another group
ch_gr2	Group number of the dataset to be compared with the chosen group

Value

Top table consists of following values

'logFC' - Log fold change values,

'AveExpr' - Average intensity values,

't'- t-statistic values,

'P.Value' - P-values,

'adj.P.Val' - Adjusted P-values,

'B'- B-statistic values

See Also

'limma::topTable'

Examples

top_table <- top_table_fn(rlr_knn_yeast_data, yeast_groups, 2, 1)
top_table

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Find out the Up and Down regulated proteins from volcano plot

Description

Volcano plot is used for visualizing the differentially expressed proteins by plotting the log fold change values in x axis and (-log10 p-values) in y axis.

This function can be used for visualizing the up regulated, down regulated, and non-significant proteins along with their information.

Usage

volcanoplot_DE_fn(top_table, x1 = NULL, x2 = NULL, p = NULL)

Arguments

top_table	Top table information
x1	Cut-off limit for down-regulated proteins
x2	Cut-off limit for up-regulated proteins
p	Cut-off limit for p-values

Value

'Result' Top table along with up, down, significant and non-significant protein information.

'Volcano plot' Interactive MA plot with the details of up and down regulated proteins

'Up-regulated' Up-regulated protein information

'Down-regulated' Down-regulated protein information

'Non-significant' Non-significant protein information

Examples

result <- volcanoplot_DE_fn(yeast_top_table, -1, 1, 0.05)
result$`Volcano Plot`
result$`Result`
result$`Up-regulated`
result$`Down-regulated`
result$`Non-significant`

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Yeast lysate - UPS1 benchmark dataset

Description

This dataset was given by Ramus et al., (2016). It is based on a highly complex sample (yeast lysate) spiked with different spiked amounts of the UPS1 standard mixture of 48 recombinant proteins. The original dataset contains 2644 rows of proteins and 2 groups of samples with three replicates each. From the original dataset, only the pairwise comparison of 50vs0.5 has taken here. This dataset contains 874 rows of proteins which includes 48 spiked-in ups proteins in the yeast.

Usage

yeast_data

Format

A data frame with 874 rows and 7 variables:

Majority protein IDs

Protein ID information

A1

1st condition, 1st technical replicate

A2

1st condition, 2nd technical replicate

A3

1st condition, 3rd technical replicate

B1

2nd condition, 1st technical replicate

B2

2nd condition, 2nd technical replicate

B3

2nd condition, 3rd technical replicate

Details

This standard proteomic dataset is suitable for benchmarking and comparing software for label-free quantification. And can also be applied to the evaluation of post-processing steps such as normalization, imputation of missing values, and statistical methods.

Source

doi:10.1016/j.dib.2015.11.063

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Group information of Yeast lysate - UPS1 benchmark dataset

Description

The standard benchmark yeast lysate - UPS1 dataset contains the variables of 6 columns, 2 conditions and 3 technical replicates.

Usage

yeast_groups

Format

A data frame with 6 rows and 2 variables:

Samples

Technical replicate details - Two kinds of technical replicates (A1, A2, A3) and (B1, B2, B3)

Groups

Condition details - Two conditions (GrA) and (GrB)

Details

The above information is required for further analysis of the dataset.

Source

doi:10.1016/j.dib.2015.11.063

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Top table for Yeast Lysate - UPS1 dataset

Description

Top table can be used for identifying the differential abundance analysis of proteins in the dataset.

Usage

yeast_top_table

Format

A data frame with 835 rows and 6 variables along with 'Fasta headers':

logFC

Log fold change values

AveExpr

Average intensity values

t

t-statistic values

P.Value

P-values

adj.P.Val

Adjusted P-values

B

B-statistic values

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