RNAseqII.at

RNA-seq Part 2

In the first part, we learned how to analyze RNA-seq data and determine which genes in the sample dataset were differentially expressed or turned on/off from instar 1 to instar 2. We used edgeR alongside a couple other R packages to further analyze the sample data set.

RNA-seq part 2 is focusing on visualizing data and narrowing down on genes that are different as well as analysis of the patterns in the data. This part is focusing on a sample data set from the R bioinformatics cookbook using the model plant species Arabidopsis thaliana. This is focusing on how its gene expression varies in different parts of the plant. Then we can use RNA-seq to see if gene expression varies by ecotype.

Install/Load packages

#if (!require("BiocManager", quietly = TRUE))
    #install.packages("BiocManager")

#BiocManager::install("ComplexHeatmap")

These packages will allow for data to be organized and visualized with useful color scheme. Installed and then replaced the #

#install.packages("viridisLite")
#install.packages("stringr")
#install.packages("RColorBrewer")
#install.packages("circlize")

Loading all the packages including rbioinfocookbook

library(ComplexHeatmap)

Loading required package: grid

========================================
ComplexHeatmap version 2.18.0
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://jokergoo.github.io/ComplexHeatmap-reference

If you use it in published research, please cite either one:
- Gu, Z. Complex Heatmap Visualization. iMeta 2022.
- Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional 
    genomic data. Bioinformatics 2016.


The new InteractiveComplexHeatmap package can directly export static 
complex heatmaps into an interactive Shiny app with zero effort. Have a try!

This message can be suppressed by:
  suppressPackageStartupMessages(library(ComplexHeatmap))
========================================

library(viridisLite)
library(stringr)
library(RColorBrewer)
library(circlize)

========================================
circlize version 0.4.16
CRAN page: https://cran.r-project.org/package=circlize
Github page: https://github.com/jokergoo/circlize
Documentation: https://jokergoo.github.io/circlize_book/book/

If you use it in published research, please cite:
Gu, Z. circlize implements and enhances circular visualization
  in R. Bioinformatics 2014.

This message can be suppressed by:
  suppressPackageStartupMessages(library(circlize))
========================================

library(rbioinfcookbook)

Loading the dataset

What is happening: loading dataset from rbioinfocookbook, name of the dataset is at_tf_gex, pulling a few columns of interest and scale the data to make it more visually useful. Then using a function in the stringer package to split the data by ecotype.

rbioinfocookbook, is spelled wrong and I think is the only reason my R is not throwing an error as if spelled correctly it is masked in global environment by count_dataframe

mat <- log(as.matrix(at_tf_gex[ , 5:55]))
ecotype <- stringr::str_split(colnames(mat), ",", simplify = TRUE)[,1]
part <- stringr::str_split(colnames(mat), ",", simplify = TRUE)[,2]

Color Palettes

use circlize and viridisLite packages to create color palettes for different types of data in the heatmap, makes it so we can create a unique color palette for each ecotype and plant part.

data_col_func <- circlize::colorRamp2(seq(0, max(mat), length.out = 6), viridisLite::magma(6))

ecotype_colors <- c(RColorBrewer::brewer.pal(12, "Set3"), RColorBrewer::brewer.pal(5, "Set1"))
names(ecotype_colors) <- unique(ecotype)

part_colors <- RColorBrewer::brewer.pal(3, "Accent")
names(part_colors) <- unique(part)

*doing command+shift only runs one line of the code and does not run the whole chunk?

Annotations and Formatting

Adding annotations to heatmap, ecotype and plant part information are included in the top_annot. use annotation_name_side to set the annotation to the left of the color that they represent.

• side_annot used to add annot to the rows of the heatmap. adding the length information for the samples in this scenario.

  • formatting the plot

    • anno_points, specifies locus of the points to be plotted

    • pch, shape of the points

    • size, size of pts

    • axis_param, locus of the ticks on the x-axis

top_annot <- HeatmapAnnotation("Ecotype" = ecotype, "Plant Part" = part, col = list("Ecotype" = ecotype_colors, "Plant Part" = part_colors), annotation_name_side = "left")

side_annot <- rowAnnotation(length = anno_points(at_tf_gex$Length, pch = 16, size = unit(1, "mm"), axis_param = list(at = seq(1, max(at_tf_gex$Length), length.out = 4)),))