Contents

Compiled date: 2021-10-26

Last edited: 2021-04-16

License: GPL-3

1 Installation

Run the following code to install the Bioconductor version of package.

# install.packages("BiocManager")
BiocManager::install("POMA")

2 Load POMA

library(POMA)

3 Automatic EDA Report

The following function will return an Exploratory Data Analysis (EDA) PDF report. The input object must be an MSnSet object.

data("st000336")
PomaEDA(st000336)

Generated EDA PDF report starts here.

4 Know your data

4.1 Summary Table

Samples Features Covariates
57 31 1
Counts_Zero Percent_Zero
0 0 %
Counts_NA Percent_NA
61 3.45 %

4.2 Samples by Group

5 Normalization Plots

6 Group Distribution Plots

7 Outlier Detection

7 possible outliers detected in your data. These outliers are ‘DMD119.2.U02’, ‘DMD084.11.U02’, ‘DMD087.12.U02’, ‘DMD023.10.U02’, ‘DMD046.11.U02’, ‘DMD133.9.U02’, ‘DMD135.10.U02’.

sample group distance_to_centroid limit_distance
DMD119.2.U02 Controls 2.742576 2.290945
DMD084.11.U02 DMD 4.522825 4.040548
DMD087.12.U02 DMD 4.252170 4.040548
DMD023.10.U02 DMD 5.653399 4.040548
DMD046.11.U02 DMD 5.580959 4.040548
DMD133.9.U02 DMD 5.349670 4.040548
DMD135.10.U02 DMD 4.055233 4.040548

8 High Correlated Features (r > 0.97)

There are 0 high correlated feature pairs in your data.

9 Heatmap and Clustering

10 Principal Component Analysis