An Alternative to Pairs for -omics Quality Control: Introducing R

Introduction:

Introduction:
When it comes to quality control in -omics data, the commonly used “pairs” plot in R has a couple of problems. Firstly, it is not space-efficient as it only utilizes half the space for datapoints. Secondly, the scatterplot in the pairs plot lacks informative value. It becomes difficult to determine the spread of the data or any normalization issues, especially in proteomics data where the high dynamic range can obscure lower abundance points.

However, there is a solution to these problems – the panel of MA plots (Minus-Average). The MA plot displays fold-change versus average intensity for a pair of samples. This plot allows for the visual identification of differences between sample groups and normalization problems. By comparing samples between groups instead of plotting each against each, we can save space.

Furthermore, it is worth mentioning that using tidy data format, as discussed in a previous post (link provided), can greatly enhance the value of the data tables. In this method, we pivot the data to generate a concise panel of MA plots.

To implement this solution, we will utilize the “tidyverse,” “GGally,” and “DEFormats” libraries. First, we will set up simulated data resembling a gene expression study with 8 samples, divided equally between a control group and a treated group. One of the samples in the treated group will have a three-fold increase to illustrate the identification of normalization problems with MA plots.

The traditional pairs plot, generated using the “ggpairs” function from the GGally package, provides some insights into the data. However, we believe it does not efficiently convey information to the user.

To create the MA plot panel, we pivot the count data for each gene, associating control and treated data for each sample. Facet_grid is then used to split the samples in the plot. The resulting plot clearly highlights the change in abundance for the specific sample, demonstrating the effectiveness of the MA plot panel for quality control in -omics data.

Although this approach may be unfamiliar to some, it is well worth the effort to introduce it to colleagues due to its advantages in data analysis.

Full Article: An Alternative to Pairs for -omics Quality Control: Introducing R

Revolutionizing Quality Control in -Omics Data with MA Plot Panels

Introduction

In the field of -omics data analysis, the commonly used “pairs” plot in R for quality control poses a couple of problems. First, it is not space-efficient as it only utilizes half the space for data points. Second, the scatterplot in the pairs plot does not provide much informative insight into the spread of the data or any normalization issues, especially in proteomics data where the high dynamic range can mask lower abundance points.

To address these issues, a panel of MA plots (Minus-Average) can be used. The MA plot displays the fold-change versus the average intensity for a pair of samples, allowing for easy comparison between sample groups and visualization of normalization problems. Instead of plotting each sample against each other, we will only compare samples between groups to save space.

This approach complements a previous post that advocates for biologists to adopt the practice of organizing data into tidy format. By leveraging data pivoting techniques, a concise panel of MA plots can be generated.

Set up the data

To illustrate the application of MA plot panels, we will use simulated data that resembles a gene expression study. This dataset consists of 8 samples, half of which are treated and half serve as controls. Among the samples, 7 are roughly similar, while Sample 4 exhibits a 3-fold increase compared to the rest. This discrepancy will help demonstrate how MA plots can identify normalization problems.

suppressPackageStartupMessages(library(tidyverse))
library(GGally)
library(DEFormats)

counts <- simulateRnaSeqData(n = 5000, m = 8)
counts[, 4] <- counts[, 4] * 3

targets <- data.frame(sample = colnames(counts), group = c(rep("control", 4), rep("treated", 4)))

The `ggpairs` function from the `GGally` package is commonly used for pairs plots.

ggpairs(data.frame(counts))

Although the pairs plot provides some information, such as correlations and outliers, it does not efficiently convey the necessary insights.

MA plot panel

To create the MA plot panel, we need to pivot the count data individually for control and treated samples. This unconventional approach involves associating control and treated data for each gene and sample. After this transformation is applied, we can calculate the fold-change and intensity.

control_samples <- targets$sample[targets$group == "control"]
treated_samples <- targets$sample[targets$group == "treated"]

data.frame(counts) %>%
  rownames_to_column("gene") %>%
  pivot_longer(all_of(control_samples), names_to = "control_sample", values_to = "control_count") %>%
  pivot_longer(all_of(treated_samples), names_to = "treated_sample", values_to = "treated_count") %>%
  mutate(FC = treated_count / control_count) %>%
  mutate(Intensity = (treated_count + control_count) / 2)

All that remains is to plot the MA plot panel. By using `facet_grid`, we can divide the samples into separate facets.

data.frame(counts) %>%
  rownames_to_column("gene") %>%
  pivot_longer(all_of(control_samples), names_to = "control_sample", values_to = "control_count") %>%
  pivot_longer(all_of(treated_samples), names_to = "treated_sample", values_to = "treated_count") %>%
  mutate(FC = treated_count / control_count) %>%
  mutate(Intensity = (treated_count + control_count) / 2) %>%
  ggplot(aes(x = Intensity, y = FC)) +
  geom_point(alpha = 0.5, na.rm = TRUE) +
  scale_x_continuous(trans = "log10") +
  scale_y_continuous(trans = "log2", breaks = 2^seq(-4, 4, 2)) +
  geom_hline(yintercept = 1) +
  labs(x = "Intensity", y = "Fold Change, treated vs control") +
  facet_grid(rows = vars(treated_sample), cols = vars(control_sample))

With the MA plot panel, the change in abundance for Sample 4 is clearly visible. Although this approach may necessitate some explanation to colleagues who are unfamiliar with it, the effort is worthwhile for the valuable insights it offers.

Summary: An Alternative to Pairs for -omics Quality Control: Introducing R

The commonly used "pairs" plot in R for quality control in -omics data has a couple of problems. First, it only uses half the space for data points, making it less space-efficient. Second, the scatterplot in the pairs plot is not very informative, especially for proteomics data with a high dynamic range. To address these issues, a panel of MA plots (Minus-Average) is proposed. The MA plot shows fold-change versus average intensity for a pair of samples, allowing for easy visualization of differences between sample groups and normalization problems. By comparing samples between groups instead of plotting each against each, space is saved. The implementation involves pivoting the data and using the GGally, tidyverse, and DEFormats packages. A simulated gene expression dataset is used as an example, with 8 samples divided into control and treated groups. The resulting MA plot panel clearly highlights the fold-change difference in sample 4, making it a more efficient and informative visualization method for -omics data quality control.

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