Title:	Interactive Analysis of UCSC Xena Data
Version:	2.1.0
Maintainer:	Shixiang Wang <w_shixiang@163.com>
Description:	Provides functions and a Shiny application for downloading, analyzing and visualizing datasets from UCSC Xena (http://xena.ucsc.edu/), which is a collection of UCSC-hosted public databases such as TCGA, ICGC, TARGET, GTEx, CCLE, and others.
License:	GPL (≥ 3)
URL:	https://github.com/openbiox/UCSCXenaShiny, https://openbiox.github.io/UCSCXenaShiny/
BugReports:	https://github.com/openbiox/UCSCXenaShiny/issues
Depends:	R (≥ 3.5)
Imports:	digest, dplyr (≥ 0.8.3), ezcox, forcats, ggplot2 (≥ 3.2.0), ggpubr (≥ 0.2), httr, magrittr (≥ 1.5), ppcor, psych, purrr, rlang, shiny (≥ 1.3.2), stats, stringr, tibble (≥ 2.1.3), tidyr, UCSCXenaTools, utils
Suggests:	covr (≥ 3.2.1), cowplot, DT (≥ 0.5), furrr, future, ggrepel, ggstatsplot, knitr, pacman, plotly, plyr, RColorBrewer (≥ 1.1.2), rmarkdown, Rtsne, scales, survival, survminer, testthat (≥ 2.0.1), umap,
VignetteBuilder:	knitr
Encoding:	UTF-8
LazyData:	true
RoxygenNote:	7.2.3
NeedsCompilation:	no
Packaged:	2024-05-14 16:53:54 UTC; wsx
Author:	Shixiang Wang [aut, cre], Shensuo Li [aut], Yi Xiong [aut], Longfei Zhao [aut], Kai Gu [aut], Yin Li [aut], Fei Zhao [aut]
Repository:	CRAN
Date/Publication:	2024-05-15 14:10:06 UTC

Xena Shiny App

Description

A Shiny App for UCSC Xena Data Hubs. See https://github.com/openbiox/UCSCXenaShiny for details.

Author(s)

Maintainer: Shixiang Wang w_shixiang@163.com (ORCID)

Authors:

• Shensuo Li lishensuo@163.com

• Yi Xiong neuroxiong@openbiox.com (ORCID)

• Longfei Zhao longfei8533@126.com (ORCID)

• Kai Gu gukai1212@163.com (ORCID)

• Yin Li yinli18@fudan.edu.cn

• Fei Zhao zhaofei415@mails.ucas.edu.cn

See Also

Useful links:

• https://github.com/openbiox/UCSCXenaShiny

• https://openbiox.github.io/UCSCXenaShiny/

• Report bugs at https://github.com/openbiox/UCSCXenaShiny/issues

Pipe Operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

A default setting for pan-cancer studies

Description

A default setting for pan-cancer studies

Usage

.opt_pancan

Format

An object of class list of length 16.

TCGA: Organ Data

Description

TCGA: Organ Data

Format

A data.frame

Examples

data("TCGA.organ")

Analyze Association between Gene (Signature) and Drug Response with CCLE Data

Description

Analyze partial correlation of gene-drug association after controlling for tissue average expression.

Usage

analyze_gene_drug_response_asso(gene_list, combine = FALSE)

Arguments

Value

a data.frame

• If combine is TRUE, genes are combined as signature.

• mean.diff and median.diff indicate mean and median of normalized expression difference between High IC50 cells and Low IC50 cells. The cutoff between High and Low are median IC50.

Examples

## Not run: 
analyze_gene_drug_response_asso("TP53")
analyze_gene_drug_response_asso(c("TP53", "KRAS"))
analyze_gene_drug_response_asso(c("TP53", "KRAS"), combine = TRUE)

# Visualization
vis_gene_drug_response_asso("TP53")

## End(Not run)

Analyze Difference of Drug Response (IC50 Value (uM)) between Gene (Signature) High and Low Expression with CCLE Data

Description

Analyze Difference of Drug Response (IC50 Value (uM)) between Gene (Signature) High and Low Expression with CCLE Data

Usage

analyze_gene_drug_response_diff(
  gene_list,
  drug = "ALL",
  tissue = "ALL",
  combine = FALSE,
  cutpoint = c(50, 50)
)

Arguments

Value

a data.frame.

Examples

tissue_list <- c(
  "prostate", "central_nervous_system", "urinary_tract", "haematopoietic_and_lymphoid_tissue",
  "kidney", "thyroid", "soft_tissue", "skin", "salivary_gland",
  "ovary", "lung", "bone", "endometrium", "pancreas", "breast",
  "large_intestine", "upper_aerodigestive_tract", "autonomic_ganglia",
  "stomach", "liver", "biliary_tract", "pleura", "oesophagus"
)

drug_list <- c(
  "AEW541", "Nilotinib", "17-AAG", "PHA-665752", "Lapatinib",
  "Nutlin-3", "AZD0530", "PF2341066", "L-685458", "ZD-6474", "Panobinostat",
  "Sorafenib", "Irinotecan", "Topotecan", "LBW242", "PD-0325901",
  "PD-0332991", "Paclitaxel", "AZD6244", "PLX4720", "RAF265", "TAE684",
  "TKI258", "Erlotinib"
)

target_list <- c(
  "IGF1R", "ABL", "HSP90", "c-MET", "EGFR", "MDM2", "GS", "HDAC",
  "RTK", "TOP1", "XIAP", "MEK", "CDK4", "TUBB1", "RAF", "ALK", "FGFR"
)
## Not run: 
analyze_gene_drug_response_diff("TP53")
analyze_gene_drug_response_diff(c("TP53", "KRAS"), drug = "AEW541")
analyze_gene_drug_response_diff(c("TP53", "KRAS"),
  tissue = "kidney",
  combine = TRUE
)

# Visualization
vis_gene_drug_response_diff("TP53")

## End(Not run)

Run UCSC Xena Shiny App

Description

Run UCSC Xena Shiny App

Usage

app_run(runMode = "client", port = getOption("shiny.port"))

Arguments

Examples

## Not run: 
app_run()

## End(Not run)

Show Available Hosts

Description

Show Available Hosts

Usage

available_hosts()

Value

hosts

Examples

available_hosts()

ABSOLUTE Result of CCLE Database

Description

ABSOLUTE Result of CCLE Database

Format

A data.frame

Source

see "data_source" attribute.

Examples

data("ccle_absolute")

Phenotype Info of CCLE Database

Description

Phenotype Info of CCLE Database

Format

A data.frame

Source

UCSC Xena.

Examples

data("ccle_info")

Cleaned Phenotype Info of CCLE Database for grouping

Description

Cleaned Phenotype Info of CCLE Database for grouping

Format

A data.frame

Source

UCSC Xena.

Examples

data("ccle_info_fine")

Run Correlation between Two Variables and Support Group by a Variable

Description

Run Correlation between Two Variables and Support Group by a Variable

Usage

ezcor(
  data = NULL,
  split = FALSE,
  split_var = NULL,
  var1 = NULL,
  var2 = NULL,
  cor_method = "pearson",
  adjust_method = "none",
  use = "complete",
  sig_label = TRUE,
  verbose = TRUE
)

Arguments

Value

a data.frame

Author(s)

Yi Xiong

Run correlation between two variables in a batch mode and support group by a variable

Description

Run correlation between two variables in a batch mode and support group by a variable

Usage

ezcor_batch(
  data,
  var1,
  var2,
  split = FALSE,
  split_var = NULL,
  cor_method = "pearson",
  adjust_method = "none",
  use = "complete",
  sig_label = TRUE,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

Value

a data.frame

Author(s)

Yi Xiong, Shixiang Wang

Run partial correlation

Description

Run partial correlation

Usage

ezcor_partial_cor(
  data = NULL,
  split = FALSE,
  split_var = NULL,
  var1 = NULL,
  var2 = NULL,
  var3 = NULL,
  cor_method = "pearson",
  sig_label = TRUE,
  ...
)

Arguments

Value

a data.frame

Author(s)

Yi Xiong

See Also

ppcor::pcor.test() which this function wraps.

Fetch Identifier Value from Pan-cancer Dataset

Description

Identifier includes gene/probe etc.

Usage

get_ccle_cn_value(identifier)

get_ccle_gene_value(identifier, norm = c("rpkm", "nc"))

get_ccle_protein_value(identifier)

get_ccle_mutation_status(identifier)

get_pancan_value(
  identifier,
  subtype = NULL,
  dataset = NULL,
  host = available_hosts(),
  samples = NULL,
  ...
)

get_pancan_gene_value(identifier, norm = c("tpm", "fpkm", "nc"))

get_pancan_transcript_value(identifier, norm = c("tpm", "fpkm", "isopct"))

get_pancan_protein_value(identifier)

get_pancan_mutation_status(identifier)

get_pancan_cn_value(identifier, gistic2 = TRUE, use_thresholded_data = FALSE)

get_pancan_methylation_value(
  identifier,
  type = c("450K", "27K"),
  rule_out = NULL,
  aggr = c("NA", "mean", "Q0", "Q25", "Q50", "Q75", "Q100")
)

get_pancan_miRNA_value(identifier)

get_pcawg_gene_value(identifier)

get_pcawg_fusion_value(identifier)

get_pcawg_promoter_value(identifier, type = c("raw", "relative", "outlier"))

get_pcawg_miRNA_value(identifier, norm = c("TMM", "UQ"))

get_pcawg_APOBEC_mutagenesis_value(
  identifier = c("tCa_MutLoad_MinEstimate", "APOBECtCa_enrich", "A3A_or_A3B",
    "APOBEC_tCa_enrich_quartile", "APOBECrtCa_enrich", "APOBECytCa_enrich",
    "APOBECytCa_enrich-APOBECrtCa_enrich", "BH_Fisher_p-value_tCa", "ntca+tgan",
    "rtCa_to_G+rtCa_to_T", "rtca+tgay", "tCa_to_G+tCa_to_T",
    "ytCa_rtCa_BH_Fisher_p-value", "ytCa_rtCa_Fisher_p-value", "ytCa_to_G+ytCa_to_T",
    "ytca+tgar")
)

Arguments

Value

a named vector or list.

Functions

• get_ccle_cn_value(): Fetch copy number value from CCLE dataset

• get_ccle_gene_value(): Fetch gene expression value from CCLE dataset

• get_ccle_protein_value(): Fetch gene protein expression value from CCLE dataset

• get_ccle_mutation_status(): Fetch gene mutation info from CCLE dataset

• get_pancan_value(): Fetch identifier value from pan-cancer dataset

• get_pancan_gene_value(): Fetch gene expression value from pan-cancer dataset

• get_pancan_transcript_value(): Fetch gene transcript expression value from pan-cancer dataset

• get_pancan_protein_value(): Fetch protein expression value from pan-cancer dataset

• get_pancan_mutation_status(): Fetch mutation status value from pan-cancer dataset

• get_pancan_cn_value(): Fetch gene copy number value from pan-cancer dataset processed by GISTIC 2.0

• get_pancan_methylation_value(): Fetch gene expression value from CCLE dataset

• get_pancan_miRNA_value(): Fetch miRNA expression value from pan-cancer dataset

• get_pcawg_gene_value(): Fetch specimen-level gene expression value from PCAWG cohort

• get_pcawg_fusion_value(): Fetch specimen-level gene fusion value from PCAWG cohort

• get_pcawg_promoter_value(): Fetch specimen-level gene promoter activity value from PCAWG cohort

• get_pcawg_miRNA_value(): Fetch specimen-level miRNA value from PCAWG cohort

• get_pcawg_APOBEC_mutagenesis_value(): Fetch specimen-level gene fusion value from PCAWG cohort

Examples

## Not run: 
# Fetch TP53 expression value from pan-cancer dataset
t1 <- get_pancan_value("TP53",
  dataset = "TcgaTargetGtex_rsem_isoform_tpm",
  host = "toilHub"
)
t2 <- get_pancan_gene_value("TP53")
t3 <- get_pancan_protein_value("AKT")
t4 <- get_pancan_mutation_status("TP53")
t5 <- get_pancan_cn_value("TP53")

## End(Not run)

Keep Only Columns Used for Sample Selection

Description

Keep Only Columns Used for Sample Selection

Usage

keep_cat_cols(x, keep_sam_cols = TRUE, return_idx = TRUE)

Arguments

Value

a data.frame or a list.

Load Dataset Provided by This Package

Description

Load data from builtin or Zenodo. Option xena.zenodoDir can be used to set default path for storing extra data from Zenodo, e.g., options(xena.zenodoDir = "/home/xxx/dataset").

Usage

load_data(name)

Arguments

Value

a dataset, typically a data.frame.

Examples

data1 <- load_data("tcga_surv")
data1

data2 <- load_data("tcga_armcalls")
data2

Quick molecule analysis and report generation

Description

Quick molecule analysis and report generation

Usage

mol_quick_analysis(molecule, data_type, out_dir = ".", out_report = FALSE)

Arguments

Value

a list.

Phenotype Info of PCAWG Database

Description

Phenotype Info of PCAWG Database

Format

A data.frame

Source

UCSC Xena.

Examples

data("pcawg_info")

Cleaned Phenotype Info of PCAWG Database for grouping

Description

Cleaned Phenotype Info of PCAWG Database for grouping

Format

A data.frame

Source

UCSC Xena.

Examples

data("pcawg_info_fine")

Purity Data of PCAWG

Description

Purity Data of PCAWG

Format

A data.frame

Source

UCSC Xena.

Examples

data("pcawg_purity")

download data for shiny general analysis

Description

download data for shiny general analysis

Usage

query_general_value(
  L1,
  L2,
  L3,
  database = c("toil", "pcawg", "ccle"),
  tpc_value_nonomics = NULL,
  opt_pancan = NULL,
  custom_metadata = NULL
)

Arguments

Examples

## Not run: 
general_value_id = UCSCXenaShiny:::query_general_id()
tcga_value_option = general_value_id[["value"]][[1]]
tcga_index_value = tcga_value_option[["Tumor index"]]
tcga_immune_value = tcga_value_option[["Immune Infiltration"]]
tcga_pathway_value = tcga_value_option[["Pathway activity"]]
tcga_phenotype_value = tcga_value_option[["Phenotype data"]]
clinical_phe = tcga_phenotype_value[["Clinical Phenotype"]]
x_data = UCSCXenaShiny:::query_general_value(
           "Molecular profile", "mRNA Expression", "TP53", "toil",
           tcga_index_value, tcga_immune_value, tcga_pathway_value, 
           clinical_phe)
                                             
y_data = UCSCXenaShiny:::query_general_value(
           "Immune Infiltration", "CIBERSORT", "Monocyte", "toil",
           tcga_index_value, tcga_immune_value, tcga_pathway_value, 
           clinical_phe)

## End(Not run)

Get Molecule or Signature Data Values from Dense (Genomic) Matrix Dataset of UCSC Xena Data Hubs

Description

Get Molecule or Signature Data Values from Dense (Genomic) Matrix Dataset of UCSC Xena Data Hubs

Usage

query_molecule_value(dataset, molecule, host = NULL)

Arguments

Value

a named vector.

Examples

# What does dense matrix mean?
table(UCSCXenaTools::XenaData$Type)
# It is a the UCSC Xena dataset with "Type" equals to "genomicMatrix"
## Not run: 
dataset <- "ccle/CCLE_copynumber_byGene_2013-12-03"
x <- query_molecule_value(dataset, "TP53")
head(x)

signature <- "TP53 + 2*KRAS - 1.3*PTEN" # a space must exist in the string
y <- query_molecule_value(dataset, signature)
head(y)

## End(Not run)

Query Single Identifier or Signature Value from Pan-cancer Database

Description

Query Single Identifier or Signature Value from Pan-cancer Database

Usage

query_pancan_value(
  molecule,
  data_type = c("mRNA", "transcript", "protein", "mutation", "cnv", "methylation",
    "miRNA", "fusion", "promoter", "APOBEC"),
  database = c("toil", "ccle", "pcawg"),
  reset_id = NULL,
  opt_pancan = .opt_pancan
)

Arguments

Details

query_pancan_value() provide convenient interface to download multi-omics data from 3 databases by specifying one or several canonical datasets. It is derived from query_pancan_value() and support query for genomic signature. To query comprehensive datasets that UCSCXena supports, users can check UCSCXenaTools::XenaData and use get_pancan_value() directly.

Option opt_pancan is a nested list and allow to adjust the downloading details. For now, only cnv(toil),methylation(toil),miRNA(toil),miRNA(pcawg),promoter(pcawg) support optional parameters. The default set is .opt_pancan and we check meanings of sublist(parameters) through the following relationship.

Value

a list.

"toil" database

1. mRNA–get_pancan_gene_value()

2. transcript–get_pancan_transcript_value()

3. protein–get_pancan_protein_value()

4. mutation–get_pancan_mutation_status()

5. cnv–get_pancan_cn_value()

6. methylation–get_pancan_methylation_value()

7. miRNA–get_pancan_miRNA_value()

"ccle" database

1. mRNA–get_ccle_gene_value()

2. protein–get_ccle_protein_value()

3. mutation–get_ccle_mutation_status()

4. cnv–get_ccle_cn_value()

"pcawg" database

1. mRNA–get_pcawg_gene_value()

2. miRNA–get_pcawg_miRNA_value()

3. promoter–get_pcawg_promoter_value()

4. fusion–get_pcawg_fusion_value()

5. APOBEC–get_pcawg_APOBEC_mutagenesis_value()

Examples

## Not run: 
query_pancan_value("KRAS")
query_pancan_value("KRAS", database = "ccle")
query_pancan_value("KRAS", database = "pcawg")


query_pancan_value("ENSG00000000419",
  database = "pcawg",
  data_type = "fusion"
) # gene symbol also work

.opt_pancan

opt_pancan = list(toil_cnv = list(use_thresholded_data = FALSE))
query_pancan_value("PTEN",data_type = "cnv", database = "toil", opt_pancan = opt_pancan)


opt_pancan = list(toil_methylation = list(type = "450K",rule_out = "cg21115430", aggr = "Q25"))
query_pancan_value("PTEN",data_type = "methylation", database = "toil", opt_pancan = opt_pancan)


## End(Not run)

Group TPC samples by build-in or custom phenotype and support filtering or merging operations

Description

Group TPC samples by build-in or custom phenotype and support filtering or merging operations

Usage

query_tcga_group(
  database = c("toil", "pcawg", "ccle"),
  cancer = NULL,
  custom = NULL,
  group = "Gender",
  filter_by = NULL,
  filter_id = NULL,
  merge_by = NULL,
  merge_quantile = FALSE,
  return_all = FALSE
)

Arguments

Value

a list object with grouping samples and statistics

Examples

## Not run: 
query_tcga_group(group = "Age")

query_tcga_group(cancer="BRCA", 
              group = "Stage_ajcc"
              )

query_tcga_group(cancer="BRCA", 
              group = "Stage_ajcc",
              filter_by = list(
                c("Code",c("TP"),"+"),
                c("Stage_ajcc",c(NA),"-"))
              )
              
query_tcga_group(cancer="BRCA", 
                 group = "Stage_ajcc",
                 filter_by = list(
                   c("Age",c(0.5),"%>"))
)

query_tcga_group(cancer="BRCA", 
                 group = "Stage_ajcc",
                 filter_by = list(
                   c("Age",c(60),">"))
)

query_tcga_group(cancer="BRCA", 
              group = "Stage_ajcc",
              merge_by = list(
                "Early"=c("Stage I"), 
                "Late" = c("Stage II","Stage III","Stage IV"))
              )

query_tcga_group(cancer="BRCA", 
                 group = "Age",
                 merge_by = list(
                   "Young"= c(20, 60), 
                   "Old"= c(60, NA)
                )
)

query_tcga_group(cancer="BRCA", 
                 group = "Age",
                 merge_quantile = TRUE,
                 merge_by = list(
                   "Young"= c(0, 0.5), 
                   "Old"= c(0.5, 1)
                 )
)

## End(Not run)

Obtain ToilHub Info for Single Molecule

Description

Obtain ToilHub Info for Single Molecule

Obtain ToilHub Info for Single Gene

Usage

query_toil_value_df(identifier = "TP53")

query_toil_value_df(identifier = "TP53")

Arguments

Value

a tibble

a tibble

Examples

## Not run: 
t <- query_toil_value_df()
t

## End(Not run)
## Not run: 
t <- query_toil_value_df()
t

## End(Not run)

TCGA Survival Analysis

Description

• Firstly, get merged data of one molecular profile value and associated clinical data from TCGA Pan-Cancer dataset.

• Secondly, filter data as your wish.

• Finally, show K-M plot.

Usage

tcga_surv_get(
  item,
  TCGA_cohort = "LUAD",
  profile = c("mRNA", "miRNA", "methylation", "transcript", "protein", "mutation", "cnv"),
  TCGA_cli_data = dplyr::full_join(load_data("tcga_clinical"), load_data("tcga_surv"), by
    = "sample"),
  opt_pancan = .opt_pancan
)

tcga_surv_plot(
  data,
  time = "time",
  status = "status",
  cutoff_mode = c("Auto", "Custom"),
  cutpoint = c(50, 50),
  cnv_type = c("Duplicated", "Normal", "Deleted"),
  profile = c("mRNA", "miRNA", "methylation", "transcript", "protein", "mutation", "cnv"),
  palette = "aaas",
  ...
)

Arguments

Value

a data.frame or a plot.

Examples

## Not run: 
# 1. get data
data <- tcga_surv_get("TP53")
# 2. filter data (optional)

# 3. show K-M plot
tcga_surv_plot(data, time = "DSS.time", status = "DSS")

## End(Not run)

Toil Hub: TCGA Clinical Data

Description

See tcga_surv for TCGA survival data.

Format

A data.frame

Source

Generate from data-raw

Examples

data("tcga_clinical")

Toil Hub: Cleaned TCGA Clinical Data for grouping

Description

See tcga_surv for TCGA survival data.

Format

A data.frame

Source

Generate from data-raw

Examples

data("tcga_clinical_fine")

TCGA: Genome Instability Data

Description

TCGA: Genome Instability Data

Format

A data.frame

Source

https://gdc.cancer.gov/about-data/publications/PanCanStemness-2018

Examples

data("tcga_genome_instability")

Toil Hub: Merged TCGA GTEx Selected Phenotype

Description

Toil Hub: Merged TCGA GTEx Selected Phenotype

Format

A data.frame

Examples

data("tcga_gtex")

TCGA: Purity Data

Description

TCGA: Purity Data

Format

A data.frame

Source

https://www.nature.com/articles/ncomms9971#Sec14

Examples

data("tcga_purity")

TCGA Subtype Data

Description

TCGA Subtype Data

Format

A data.frame

Source

UCSC Xena.

Examples

data("tcga_subtypes")

Toil Hub: TCGA Survival Data

Description

Toil Hub: TCGA Survival Data

Format

A data.frame

Source

Generate from data-raw

Examples

data("tcga_surv")

TCGA: TMB (Tumor Mutation Burden) Data

Description

TCGA: TMB (Tumor Mutation Burden) Data

Format

A data.frame

Source

https://gdc.cancer.gov/about-data/publications/panimmune

Examples

data("tcga_tmb")

Toil Hub: TCGA TARGET GTEX Selected Phenotype

Description

Toil Hub: TCGA TARGET GTEX Selected Phenotype

Format

A data.frame

Source

Generate from data-raw

Examples

data("toil_info")

Visualize CCLE Gene Expression Correlation

Description

Visualize CCLE Gene Expression Correlation

Usage

vis_ccle_gene_cor(
  Gene1 = "CSF1R",
  Gene2 = "JAK3",
  data_type1 = "mRNA",
  data_type2 = "mRNA",
  cor_method = "spearman",
  use_log_x = FALSE,
  use_log_y = FALSE,
  use_regline = TRUE,
  SitePrimary = "prostate",
  use_all = FALSE,
  alpha = 0.5,
  color = "#000000",
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Visualize CCLE Gene Expression

Description

Visualize CCLE Gene Expression

Usage

vis_ccle_tpm(
  Gene = "TP53",
  data_type = "mRNA",
  use_log = FALSE,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Visualize the distribution difference of samples after dimension reduction analysis

Description

Visualize the distribution difference of samples after dimension reduction analysis

Usage

vis_dim_dist(
  ids = c("TP53", "KRAS", "PTEN", "MDM2", "CDKN1A"),
  data_type = "mRNA",
  group_info = NULL,
  DR_method = c("PCA", "UMAP", "tSNE"),
  palette = "Set1",
  add_margin = NULL,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object or rawdata list

Examples

## Not run: 
group_info = tcga_clinical_fine %>% 
  dplyr::filter(Cancer=="BRCA") %>% 
  dplyr::select(Sample, Code) %>% 
  dplyr::rename(Group=Code)

vis_dim_dist(
  ids = c("TP53", "KRAS", "PTEN", "MDM2", "CDKN1A"),
  group_info = group_info
)


## End(Not run)

Heatmap for Correlation between Gene and Tumor Immune Infiltration (TIL)

Description

Heatmap for Correlation between Gene and Tumor Immune Infiltration (TIL)

Usage

vis_gene_TIL_cor(
  Gene = "TP53",
  cor_method = "spearman",
  data_type = "mRNA",
  sig = c("B cell_TIMER", "T cell CD4+_TIMER", "T cell CD8+_TIMER", "Neutrophil_TIMER",
    "Macrophage_TIMER", "Myeloid dendritic cell_TIMER"),
  Plot = "TRUE",
  opt_pancan = .opt_pancan
)

Arguments

Examples

## Not run: 
p <- vis_gene_TIL_cor(Gene = "TP53")

## End(Not run)

Visualize Gene-Gene Correlation in TCGA

Description

Visualize Gene-Gene Correlation in TCGA

Usage

vis_gene_cor(
  Gene1 = "CSF1R",
  Gene2 = "JAK3",
  data_type1 = "mRNA",
  data_type2 = "mRNA",
  use_regline = TRUE,
  purity_adj = TRUE,
  alpha = 0.5,
  color = "#000000",
  filter_tumor = TRUE,
  opt_pancan = .opt_pancan
)

Arguments

Visualize Gene-Gene Correlation in a TCGA Cancer Type

Description

Visualize Gene-Gene Correlation in a TCGA Cancer Type

Usage

vis_gene_cor_cancer(
  Gene1 = "CSF1R",
  Gene2 = "JAK3",
  data_type1 = "mRNA",
  data_type2 = "mRNA",
  purity_adj = TRUE,
  cancer_choose = "GBM",
  use_regline = TRUE,
  cor_method = "spearman",
  use_all = FALSE,
  alpha = 0.5,
  color = "#000000",
  opt_pancan = .opt_pancan
)

Arguments

Visualize Gene and Drug-Target Association with CCLE Data

Description

See analyze_gene_drug_response_asso for examples.

Usage

vis_gene_drug_response_asso(
  Gene = "TP53",
  x_axis_type = c("mean.diff", "median.diff"),
  output_form = c("plotly", "ggplot2")
)

Arguments

Value

plotly or ggplot2 object.

Visualize Gene and Drug Response Difference with CCLE Data

Description

See analyze_gene_drug_response_diff for examples.

Usage

vis_gene_drug_response_diff(
  Gene = "TP53",
  tissue = "lung",
  Show.P.label = TRUE,
  Method = "wilcox.test",
  values = c("#DF2020", "#DDDF21"),
  alpha = 0.5
)

Arguments

Value

a ggplot object.

Heatmap for Correlation between Gene and Immune Signatures

Description

Heatmap for Correlation between Gene and Immune Signatures

Usage

vis_gene_immune_cor(
  Gene = "TP53",
  cor_method = "spearman",
  data_type = "mRNA",
  Immune_sig_type = "Cibersort",
  Plot = "TRUE",
  opt_pancan = .opt_pancan
)

Arguments

Examples

## Not run: 
p <- vis_gene_immune_cor(Gene = "TP53")

## End(Not run)

Visualize Correlation between Gene and MSI (Microsatellite instability)

Description

Visualize Correlation between Gene and MSI (Microsatellite instability)

Usage

vis_gene_msi_cor(
  Gene = "TP53",
  cor_method = "spearman",
  data_type = "mRNA",
  Plot = "TRUE",
  opt_pancan = .opt_pancan
)

Arguments

Examples

## Not run: 
p <- vis_gene_msi_cor(Gene = "TP53")

## End(Not run)

Visualize Correlation between Gene and Pathway signature Score

Description

Visualize Correlation between Gene and Pathway signature Score

Usage

vis_gene_pw_cor(
  Gene = "TP53",
  data_type = "mRNA",
  pw_name = "HALLMARK_ADIPOGENESIS",
  cancer_choose = "GBM",
  use_regline = TRUE,
  cor_method = "spearman",
  use_all = FALSE,
  alpha = 0.5,
  color = "#000000",
  filter_tumor = TRUE,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object or dataframe

Examples

## Not run: 
vis_gene_pw_cor(Gene = "TP53", data_type = "mRNA", 
                pw_name = "HALLMARK_ADIPOGENESIS",
                cancer_choose = "BRCA")

## End(Not run)

Visualize Correlation between Gene and Tumor Stemness

Description

Visualize Correlation between Gene and Tumor Stemness

Usage

vis_gene_stemness_cor(
  Gene = "TP53",
  cor_method = "spearman",
  data_type = "mRNA",
  Plot = "TRUE",
  opt_pancan = .opt_pancan
)

Arguments

Examples

## Not run: 
p <- vis_gene_stemness_cor(Gene = "TP53")
p

## End(Not run)
## To generate a radar plot, uncomment the following code
# pdata <- p$data %>%
#   dplyr::mutate(cor = round(cor, digits = 3), p.value = round(p.value, digits = 3))
#
# df <- pdata %>%
#   select(cor, cancer) %>%
#   pivot_wider(names_from = cancer, values_from = cor)
#
# ggradar::ggradar(
#   df[1, ],
#   font.radar = "sans",
#   values.radar = c("-1", "0", "1"),
#   grid.min = -1, grid.mid = 0, grid.max = 1,
#   # Background and grid lines
#   background.circle.colour = "white",
#   gridline.mid.colour = "grey",
#   # Polygons
#   group.line.width = 1,
#   group.point.size = 3,
#   group.colours = "#00AFBB") +
#   theme(plot.title = element_text(hjust = .5))

Visualize Correlation between Gene and TMB (Tumor Mutation Burden)

Description

Visualize Correlation between Gene and TMB (Tumor Mutation Burden)

Usage

vis_gene_tmb_cor(
  Gene = "TP53",
  cor_method = "spearman",
  data_type = "mRNA",
  Plot = "TRUE",
  opt_pancan = .opt_pancan
)

Arguments

Examples

## Not run: 
p <- vis_gene_tmb_cor(Gene = "TP53")

## End(Not run)

Visualize Identifier-Identifier Correlation

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_cor(
  dataset1,
  id1,
  dataset2,
  id2,
  samples = NULL,
  use_ggstats = FALSE,
  use_simple_axis_label = TRUE,
  line_color = "blue",
  alpha = 0.5,
  ...
)

Arguments

Value

a (gg)plot object.

Examples

## Not run: 
dataset <- "TcgaTargetGtex_rsem_isoform_tpm"
id1 <- "TP53"
id2 <- "KRAS"
vis_identifier_cor(dataset, id1, dataset, id2)

samples <- c(
  "TCGA-D5-5538-01", "TCGA-VM-A8C8-01",
  "TCGA-ZN-A9VQ-01", "TCGA-EE-A17X-06",
  "TCGA-05-4420-01"
)
vis_identifier_cor(dataset, id1, dataset, id2, samples)

dataset1 <- "TCGA-BLCA.htseq_counts.tsv"
dataset2 <- "TCGA-BLCA.gistic.tsv"
id1 <- "TP53"
id2 <- "KRAS"
vis_identifier_cor(dataset1, id1, dataset2, id2)

## End(Not run)

Visualize the distribution difference of samples after Molecule Identifier dimension reduction analysis

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_dim_dist(
  dataset = NULL,
  ids = NULL,
  grp_df,
  samples = NULL,
  return.data = FALSE,
  DR_method = c("PCA", "UMAP", "tSNE"),
  add_margin = NULL,
  palette = "Set1"
)

Arguments

Value

a ggplot object.

Examples

library(UCSCXenaTools)
expr_dataset <- "TCGA.LUAD.sampleMap/HiSeqV2_percentile"
ids = c("TP53", "KRAS", "PTEN", "MDM2", "CDKN1A")

cli_dataset <- "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
cli_df <- XenaGenerate(
  subset = XenaDatasets == cli_dataset
) %>%
  XenaQuery() %>%
  XenaDownload() %>%
  XenaPrepare()
grp_df = cli_df[, c("sampleID", "gender")]
vis_identifier_dim_dist(expr_dataset, ids, grp_df, DR_method="PCA")

Visualize Comparison of an Molecule Identifier between Groups

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_grp_comparison(
  dataset = NULL,
  id = NULL,
  grp_df,
  samples = NULL,
  fun_type = c("betweenstats", "withinstats"),
  type = c("parametric", "nonparametric", "robust", "bayes"),
  pairwise.comparisons = TRUE,
  p.adjust.method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  ggtheme = cowplot::theme_cowplot(),
  ...
)

Arguments

Value

a (gg)plot object.

Examples

## Not run: 
library(UCSCXenaTools)
expr_dataset <- "TCGA.LUAD.sampleMap/HiSeqV2_percentile"
cli_dataset <- "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
id <- "TP53"
cli_df <- XenaGenerate(
  subset = XenaDatasets == "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
) %>%
  XenaQuery() %>%
  XenaDownload() %>%
  XenaPrepare()

# group data.frame with 2 columns
vis_identifier_grp_comparison(expr_dataset, id, cli_df[, c("sampleID", "gender")])
# group data.frame with 3 columns
vis_identifier_grp_comparison(
  expr_dataset, id,
  cli_df[, c("sampleID", "pathologic_M", "gender")] %>%
    dplyr::filter(pathologic_M %in% c("M0", "MX"))
)

# When not use the value of `identifier` from `dataset`
vis_identifier_grp_comparison(grp_df = cli_df[, c(1, 2, 71)])
vis_identifier_grp_comparison(grp_df = cli_df[, c(1, 2, 71, 111)])

## End(Not run)

Visualize Identifier Group Survival Difference

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_grp_surv(
  dataset = NULL,
  id = NULL,
  surv_df,
  samples = NULL,
  cutoff_mode = c("Auto", "Custom", "None"),
  cutpoint = c(50, 50),
  palette = "aaas",
  ...
)

Arguments

Value

a (gg)plot object.

Examples

## Not run: 
library(UCSCXenaTools)
expr_dataset <- "TCGA.LUAD.sampleMap/HiSeqV2_percentile"
cli_dataset <- "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
id <- "KRAS"
cli_df <- XenaGenerate(
  subset = XenaDatasets == "TCGA.LUAD.sampleMap/LUAD_clinicalMatrix"
) %>%
  XenaQuery() %>%
  XenaDownload() %>%
  XenaPrepare()

# Use individual survival data
surv_df1 <- cli_df[, c("sampleID", "ABSOLUTE_Ploidy", "days_to_death", "vital_status")]
surv_df1$vital_status <- ifelse(surv_df1$vital_status == "DECEASED", 1, 0)
vis_identifier_grp_surv(surv_df = surv_df1)

# Use both dataset argument and vis_identifier_grp_surv(surv_df = surv_df1)
surv_df2 <- surv_df1[, c(1, 3, 4)]
vis_identifier_grp_surv(expr_dataset, id, surv_df = surv_df2)
vis_identifier_grp_surv(expr_dataset, id,
  surv_df = surv_df2,
  cutoff_mode = "Custom", cutpoint = c(25, 75)
)

## End(Not run)

Visualize Correlation for Multiple Identifiers

Description

NOTE: the dataset must be dense matrix in UCSC Xena data hubs.

Usage

vis_identifier_multi_cor(
  dataset,
  ids,
  samples = NULL,
  matrix.type = c("full", "upper", "lower"),
  type = c("parametric", "nonparametric", "robust", "bayes"),
  partial = FALSE,
  sig.level = 0.05,
  p.adjust.method = c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr",
    "none"),
  color_low = "#E69F00",
  color_high = "#009E73",
  ...
)

Arguments

Value

a (gg)plot object.

Examples

## Not run: 
dataset <- "TcgaTargetGtex_rsem_isoform_tpm"
ids <- c("TP53", "KRAS", "PTEN")
vis_identifier_multi_cor(dataset, ids)

## End(Not run)

Visualize Single Gene Expression in Anatomy Location

Description

Visualize Single Gene Expression in Anatomy Location

Usage

vis_pancan_anatomy(
  Gene = "TP53",
  Gender = c("Female", "Male"),
  data_type = "mRNA",
  option = "D",
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Visualize molecular profile in PCAWG

Description

Visualize molecular profile in PCAWG

Usage

vis_pcawg_dist(
  Gene = "TP53",
  Mode = c("Boxplot", "Violinplot"),
  data_type = "mRNA",
  Show.P.value = TRUE,
  Show.P.label = TRUE,
  Method = c("wilcox.test", "t.test"),
  values = c("#DF2020", "#DDDF21"),
  draw_quantiles = c(0.25, 0.5, 0.75),
  trim = TRUE,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Examples

## Not run: 
p <- vis_pcawg_dist(Gene = "TP53")

## End(Not run)

Visualize Gene-Gene Correlation in TCGA

Description

Visualize Gene-Gene Correlation in TCGA

Usage

vis_pcawg_gene_cor(
  Gene1 = "CSF1R",
  Gene2 = "JAK3",
  data_type1 = "mRNA",
  data_type2 = "mRNA",
  cor_method = "spearman",
  purity_adj = TRUE,
  use_log_x = FALSE,
  use_log_y = FALSE,
  use_regline = TRUE,
  dcc_project_code_choose = "BLCA-US",
  use_all = FALSE,
  filter_tumor = TRUE,
  alpha = 0.5,
  color = "#000000",
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Visualize Single Gene Univariable Cox Result in PCAWG

Description

Visualize Single Gene Univariable Cox Result in PCAWG

Usage

vis_pcawg_unicox_tree(
  Gene = "TP53",
  measure = "OS",
  data_type = "mRNA",
  threshold = 0.5,
  values = c("grey", "#E31A1C", "#377DB8"),
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Examples

## Not run: 
p <- vis_pcawg_unicox_tree(Gene = "TP53")

## End(Not run)

Visualize molecular profile difference between mutation and wild status of queried gene

Description

Visualize molecular profile difference between mutation and wild status of queried gene

Usage

vis_toil_Mut(
  mut_Gene = "TP53",
  Gene = NULL,
  data_type = NULL,
  Mode = c("Boxplot", "Violinplot"),
  Show.P.value = TRUE,
  Show.P.label = TRUE,
  Method = c("wilcox.test", "t.test"),
  values = c("#DF2020", "#DDDF21"),
  draw_quantiles = c(0.25, 0.5, 0.75),
  trim = TRUE,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object or a tibble data.frame

Examples

## Not run: 
p <- vis_toil_Mut(mut_Gene = "TP53")
p <- vis_toil_Mut(mut_Gene = "TP53", Gene = "TNF")
p <- vis_toil_Mut(mut_Gene = "TP53", Gene = "hsa-let-7d-3p", data_type = "miRNA")

## End(Not run)

Visualize molecular profile difference between mutation and wild status of queried gene in Single Cancer Type

Description

Visualize molecular profile difference between mutation and wild status of queried gene in Single Cancer Type

Usage

vis_toil_Mut_cancer(
  mut_Gene = "TP53",
  Gene = NULL,
  data_type = NULL,
  Mode = c("Dotplot", "Violinplot"),
  Show.P.value = TRUE,
  Show.P.label = TRUE,
  Method = c("wilcox.test", "t.test"),
  values = c("#DF2020", "#DDDF21"),
  draw_quantiles = c(0.25, 0.5, 0.75),
  trim = TRUE,
  Cancer = "ACC",
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object or a tibble data.frame.

Visualize Pan-cancer TPM (tumor (TCGA) vs Normal (TCGA & GTEx))

Description

Visualize Pan-cancer TPM (tumor (TCGA) vs Normal (TCGA & GTEx))

Usage

vis_toil_TvsN(
  Gene = "TP53",
  Mode = c("Boxplot", "Violinplot"),
  data_type = "mRNA",
  Show.P.value = TRUE,
  Show.P.label = TRUE,
  Method = c("wilcox.test", "t.test"),
  values = c("#DF2020", "#DDDF21"),
  TCGA.only = FALSE,
  draw_quantiles = c(0.25, 0.5, 0.75),
  trim = TRUE,
  include.Tumor.only = FALSE,
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Examples

## Not run: 
p <- vis_toil_TvsN(Gene = "TP53", Mode = "Violinplot", Show.P.value = FALSE, Show.P.label = FALSE)
p <- vis_toil_TvsN(Gene = "TP53", Mode = "Boxplot", Show.P.value = FALSE, Show.P.label = FALSE)

## End(Not run)

Visualize Gene TPM in Single Cancer Type (Tumor (TCGA) vs Normal (TCGA & GTEx))

Description

Visualize Gene TPM in Single Cancer Type (Tumor (TCGA) vs Normal (TCGA & GTEx))

Usage

vis_toil_TvsN_cancer(
  Gene = "TP53",
  Mode = c("Violinplot", "Dotplot"),
  data_type = "mRNA",
  Show.P.value = FALSE,
  Show.P.label = FALSE,
  Method = "wilcox.test",
  values = c("#DF2020", "#DDDF21"),
  TCGA.only = FALSE,
  Cancer = "ACC",
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object.

Visualize Single Gene Univariable Cox Result from Toil Data Hub

Description

Visualize Single Gene Univariable Cox Result from Toil Data Hub

Usage

vis_unicox_tree(
  Gene = "TP53",
  measure = "OS",
  data_type = "mRNA",
  threshold = 0.5,
  values = c("grey", "#E31A1C", "#377DB8"),
  opt_pancan = .opt_pancan
)

Arguments

Value

a ggplot object

Examples

## Not run: 
p <- vis_unicox_tree(Gene = "TP53")

## End(Not run)