Variant Analysis of RNA-seq Reads from 96 Canine CD4+ PTCLs (Cohort 2)

Introduction

The purpose of this script is to perform variant analysis on maf files with maftools. The maftools documentation is available here: https://www.bioconductor.org/packages/release/bioc/vignettes/maftools/inst/doc/maftools.html

Software

# First time only
BiocManager::install("maftools")

# Load every time
library(knitr)
library(maftools)
library(dplyr)
library(VennDiagram)
library(ggplot2)

Data input

Input: One concatenated .maf file of all 96 canine CD4+ PTCL samples and one concatenated .maf file of all control samples (5 samples of sorted nodal CD4+ T cells and 2 samples of sorted CD4+ thymocytes). These input files were generated from RNA-seq data with the GATK pipeline. SNPs and INDELs were filtered based on these recommendations for hard filtering from the GATK documentation: https://gatk.broadinstitute.org/hc/en-us/articles/360035890471-Hard-filtering-germline-short-variants. SNPs with QD2 < 2, SOR > 3, FS > 60, MQ < 40, MQRankSum < -12.5, and ReadPosRankSum < -8 and INDELs with QD < 2, FS > 200, and ReadPosRankSum < -20 were excluded. After filtering, vcf files were annotated with the Ensembl Variant Effect Predictor (VEP) and converted to maf files with vcf2maf. Variants annotated as intronic or having low or mediator effects were also filtered out and excluded.

# set working directory
setwd("C:/Users/edlarsen/Documents/PTCLRNASeq")

# read PTCL maf file
mymaf <- read.maf(maf = 'Cohort_2/Input/AllPTCLs.CanFam31.QD2.vep.filtered.maf')

# read CTRL maf file
mymaf_ctrl <- read.maf(maf = 'Cohort_2/Input/AllCTRLs.CanFam31.QD2.vep.filtered.maf')

Inspect maf objects and export a summary

PTCLs

# Print a table of the 20 samples with the highest and lowest number of variants
sampleSummary <- getSampleSummary(mymaf)
first20Samples <- head(sampleSummary, 20)
last20Samples <- tail(sampleSummary, 20)
kable(first20Samples, caption = "Top 20 Canine CD4 PTCL Samples with Highest Number of Variants")

Top 20 Canine CD4 PTCL Samples with Highest Number of Variants

Tumor_Sample_Barcode	Frame_Shift_Del	Frame_Shift_Ins	In_Frame_Del	In_Frame_Ins	Missense_Mutation	Nonsense_Mutation	Nonstop_Mutation	Splice_Site	Translation_Start_Site	total
CI155427	143	709	119	338	704	239	8	9367	2	11629
CI161277	151	634	131	332	661	240	8	9327	4	11488
CI104568	148	616	123	331	685	206	9	9348	8	11474
CI124799	168	627	116	308	604	238	12	9328	8	11409
CI153070	192	640	128	302	729	236	11	9130	4	11372
CI153427	140	652	128	335	612	235	8	9255	7	11372
CI158606	138	538	124	353	560	213	5	9410	6	11347
CI150689	138	630	118	326	654	254	14	9188	6	11328
CI165189	128	661	132	309	765	232	9	9078	8	11322
CI124809	156	656	121	295	593	230	9	9245	15	11320
CI165776	152	590	123	321	648	230	12	9218	4	11298
CI164860	139	615	126	322	632	243	10	9203	6	11296
CI167185	172	571	115	261	639	215	11	9303	8	11295
CI105835	149	601	133	308	685	223	7	9172	7	11285
CI152837	140	569	105	317	678	246	12	9204	7	11278
CI171792	132	711	123	313	579	225	10	9139	6	11238
CI164934	149	551	111	257	856	233	11	9048	4	11220
CI166465	149	584	127	307	609	202	9	9224	9	11220
CI163077	131	619	119	336	659	242	10	9092	8	11216
CI164968	157	621	106	258	629	235	14	9187	8	11215

# Print a table of the top 20 mutated genes
geneSummary <- getGeneSummary(mymaf)
topVariantGenes <- head(geneSummary, 20)
kable(topVariantGenes, caption = "Top 20 Mutated Genes in Canine CD4 PTCL")

Top 20 Mutated Genes in Canine CD4 PTCL

Hugo_Symbol	Frame_Shift_Del	Frame_Shift_Ins	In_Frame_Del	In_Frame_Ins	Missense_Mutation	Nonsense_Mutation	Nonstop_Mutation	Splice_Site	Translation_Start_Site	total	MutatedSamples	AlteredSamples
COL7A1	3	7	0	1	40	2	0	3527	0	3580	96	96
FLNA	0	0	0	2	81	0	0	2230	0	2313	96	96
WDR90	87	32	2	5	29	0	0	2089	0	2244	96	96
SZT2	5	1	186	0	38	3	0	1970	0	2203	96	96
NBEAL2	10	3	96	2	38	0	0	2003	0	2152	96	96
PIEZO1	3	2	0	2	28	0	0	2093	0	2128	96	96
CPSF1	39	4	0	0	5	1	0	1951	0	2000	96	96
DYNC1H1	2	31	0	73	21	1	0	1863	0	1991	96	96
FASN	88	0	2	1	24	0	0	1875	0	1990	96	96
STAB1	2	29	0	2	31	3	0	1877	0	1944	96	96
KMT2D	8	12	14	2	97	5	0	1704	0	1842	96	96
PLXNB2	2	3	0	1	21	1	0	1666	0	1694	96	96
LAMB2	4	0	0	1	10	1	0	1630	0	1646	96	96
SSPO	4	8	0	8	49	6	0	1473	0	1548	96	96
SCRIB	3	8	3	0	12	0	0	1495	0	1521	96	96
IPO4	1	4	95	1	8	1	0	1386	1	1497	96	96
TRAF7	76	0	1	0	10	0	0	1396	0	1483	96	96
PLEC	3	2	4	1	50	4	0	1395	0	1459	96	96
USP19	0	80	2	0	5	1	0	1343	0	1431	96	96
AGRN	3	5	0	2	23	1	0	1392	0	1426	96	96

# Write maf summary to an output file
write.mafSummary(maf = mymaf, basename = 'Cohort_2/Output/CD4PTCL_maftools')

Controls

# Print a table of the 20 samples with the highest and lowest number of variants
sampleSummary_ctrl <- getSampleSummary(mymaf_ctrl)
first20Samples_ctrl <- head(sampleSummary_ctrl, 20)
last20Samples_ctrl <- tail(sampleSummary_ctrl, 20)
kable(first20Samples_ctrl, caption = "Top 20 Canine CD4 Control Samples with Highest Number of Variants")

Top 20 Canine CD4 Control Samples with Highest Number of Variants

Tumor_Sample_Barcode	Frame_Shift_Del	Frame_Shift_Ins	In_Frame_Del	In_Frame_Ins	Missense_Mutation	Nonsense_Mutation	Nonstop_Mutation	Splice_Site	Translation_Start_Site	total
CI80400	206	524	121	278	662	202	9	8412	8	10422
CI157953	142	578	133	254	682	228	10	8334	5	10366
CI80397	194	618	119	284	536	204	10	8387	7	10359
CI156615	143	554	120	276	586	195	8	8389	5	10276
CI80399	191	496	115	275	652	216	7	8314	9	10275
CI156616	163	541	106	261	576	202	6	8195	7	10057
CI157907	171	589	114	269	536	209	10	8083	2	9983
	0	0	0	0	0	0	0	0	0	0
Tumor_Sample_Barcode	0	0	0	0	0	0	0	0	0	0

# Print a table of the top 20 mutated genes
geneSummary_ctrl <- getGeneSummary(mymaf_ctrl)
topVariantGenes_ctrl <- head(geneSummary_ctrl, 20)
kable(topVariantGenes_ctrl, caption = "Top 20 Mutated Genes in Canine CD4 Controls")

Top 20 Mutated Genes in Canine CD4 Controls

Hugo_Symbol	Frame_Shift_Del	Frame_Shift_Ins	In_Frame_Del	In_Frame_Ins	Missense_Mutation	Nonsense_Mutation	Nonstop_Mutation	Splice_Site	Translation_Start_Site	total	MutatedSamples	AlteredSamples
COL7A1	0	0	0	0	2	1	0	248	0	251	7	7
FLNA	0	0	0	0	9	0	0	165	0	174	7	7
SZT2	0	0	13	1	3	2	0	153	0	172	7	7
PIEZO1	1	0	0	0	3	0	0	162	0	166	7	7
NBEAL2	2	1	7	0	2	0	0	153	0	165	7	7
WDR90	7	3	1	0	4	0	0	150	0	165	7	7
CPSF1	2	0	0	0	0	0	0	145	0	147	7	7
KMT2D	0	0	1	0	10	0	0	130	0	141	7	7
SSPO	0	1	0	1	6	1	0	131	0	140	7	7
FASN	4	0	0	0	0	0	0	134	0	138	7	7
DYNC1H1	0	2	0	4	3	0	0	123	0	132	7	7
LAMB2	0	0	0	0	1	0	0	125	0	126	7	7
PLXNB2	0	0	0	0	0	0	0	124	0	124	7	7
TRAF7	6	0	0	0	0	0	0	107	0	113	7	7
SCRIB	0	1	0	0	0	0	0	110	0	111	7	7
KMT2B	0	2	6	0	0	0	0	102	0	110	7	7
IPO4	0	1	7	0	0	0	0	98	0	106	7	7
PLEC	1	2	0	0	2	0	0	101	0	106	7	7
CC2D1A	1	0	0	0	2	1	0	100	0	104	7	7
TLN1	0	2	0	0	1	0	0	100	0	103	7	7

# Write maf summary to an output file
write.mafSummary(maf = mymaf_ctrl, basename = 'Cohort_2/Output/CD4CTRL_maftools')

Initial data visualization

# set colors to annotate mutation types
var_cols = RColorBrewer::brewer.pal(n = 10, name = 'Paired')
names(var_cols) = c(
  'In_Frame_Ins',
  'Missense_Mutation',
  'In_Frame_Del',
  'Frame_Shift_Ins',
  'Translation_Start_Site',
  'Nonstop_Mutation',
  'Frame_Shift_Del',
  'Multi_Hit',
  'Nonsense_Mutation',
  'Splice_Site'
)

titvcols = RColorBrewer::brewer.pal(n = 6, name = 'Set3')
names(titvcols) = c("C>T", "C>G", "C>A", "T>A", "T>C", "T>G")

Plotting MAF summaries

Displays the number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification. ### PTCLs

plotmafSummary(maf = mymaf,
               color = var_cols,
               titvColor = titvcols,
               rmOutlier = TRUE, 
               addStat = 'median', 
               dashboard = TRUE, 
               titvRaw = FALSE)

Controls

plotmafSummary(maf = mymaf_ctrl,
               color = var_cols,
               titvColor = titvcols,
               rmOutlier = TRUE, 
               addStat = 'median', 
               dashboard = TRUE, 
               titvRaw = FALSE)

Barplots of mutated genes

PTCLs

par(mar = c(5, 0.1, 4, 2))

mafbarplot(
  mymaf,
  color = var_cols,
  n = 20,
  genes = NULL,
  fontSize = 0.6,
  includeCN = FALSE,
  legendfontSize = 1,
  borderCol = "#34495e",
  showPct = TRUE
)

Controls

par(mar = c(5, 0.1, 4, 2))

mafbarplot(
  mymaf_ctrl,
  color = var_cols,
  n = 20,
  genes = NULL,
  fontSize = 0.6,
  includeCN = FALSE,
  legendfontSize = 1,
  borderCol = "#34495e",
  showPct = TRUE
)

Oncoplot of genes commonly mutated in human PTCL

Canine CD4+ PTCLs

hPTCLgenes = c("TET2", "DNMT3A", "PTEN", "TP53", "CDKN2A", "MYC", "STAT3", "BCL11B", "BCL6", "CD244", "CD247", "FASLG", "TP63", "TPRG1", "FYN", "IBTK", "LATS1", "ZC3H12D", "TNFAIP3")

# subset PTCL maf for only fusion gene partners
mymaf_hPTCL <- subsetMaf(mymaf, genes = hPTCLgenes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_hPTCL,
  genes = hPTCLgenes,
  colors = var_cols,
  titleText = "Canine CD4+ PTCL Variants in Genes Commonly Mutated in Human PTCL"
)

Canine CD4+ control lymphocytes and thymocytes

# subset control maf for only fusion gene partners
mymaf_CTRL_hPTCL <- subsetMaf(mymaf_ctrl, genes = hPTCLgenes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_CTRL_hPTCL,
  genes = hPTCLgenes,
  colors = var_cols,
  titleText = "Canine CD4+ CTRL Variants in Genes Commonly Mutated in Human PTCL"
)

Oncoplot of genes commonly mutated in canine PTCL

PTCLs

# define list of genes
genes <- c("PTEN", "SATB1", "MAP2K1", "NLRP14", "KCND2", "PSMA1", "TBC1D26")

# subset PTCL maf for only these genes
mymaf_PTCLgenes <- subsetMaf(mymaf, genes=genes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_PTCLgenes,
  genes = genes,
  color = var_cols,
  titleText = "Canine CD4+ PTCL Variants in Genes Commonly Mutated in Canine TCL"
)

Controls

# subset control maf for only these genes
mymaf_ctrl_PTCLgenes <- subsetMaf(mymaf_ctrl, genes=genes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_ctrl_PTCLgenes,
  genes = genes,
  color = var_cols,
  titleText = "Canine CD4+ CTRL Variants in Genes Commonly Mutated in Canine TCL"
)

Oncoplot of canine PTCL fusion gene partners

PTCLs

# define list of fusion gene partners
fusiongenes = c("GATD3A", "LMO4", "PTMA", "NCL", "JPT1", "MROH1", "TPD52L2", "TOX2", "REV3L", "FYN", "HMGB1", "BZW1", "HSPD1", "CHD3", "PER1", "EIF5A", "GRB10", "IKZF1", "MYC", "TRIB1", "YWHAZ", "KLF10", "SRSF5")

# subset PTCL maf for only fusion gene partners
mymaf_PTCLfusion <- subsetMaf(mymaf, genes = fusiongenes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_PTCLfusion,
  genes = fusiongenes,
  color = var_cols,
  titleText = "Canine CD4+ PTCL Variants Called in Fusion Partner Genes"
)

Controls

# subset control maf for only fusion gene partners
mymaf_CTRLfusion <- subsetMaf(mymaf_ctrl, genes = fusiongenes)

## -Processing clinical data

# draw oncoplot
oncoplot(
  maf = mymaf_CTRLfusion,
  genes = fusiongenes,
  color = var_cols,
  titleText = "Canine CD4+ CTRL Variants Called in Fusion Partner Genes"
)

Transition and Transversions

Boxplot summarizes the overall distribution of different conversions, and stacked barplot shows fraction of conversions in each sample. ### PTCLs

mymaf.titv = titv(maf = mymaf,
                  plot = FALSE,
                  useSyn = TRUE)
# plot titv summary
plotTiTv(res = mymaf.titv, color = titvcols)

Controls

mymaf_ctrl.titv = titv(maf = mymaf_ctrl,
                  plot = FALSE,
                  useSyn = TRUE)
# plot titv summary
plotTiTv(res = mymaf_ctrl.titv, color = titvcols)

Compare variants called in both tumor and control samples

Venn Diagram

tumor_data <- mymaf@data
ctrl_data <- mymaf_ctrl@data

# select columns for matching
tumor_vars <- tumor_data[, c("Chromosome", "Start_Position", "End_Position", "Variant_Classification", "Variant_Type")]
ctrl_vars <- ctrl_data[, c("Chromosome", "Start_Position", "End_Position", "Variant_Classification", "Variant_Type")]

# find common variants
shared_variants <- merge(tumor_vars, ctrl_vars, 
                         by = c("Chromosome", "Start_Position", "End_Position", "Variant_Classification", "Variant_Type"),
                         allow.cartesian = TRUE)


##### Venn Diagram #####
# Read in list of variants in both groups
tumor_vars_unique <- unique(paste(tumor_vars$Chromosome, tumor_vars$Start_Position, tumor_vars$End_Position, tumor_vars$Variant_Classification, tumor_vars$Variant_Type))

ctrl_vars_unique <- unique(paste(ctrl_vars$Chromosome, ctrl_vars$Start_Position, ctrl_vars$End_Position, ctrl_vars$Variant_Classification, ctrl_vars$Variant_Type))

venn1 <- venn.diagram(
  x = list(tumor_vars_unique, ctrl_vars_unique),
  category.names = c("CD4+ PTCL", "CD4+ CTRL"),
  
  # Output features
  filename = NULL,
  disable.logging = TRUE,
  
  # Title
  main = "Variants Shared Between CD4+ PTCL and \nControl CD4+ Lymphocytes and Thymocytes",
  main.cex = 1.5,
  main.fontfamily = "sans",
  main.fontface = "bold",
  
  # Circles
  fill = c(alpha("#440154ff", 0.3), alpha('#21908dff', 0.3)),
  lwd = 1,
  col = c("#440154ff", '#21908dff'),
  
  # Numbers
  cex=1.5,
  fontfamly = "sans",
  
  # Categories
  cat.cex = 1.5,
  cat.fontfamily = "sans",
  cat.fontface = "bold",
  cat.dist = c(0.05, 0.05),
  cat.pos = c(-27, 27),
  cat.default.pos = "outer",
  cat.col = c("#440154ff", '#21908dff'),
  scaled = FALSE,
)
grid.newpage()
grid.draw(venn1)

Filter genes from tumor samples that were called in control samples

# filter tumor maf for shared variants
shared_condition <- with(tumor_data, 
                         paste(Chromosome, Start_Position, End_Position, Variant_Classification, Variant_Type) %in%
                           paste(shared_variants$Chromosome, shared_variants$Start_Position, shared_variants$End_Position, 
                                 shared_variants$Variant_Classification, shared_variants$Variant_Type))

tumor_data_filtered <- tumor_data[!shared_condition, ]

mymaf_filtered <- read.maf(tumor_data_filtered)

# export
write.table(tumor_data_filtered, file = "ptcl_unique_vars_only_with_splice_vars.maf", sep = "\t", quote = FALSE, row.names = FALSE)

filtered_tumor_data <- mymaf_filtered@data
filtered_tumor_vars <- filtered_tumor_data[, c("Chromosome", "Start_Position", "End_Position", "Variant_Classification", "Variant_Type")]
filtered_tumor_vars_unique <- unique(paste(filtered_tumor_vars$Chromosome, filtered_tumor_vars$Start_Position, filtered_tumor_vars$End_Position, filtered_tumor_vars$Variant_Classification, filtered_tumor_vars$Variant_Type))
paste("Unique variant calls:", length(filtered_tumor_vars_unique), sep=" ")

Data visualization after filtering

Data visualization of only those genes that were mutated in tumor samples, and not in control samples.

Plotting MAF summary in PTCLs

Displays the number of variants in each PTCL sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification.

plotmafSummary(maf = mymaf_filtered,
               color = var_cols,
               titvColor = titvcols,
               rmOutlier = TRUE, 
               addStat = 'median', 
               dashboard = TRUE, 
               titvRaw = FALSE)

Barplot of mutated genes in PTCLs

par(mar = c(5, 0.1, 4, 2))
mafbarplot(
  mymaf_filtered,
  n = 20,
  genes = NULL,
  color = var_cols,
  fontSize = 0.7,
  includeCN = FALSE,
  legendfontSize = 1,
  borderCol = "#34495e",
  showPct = TRUE
)

Oncoplots

Top tumor-specific mutated genes

Oncoplot for the top 20 mutated genes after filtering out genes also called in control samples. Note: Variants annotated as Multi_Hit are those genes which are mutated more than once in the same sample.

#par(mar = c(5, 2, 4, 2))
oncoplot(maf = mymaf_filtered,
         fontSize = 0.5,
         top = 20,
         colors = var_cols,
         titleText = "Top Canine CD4+ PTCL-Specific Variants")

Oncoplot of oncogenic signaling pathway genes in PTCLs

oncoplot(maf = mymaf_filtered,
         colors = var_cols,
         pathways = "sigpw",
         titleText = "Top 10 Mutated Oncogenic Signaling Pathways in Canine CD4+ PTCL",
         gene_mar = 8, 
         fontSize = 0.8, 
         topPathways = 10,
         collapsePathway = TRUE)

oncoplot(maf = mymaf_filtered,
         colors = var_cols,
         pathways = "sigpw",
         titleText = "Details of Top Mutated Oncogenic Signaling Pathway in Canine CD4+ PTCL",
         gene_mar = 8, 
         fontSize = 0.8, 
         topPathways = 1)

Oncoplot of genes commonly mutated in human PTCL

# draw oncoplot
oncoplot(
  maf = mymaf_filtered,
  genes = hPTCLgenes,
  colors = var_cols,
  titleText = "Canine CD4+ PTCL-Specific Variants in Genes Commonly Mutated in Human PTCL"
)

Oncoplot of commonly mutated genes in canine PTCL

oncoplot(
  maf = mymaf_filtered,
  genes = genes,
  color = var_cols,
  titleText = "Canine CD4+ PTCL-Specific Variants in Genes Commonly Mutated in Canine TCL"
)

Oncoplot of fusion gene partners in PTCLs

oncoplot(
  maf = mymaf_filtered,
  genes = fusiongenes,
  color = var_cols,
  titleText = "Canine CD4+ PTCL-Specific Variants Called in Fusion Partner Genes"
)

Transition and Transversions

Boxplot summarizes the overall distribution of different conversions, and stacked barplot shows fraction of conversions in each sample.

mymafFiltered.titv = titv(maf = mymaf_filtered,
                  plot = FALSE,
                  useSyn = TRUE)
# plot titv summary
plotTiTv(res = mymafFiltered.titv, color = titvcols)

Rainfall plots of the 5 tumors with the most mutations

Visualizes hypermutated genomic regions in cancer genomes by plotting inter variant distance on a linear genomic scale. “Kataegis” are defined as those genomic segments containing 6 or more consecutive mutations with an average inter-mutation distance of less than or equal to 1,00 bp 5. If tsb = NULL, the most mutated sample is plotted.

top5 <- head(sampleSummary, 5)
top5 <- as.character(top5$Tumor_Sample_Barcode[1:5])
top5

## [1] "CI155427" "CI161277" "CI104568" "CI124799" "CI153070"

for (barcode in top5){
  rainfallPlot(maf = mymaf_filtered,
             detectChangePoints = TRUE,
             tsb = barcode,
             width = 10,
             height = 5,
             pointSize = 0.8)
}

Somatic interactions

The somaticInteractions function performs pair-wise Fisher’s Exact test to detect signfiicant pairs of mutually exclusive or co-occurring sets of genes.

#exclusive/co-occurance event analysis on top 25 tumor-specific mutated genes. 
somaticInteractions(maf = mymaf_filtered, 
                    top = 25, 
                    pvalue = c(0.05, 0.1),
                    fontSize = 0.6)

Citations

sessionInfo()

## R version 4.4.0 (2024-04-24 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 22631)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=English_United States.utf8 
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/Denver
## tzcode source: internal
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ggplot2_3.5.1       VennDiagram_1.7.3   futile.logger_1.4.3
## [4] dplyr_1.1.4         maftools_2.20.0     knitr_1.49         
## 
## loaded via a namespace (and not attached):
##  [1] Matrix_1.7-0         gtable_0.3.6         jsonlite_1.8.9      
##  [4] compiler_4.4.0       tidyselect_1.2.1     jquerylib_0.1.4     
##  [7] scales_1.3.0         splines_4.4.0        yaml_2.3.8          
## [10] fastmap_1.2.0        lattice_0.22-6       DNAcopy_1.78.0      
## [13] R6_2.5.1             generics_0.1.3       tibble_3.2.1        
## [16] munsell_0.5.1        bslib_0.8.0          pillar_1.9.0        
## [19] RColorBrewer_1.1-3   rlang_1.1.3          utf8_1.2.4          
## [22] cachem_1.1.0         xfun_0.49            sass_0.4.9          
## [25] cli_3.6.2            withr_3.0.2          magrittr_2.0.3      
## [28] formatR_1.14         futile.options_1.0.1 digest_0.6.35       
## [31] rstudioapi_0.17.1    lifecycle_1.0.4      vctrs_0.6.5         
## [34] evaluate_1.0.1       glue_1.7.0           data.table_1.16.4   
## [37] farver_2.1.2         lambda.r_1.2.4       codetools_0.2-20    
## [40] survival_3.5-8       colorspace_2.1-1     fansi_1.0.6         
## [43] rmarkdown_2.29       tools_4.4.0          pkgconfig_2.0.3     
## [46] htmltools_0.5.8.1

citation()

## To cite R in publications use:
## 
##   R Core Team (2024). _R: A Language and Environment for Statistical
##   Computing_. R Foundation for Statistical Computing, Vienna, Austria.
##   <https://www.R-project.org/>.
## 
## A BibTeX entry for LaTeX users is
## 
##   @Manual{,
##     title = {R: A Language and Environment for Statistical Computing},
##     author = {{R Core Team}},
##     organization = {R Foundation for Statistical Computing},
##     address = {Vienna, Austria},
##     year = {2024},
##     url = {https://www.R-project.org/},
##   }
## 
## We have invested a lot of time and effort in creating R, please cite it
## when using it for data analysis. See also 'citation("pkgname")' for
## citing R packages.