In OceaneCsn/DIANE: DIANE

r <- params$r
input <- params$input

library(knitr)

Dashboard for the Inference and Analysis of Networks from Expression data

---

This report was automatically generated by DIANE to improve research reproducibility.

It contains the main settings and results for the chosen tab of the application.

Your data

The organism you're studying :

r$organism

Number of genes :

nrow(r$raw_counts)

Experimental conditions :

unique(r$conditions)

Design:

r$design

Settings

Prior removal of differentially expressed genes :

input$prior_removal

Normalization method :

input$norm_method

Number of genes before low counts removal :

nrow(r$raw_counts)

Number of genes after low counts removal :

nrow(r$normalized_counts)

The criteria for gene filtering was a sum of expression across all samples above:

input$low_counts_filter

Distribution visualization

For each condition, we can visualize the distributions of gene counts before normalization and low can removal, and after.

pre_process <- DIANE::draw_distributions(r$raw_counts, boxplot = FALSE) + ggplot2::ggtitle("Before")
post_process <- DIANE::draw_distributions(r$normalized_counts, boxplot = FALSE)+ ggplot2::ggtitle("After")

gridExtra::grid.arrange(pre_process, post_process, ncol = 2)

The distribution modes should be aligned, and the initial peak around low values should have disappeared.

Summary

Raw TCC summary including the chosen pipeline, library sizes and normalization factors :

if(input$norm_method != 'none') print(r$tcc) else print("No normalization factors were applied.")

Exploratory analysis

PCA

DIANE::draw_PCA(r$normalized_counts)

OceaneCsn/DIANE documentation built on Jan. 10, 2024, 6:43 p.m.