Description
Visualization Tool for the Cancer Genome Atlas Program (TCGA).
Description
Differential analysis of tumor tissue immune cell type abundance based on RNA-seq gene-level expression from The Cancer Genome Atlas (TCGA; <https://pancanatlas.xenahubs.net>) database.
README.md
tcgaViz
Differential analysis of tumor tissue immune cell type abundance based on RNASeq gene-level expression from The Cancer Genome Atlas (TCGA) database.
Installation
Required: - Softwares : R (≥ 3.3.0); RStudio (https://posit.co/downloads/) - R libraries : see the DESCRIPTION file.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("ecamenen/tcgaViz")
Launch the Shiny server
- Download the tcga dataset here.
- Copy it in the “extdata” folder of the library (get the path of your library with the following R command:
system.file("extdata", package = "tcgaViz"). - Open RStudio and run:
tcgaViz::run_app()
Docker
Pull
docker pull eucee/tcga-viz
Run in command-line
docker run --rm -p 127.0.0.1:3838:3838 eucee/tcga-viz
Example
Load the dataset
A subset of invasive breast carcinoma data from primary tumor tissue. See ?tcga for more information on loading the full dataset or metadata.
library(tcgaViz)
library(ggplot2)
data(tcga)
head(tcga$genes)
#> # A tibble: 6 x 2
#> sample ICOS
#> <chr> <dbl>
#> 1 TCGA-3C-AAAU-01 1.25
#> 2 TCGA-A2-A04Q-01 7.79
#> 3 TCGA-A2-A0T4-01 4.97
#> 4 TCGA-A8-A08S-01 3.69
#> 5 TCGA-A8-A09B-01 2.55
#> 6 TCGA-A8-A0AD-01 3.72
head(tcga$cells$Cibersort_ABS)
#> # A tibble: 6 x 24
#> sample study B_cell_naive B_cell_memory B_cell_plasma T_cell_CD8.
#> <chr> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 TCGA-3C-AAAU-01 BRCA 0 0.0221 0.0192 0.0129
#> 2 TCGA-A2-A04Q-01 BRCA 0.0274 0.0249 0.0236 0.118
#> 3 TCGA-A2-A0T4-01 BRCA 0.0167 0 0.0159 0.0432
#> 4 TCGA-A8-A08S-01 BRCA 0 0.00425 0 0.0217
#> 5 TCGA-A8-A09B-01 BRCA 0.0146 0 0.00612 0.0256
#> 6 TCGA-A8-A0AD-01 BRCA 0.000919 0.000797 0.00290 0
#> # … with 18 more variables: T_cell_CD4._naive <dbl>,
#> # T_cell_CD4._memory_resting <dbl>, T_cell_CD4._memory_activated <dbl>,
#> # T_cell_follicular_helper <dbl>, T_cell_regulatory_.Tregs. <dbl>,
#> # T_cell_gamma_delta <dbl>, NK_cell_resting <dbl>, NK_cell_activated <dbl>,
#> # Monocyte <dbl>, Macrophage_M0 <dbl>, Macrophage_M1 <dbl>,
#> # Macrophage_M2 <dbl>, Myeloid_dendritic_cell_resting <dbl>,
#> # Myeloid_dendritic_cell_activated <dbl>, Mast_cell_activated <dbl>,
#> # Mast_cell_resting <dbl>, Eosinophil <dbl>, Neutrophil <dbl>
Violin plot of cell subtypes
And perform a significance of a Wilcoxon adjusted test according to the expression level (high or low) of a selected gene.
(df <- convert2biodata(
algorithm = "Cibersort_ABS",
disease = "breast invasive carcinoma",
tissue = "Primary Tumor",
gene_x = "ICOS"
))
#> # A tibble: 660 x 3
#> high cell_type value
#> * <fct> <fct> <dbl>
#> 1 Low B_cell_naive 0.00001
#> 2 High B_cell_naive 0.0274
#> 3 High B_cell_naive 0.0167
#> 4 Low B_cell_naive 0.00001
#> 5 Low B_cell_naive 0.0146
#> 6 Low B_cell_naive 0.000929
#> 7 Low B_cell_naive 0.00180
#> 8 High B_cell_naive 0.0112
#> 9 Low B_cell_naive 0.0141
#> 10 Low B_cell_naive 0.00546
#> # … with 650 more rows
(stats <- calculate_pvalue(df))
#> Breast Invasive Carcinoma (BRCA; Primary Tumor)
#> Wilcoxon-Mann-Whitney test with Benjamini & Hochberg correction (n_low = 16; n_high = 14).
#> # A tibble: 6 x 9
#> `Cell type` `Average(High)` `Average(Low)` `SD(High)` `SD(Low)`
#> <fct> <dbl> <dbl> <dbl> <dbl>
#> 1 Macrophage_M1 0.0454 0.00943 0.0328 0.0116
#> 2 Macrophage_M2 0.109 0.0697 0.0321 0.0368
#> 3 T_cell_CD4._memory_resting 0.0504 0.0122 0.0377 0.0124
#> 4 T_cell_CD8. 0.0498 0.0127 0.0387 0.00934
#> 5 T_cell_follicular_helper 0.0352 0.0119 0.0259 0.00691
#> 6 T_cell_gamma_delta 0.00823 0.000956 0.0101 0.00258
#> # … with 4 more variables: Average(High - Low) <dbl>, P-value <dbl>,
#> # P-value adjusted <dbl>, Significance <chr>
plot(df, stats = stats)
Advanced parameters
With ggplot2::theme() expressions.
(df <- convert2biodata(
algorithm = "Cibersort_ABS",
disease = "breast invasive carcinoma",
tissue = "Primary Tumor",
gene_x = "ICOS",
stat = "quantile"
))
#> # A tibble: 352 x 3
#> high cell_type value
#> * <fct> <fct> <dbl>
#> 1 25% B_cell_naive 0.00001
#> 2 75% B_cell_naive 0.0274
#> 3 25% B_cell_naive 0.0146
#> 4 75% B_cell_naive 0.0112
#> 5 25% B_cell_naive 0.0141
#> 6 25% B_cell_naive 0.00546
#> 7 75% B_cell_naive 0.0289
#> 8 75% B_cell_naive 0.00376
#> 9 25% B_cell_naive 0.00001
#> 10 75% B_cell_naive 0.00118
#> # … with 342 more rows
(stats <- calculate_pvalue(
df,
method_test = "t_test",
method_adjust = "bonferroni",
p_threshold = 0.05
))
#> Breast Invasive Carcinoma (BRCA; Primary Tumor)
#> Student's t-test with bonferroni correction (n_low = 8; n_high = 8).
#> # A tibble: 2 x 9
#> `Cell type` `Average(75%)` `Average(25%)` `SD(75%)` `SD(25%)` `Average(75% - …
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Macrophage… 0.0646 0.00560 0.0348 0.00651 0.0590
#> 2 Macrophage… 0.117 0.0456 0.0274 0.0216 0.0719
#> # … with 3 more variables: P-value <dbl>, P-value adjusted <dbl>,
#> # Significance <chr>
plot(
df,
stats = stats,
type = "boxplot",
dots = TRUE,
xlab = "Expression level of the 'ICOS' gene by cell type",
ylab = "Percent of relative abundance\n(from the Cibersort_ABS algorithm)",
title = toupper("Differential analysis of immune cell type abundance
based on RNASeq gene-level expression from The Cancer Genome Atlas"),
axis.text.y = element_text(size = 8, hjust = 0.5),
plot.title = element_text(face = "bold", hjust = 0.5),
plot.subtitle = element_text(size = , face = "italic", hjust = 0.5),
draw = FALSE
) + labs(
subtitle = paste("Breast Invasive Carcinoma (BRCA; Primary Tumor):",
"Student's t-test with Bonferroni (P < 0.05)")
)
