Package 'UCSCXenaShiny'

Title: Interactive Analysis of UCSC Xena Data
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.
Authors: Shixiang Wang [aut, cre] , Shensuo Li [aut], Yi Xiong [aut] , Longfei Zhao [aut] , Kai Gu [aut] , Yin Li [aut], Fei Zhao [aut]
Maintainer: Shixiang Wang <[email protected]>
License: GPL (>= 3)
Version: 2.2.0
Built: 2024-08-22 13:30:05 UTC
Source: https://github.com/openbiox/UCSCXenaShiny

Help Index

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.

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

gene_list

a gene symbol list.
combine

if TRUE, combine the expression of gene list as a gene signature.

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

gene_list

a gene symbol list.
drug

a drug name. Check examples.
tissue

a tissue name. Check examples.
combine

if TRUE, combine the expression of gene list as a gene signature.
cutpoint

cut point (in percent) for High and Low group, default is c(50, 50).

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

runMode

default is 'client' for personal user, set it to 'server' for running on server.
port

The TCP port that the application should listen on. If the port is not specified, and the shiny.port option is set (with options(shiny.port = XX)), then that port will be used. Otherwise, use a random port between 3000:8000, excluding ports that are blocked by Google Chrome for being considered unsafe: 3659, 4045, 5060, 5061, 6000, 6566, 6665:6669 and 6697. Up to twenty random ports will be tried.

Examples

## Not run: 
app_run()

## End(Not run)

Run UCSC Xena Shiny App with specifc content

Description

Run UCSC Xena Shiny App with specifc content

Usage

app_run2(
  runMode = "client",
  port = getOption("shiny.port"),
  content = c("a", "s", "q", "p", "d")
)

Arguments

runMode

default is 'client' for personal user, set it to 'server' for running on server.
port

The TCP port that the application should listen on. If the port is not specified, and the shiny.port option is set (with options(shiny.port = XX)), then that port will be used. Otherwise, use a random port between 3000:8000, excluding ports that are blocked by Google Chrome for being considered unsafe: 3659, 4045, 5060, 5061, 6000, 6566, 6665:6669 and 6697. Up to twenty random ports will be tried.
content

Modules to enable.

  • a: all modules

  • s: only loading basic modules;

  • q: add tpc (TCGA, PCAWG, CCLE) modules

  • p: add tpc pipelines

  • d: add pharmcogenomics modules

Examples

## Not run: 
app_run2(content = "s")

## End(Not run)

Show Available Hosts

Description

Show Available Hosts

Usage

available_hosts()

Value

hosts

Examples

available_hosts()

Prepare Mut/CNV data of pathways given specific samples

Description

Prepare Mut/CNV data of pathways given specific samples

Usage

calc_pw_mut_cnv(tumor_samples = NULL)

Arguments

tumor_samples

Select specific tumor samples. Default NULL, indicating all tumor samples.

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

data

a data.frame containing variables
split

whether perform correlation grouped by a variable, default is 'FALSE'
split_var

a character, the group variable
var1

a character, the first variable in correlation
var2

a character, the second variable in correlation
cor_method

method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall"
adjust_method

What adjustment for multiple tests should be used? ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")
use

use="pairwise" will do pairwise deletion of cases. use="complete" will select just complete cases
sig_label

whether add symbal of significance. P < 0.001,⁠***⁠; P < 0.01,⁠**⁠; P < 0.05,*; P >=0.05,""
verbose

if TRUE, print extra info.

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

data

a data.frame containing variables
var1

a character, the first variable in correlation
var2

a character, the second variable in correlation
split

whether perform correlation grouped by a variable, default is 'FALSE'
split_var

a character, the group variable
cor_method

method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall"
adjust_method

What adjustment for multiple tests should be used? ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")
use

use="pairwise" will do pairwise deletion of cases. use="complete" will select just complete cases
sig_label

whether add symbal of significance. P < 0.001,⁠***⁠; P < 0.01,⁠**⁠; P < 0.05,*; P >=0.05,""
parallel

if TRUE, do parallel computation by furrr package.
verbose

if TRUE, print extra info.

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

data

a data.frame containing variables
split

whether perform correlation grouped by a variable, default is 'FALSE'
split_var

a character, the group variable
var1

a character, the first variable in correlation
var2

a character, the second variable in correlation
var3

a character or ⁠character vector⁠, the third variable in correlation
cor_method

method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall"
sig_label

whether add symbal of significance. P < 0.001,""; P < 0.01,""; P < 0.05,""; P >=0.05,""
...

other arguments passed to methods

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

identifier

a length-1 character representing a gene symbol, ensembl gene id, or probe id. Gene symbol is highly recommended.
norm

the normalization method.
subtype

a length-1 chracter representing a regular expression for matching DataSubtype column of UCSCXenaTools::XenaData.
dataset

a length-1 chracter representing a regular expression for matching XenaDatasets of UCSCXenaTools::XenaData.
host

a character vector representing host name(s), e.g. "toilHub".
samples

a character vector representing samples want to be returned.
...

other parameters.
gistic2

if TRUE (default), use GISTIC2 data.
use_thresholded_data

if TRUE, use GISTIC2-thresholded value.
type

methylation type, one of "450K" and "27K". for function get_pcawg_promoter_value, it can be one of "raw", "relative", "outlier".
rule_out

methylation sites to rule out before analyzing.
aggr

apporaches to aggregate the methylation data, default is 'NA', in such case, a mean value is obtained for gene-level methylation. Allowed value is one of c("NA", "mean", "Q0", "Q25", "Q50", "Q75", "Q100"). Here, Q50 is median.

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

x

a data.frame with many columns.
keep_sam_cols

if TRUE (default), keep columns with pattern 'sample', 'patient', etc.
return_idx

if TRUE (default), return index of 5 (at most) columns, it is useful in Shiny.

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

name

a dataset name. Could be one of

Builtin datasets:

  • ccle_absolute: CCLE ABSOLUTE result.

  • ccle_info: CCLE information.

  • ccle_info_fine: cleaned CCLE information for TPC analysis.

  • pcawg_info: PCAWG information.

  • pcawg_info_fine: cleaned PCAWG information for TPC analysis.

  • pcawg_purity: PCAWG tumor purity, ploidy and WGD data.

  • tcga_clinical: TCGA clinical data.

  • tcga_clinical_fine: cleaned TCGA information for TPC analysis.

  • tcga_genome_instability: TCGA genome instability data.

  • tcga_gtex: TCGA and GTEX sample info.

  • tcga_purity: TCGA tumor purity data.

  • tcga_subtypes: TCGA subtypes data.

  • tcga_surv: TCGA survival data.

  • TCGA.organ: TCGA organ data.

  • toil_info: Toil hub information.

Remote datasets stored in Zenodo:

  • pcawg_promoter_id: PCAWG promoter identifiers.

  • transcript_identifier: Common transcript identifiers.

  • ccle_expr_and_drug_response: CCLE expression and drug response data.

  • ccle_drug_response_extend: CCLE drug response extended data.

  • pancan_MSI: Pan-cancer MSI data.

  • tcga_chr_alteration: TCGA chromosome alteration data.

  • tcga_MSI: TCGA MSI data.

  • tcga_pan_immune_signature: TCGA pan-cancer immune signature.

  • tcga_stemness: TCGA tumor stemness data.

  • tcga_TIL: TCGA TIL data.

  • tcga_PW: ssGSEA scores of HALLMARK, KEGG, IOBR terms for TCGA samples.

  • tcga_PW_meta: metadata annotation for HALLMARK, KEGG, IOBR terms.

  • tcga_tmb: TCGA TMB data.

  • tcga_armcalls: TCGA arm alteration calls and Aneuploidy data.

  • tcga_dna_repair: TCGA DNA repair data.

  • pancancer_conserved_immune_subtype: Pan-cancer conserved immune subtypes.

  • pcawg_TIL: PCAWG TIL data.

  • pcawg_PW: ssGSEA scores of HALLMARK, KEGG, IOBR terms for PCAWG samples.

  • ...

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

molecule

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

data type. Can be one of "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
out_dir

path to save analysis result and report, default is '.'
out_report

logical value wheather to generate html report

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

L1

level 1 main datatype
L2

level 2 sub datatype
L3

level 3 identifier
database

one of c("toil","pcawg","ccle")
tpc_value_nonomics

non-omics matrix data of one database
opt_pancan

molecular datasets parameters
custom_metadata

user customized metadata

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

dataset

a UCSC Xena dataset in dense matrix format (rows are features (e.g., gene, cell line) and columns are samples).
molecule

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN"). NOTE, when a signature is specified, a space must exist in the input.
host

a UCSC Xena host, default is NULL, auto-detect from the dataset.

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

molecule

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

data type. Can be one of "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
database

database, either 'toil' for TCGA TARGET GTEx, or 'ccle' for CCLE.
reset_id

if not NULL, set the specified variable at parent frame to "Signature".
opt_pancan

other extra parameters passing to the underlying functions.

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

database

one of c("toil","pcawg","ccle")
cancer

select cancer cohort(s)
custom

upload custom phenotype data
group

target group names
filter_by

filter samples by one or multiple criterion
filter_id

directly filter samples by provided sample ids
merge_by

merge the target group for main categories
merge_quantile

whether to merge numerical variable by percentiles
return_all

return the all phenotype data

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

identifier

a length-1 character representing a gene symbol, ensembl gene id, or probe id. Gene symbol is highly recommended.

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

item

a molecular identifier, can be gene symbol (common cases), protein symbol, etc.
TCGA_cohort

a TCGA cohort, e.g. "LUAD" (default), "LUSC", "ACC".
profile

a molecular profile. Option can be one of "mRNA" (default), "miRNA", "methylation", "transcript", "protein", "mutation", "cnv".
TCGA_cli_data

a data.frame containing TCGA clinical data. Default use pre-compiled TCGA clinical data in this package.
opt_pancan

specify one dataset for some molercular profiles
data

a subset of result from tcga_surv_get().
time

the column name for "time".
status

the column name for "status".
cutoff_mode

mode for grouping samples, can be "Auto" (default) or "Custom".
cutpoint

cut point (in percent) for "Custom" mode, default is c(50, 50).
cnv_type

only used when profile is "cnv", can select from c("Duplicated", "Normal", "Deleted").
palette

color palette, can be "hue", "grey", "RdBu", "Blues", "npg", "aaas", etc. More see ?survminer::ggsurvplot.
...

other parameters passing to survminer::ggsurvplot

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")

TCGA: Organ Data

Description

TCGA: Organ Data

Format

A data.frame

Examples

data("TCGA.organ")

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

Gene1

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Gene2

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type1

choose gene profile type for the first gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
data_type2

choose gene profile type for the second gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
cor_method

correlation method
use_log_x

if TRUE, log X values.
use_log_y

if TRUE, log Y values.
use_regline

if TRUE, add regression line.
SitePrimary

select cell line origin tissue.
use_all

use all sample, default FALSE.
alpha

dot alpha.
color

dot color.
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

support genomic profile for CCLE, currently "mRNA", "protein","cnv" are supported
use_log

if TRUE, log values.
opt_pancan

specify one dataset for some molercular profiles

Value

a ggplot object

Visualize the distribution difference of samples after dimensionality reduction analysis

Description

Visualize the distribution difference of samples after dimensionality 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

ids

molecular identifiers (>=3)
data_type

molecular types, refer to query_pancan_value() function
group_info

two-column grouping information with names 'Sample','Group'
DR_method

the dimensionality reduction method
palette

the color setting of RColorBrewer
add_margin

the marginal plot (NULL, "density", "boxplot")
opt_pancan

specify one dataset for some molercular profiles

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)

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

Gene1

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Gene2

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type1

choose gene profile type for the first gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
data_type2

choose gene profile type for the second gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
use_regline

if TRUE, add regression line.
purity_adj

whether performing partial correlation adjusted by purity
alpha

dot alpha.
color

dot color.
filter_tumor

whether use tumor sample only, default TRUE
opt_pancan

specify one dataset for some molercular profiles

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

Gene1

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Gene2

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type1

choose gene profile type for the first gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
data_type2

choose gene profile type for the second gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
purity_adj

whether performing partial correlation adjusted by purity
cancer_choose

TCGA cohort name, e.g. "ACC".
use_regline

if TRUE, add regression line.
cor_method

correlation method.
use_all

use all sample, default FALSE.
alpha

dot alpha.
color

dot color.
opt_pancan

specify one dataset for some molercular profiles

Visualize cross-omics of one gene among pan-cancers

Description

Visualize cross-omics of one gene among pan-cancers

Usage

vis_gene_cross_omics(
  gene = "TP53",
  tumor_projects = NULL,
  tumor_samples = NULL,
  n_trans = 5,
  n_methy = 5,
  seed = 42,
  pval_mrna = c(0.05, 0.01, 0.001),
  return_list = FALSE
)

Arguments

gene

a gene symbol identifier (e.g., "TP53")
tumor_projects

Select specific TCGA projects. Default NULL, indicating all TCGA projects.
tumor_samples

Select specific tumor samples. Default NULL, indicating all tumor samples.
n_trans

The number of sampling transcripts or specific transcript identifiers.
n_methy

The number of sampling CpG sites or specific CpG identifiers.
seed

The seed of sampling.
pval_mrna

The P value thresholds
return_list

TRUE returns a list including plot object and data. FALSE just returns plot.

Value

funkyheatmap

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
x_axis_type

set the value type for X axis.
output_form

plotly or ggplot2.

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
tissue

select cell line origin tissue.
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill tumor or normal
alpha

set alpha for dots.

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
cor_method

correlation method
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Immune_sig_type

quantification method, default is "Cibersort"
Plot

output the plot directly, default 'TRUE'
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
cor_method

correlation method
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Plot

output the plot directly, default 'TRUE'
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
pw_name

the queried Pathway name, see the supported pathway from 'load("toil_sig_score")'default is NULL
cancer_choose

select cancer cohort(s)
use_regline

if TRUE, add regression line.
cor_method

select correlation coefficient (pearson/spearman)
use_all

use all sample, default FALSE.
alpha

dot alpha.
color

dot color.
filter_tumor

whether use tumor sample only, default TRUE
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
cor_method

correlation method
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Plot

output the plot directly, default 'TRUE'
opt_pancan

specify one dataset for some molercular profiles

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))

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
cor_method

correlation method
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
sig

Immune Signature, default: result from TIMER
Plot

output the plot directly, default 'TRUE'
opt_pancan

specify one dataset for some molercular profiles

Examples

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

## End(Not run)

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
cor_method

correlation method
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Plot

output the plot directly, default 'TRUE'
opt_pancan

specify one dataset for some molercular profiles

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

dataset1

the dataset to obtain id1.
id1

the first molecule identifier.
dataset2

the dataset to obtain id2.
id2

the second molecule identifier.
samples

default is NULL, can be common sample names for two datasets.
use_ggstats

if TRUE, use ggstatsplot package for plotting.
use_simple_axis_label

if TRUE (default), use simple axis labels. Otherwise, data subtype will be labeled.
line_color

set the color for regression line.
alpha

set the alpha for dots.
...

other parameters passing to ggscatter.

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 dimensionality 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

dataset

the dataset to obtain identifiers.
ids

the molecule identifiers.
grp_df

When dataset and id are all not NULL, it should be a data.frame with 2 columns.

  • The first column refers to sample ID.

  • The second column refers to groups indicated in axis X.

samples

default is NULL, can be common sample names for two datasets.
return.data

whether to reture the raw meta/matrix data (list) instead of plot
DR_method

the dimensionality reduction method
add_margin

the marginal plot (NULL, "density", "boxplot")
palette

the color setting of RColorBrewer

Value

a ggplot object.

Examples

# 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

dataset

the dataset to obtain identifiers.
id

the molecule identifier.
grp_df

When dataset and id are all not NULL, it should be a data.frame with 2 or 3 columns.

  • The first column refers to sample ID.

  • The second column refers to groups indicated in axis X.

  • The third column is optional, which indicates facet variable. When any of dataset and id is NULL, it should be a data.frame with 3 or 4 columns.

  • The first column refers to sample ID.

  • The second column refers to values indicated in axis Y.

  • The third column refers to groups indicated in axis X.

  • The fourth column is optional, which indicates facet variable.

samples

default is NULL, can be common sample names for two datasets.
fun_type

select the function to compare groups.
type

A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.
pairwise.comparisons

whether pairwise comparison
p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
ggtheme

A {ggplot2} theme. Default value is ggstatsplot::theme_ggstatsplot(). Any of the {ggplot2} themes (e.g., theme_bw()), or themes from extension packages are allowed (e.g., ggthemes::theme_fivethirtyeight(), hrbrthemes::theme_ipsum_ps(), etc.). But note that sometimes these themes will remove some of the details that {ggstatsplot} plots typically contains. For example, if relevant, ggbetweenstats() shows details about multiple comparison test as a label on the secondary Y-axis. Some themes (e.g. ggthemes::theme_fivethirtyeight()) will remove the secondary Y-axis and thus the details as well.
...

other parameters passing to ggstatsplot::ggbetweenstats or ggstatsplot::ggwithinstats.

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

dataset

the dataset to obtain identifiers.
id

the molecule identifier.
surv_df

a data.frame. The "time" should be in unit of "days".

  • If there are 3 columns, the names should be "sample", "time", "status".

  • If there are 4 columns, the names should be "sample", "value", "time", "status".

samples

default is NULL, can be common sample names for two datasets.
cutoff_mode

mode for grouping samples, can be "Auto" (default) or "Custom" or "None" (for groups have been prepared).
cutpoint

cut point (in percent) for "Custom" mode, default is c(50, 50).
palette

color palette, can be "hue", "grey", "RdBu", "Blues", "npg", "aaas", etc. More see ?survminer::ggsurvplot.
...

other parameters passing to survminer::ggsurvplot

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

dataset

the dataset to obtain identifiers.
ids

the molecule identifiers.
samples

default is NULL, can be common sample names for two datasets.
matrix.type

Character, "upper" (default), "lower", or "full", display full matrix, lower triangular or upper triangular matrix.
type

A character specifying the type of statistical approach:

  • "parametric"

  • "nonparametric"

  • "robust"

  • "bayes"

You can specify just the initial letter.
partial

Can be TRUE for partial correlations. For Bayesian partial correlations, "full" instead of pseudo-Bayesian partial correlations (i.e., Bayesian correlation based on frequentist partialization) are returned.
sig.level

Significance level (Default: 0.05). If the p-value in p-value matrix is bigger than sig.level, then the corresponding correlation coefficient is regarded as insignificant and flagged as such in the plot.
p.adjust.method

Adjustment method for p-values for multiple comparisons. Possible methods are: "holm" (default), "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none".
color_low

the color code for lower value mapping.
color_high

the color code for higher value mapping.
...

other parameters passing to ggstatsplot::ggcorrmat.

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Gender

a string, "Female" (default) or "Male".
data_type

choose gene profile type, including "mRNA","transcript","methylation","miRNA","protein","cnv"
option

A character string indicating the color map option to use. Eight options are available:

  • "magma" (or "A")

  • "inferno" (or "B")

  • "plasma" (or "C")

  • "viridis" (or "D")

  • "cividis" (or "E")

  • "rocket" (or "F")

  • "mako" (or "G")

  • "turbo" (or "H")

opt_pancan

specify one dataset for some molercular profiles

Value

a ggplot object

Visualize cross-omics of one pathway among pan-cancers

Description

Visualize cross-omics of one pathway among pan-cancers

Usage

vis_pathway_cross_omics(
  pw = "HALLMARK_ADIPOGENESIS",
  tumor_projects = NULL,
  tumor_samples = NULL,
  pval_mrna = c(0.05, 0.01, 0.001),
  return_list = FALSE
)

Arguments

pw

pathway name
tumor_projects

Select specific TCGA projects. Default NULL, indicating all TCGA projects.
tumor_samples

Select specific tumor samples. Default NULL, indicating all tumor samples.
pval_mrna

The P value thresholds
return_list

TRUE returns a list including plot object and data. FALSE just returns plot.

Value

funkyheatmap

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Mode

"Boxplot" or "Violinplot" to represent data
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Show.P.value

TRUE or FALSE whether to count P value
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill tumor or normal
draw_quantiles

draw quantiles for violinplot
trim

whether trim the violin
opt_pancan

specify one dataset for some molercular profiles

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

Gene1

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Gene2

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type1

choose gene profile type for the first gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
data_type2

choose gene profile type for the second gene, including "mRNA","transcript","methylation","miRNA","protein","cnv_gistic2"
cor_method

correlation method
purity_adj

whether performing partial correlation adjusted by purity
use_log_x

if TRUE, log X values.
use_log_y

if TRUE, log Y values.
use_regline

if TRUE, add regression line.
dcc_project_code_choose

select project code.
use_all

use all sample, default FALSE.
filter_tumor

whether use tumor sample only, default TRUE
alpha

dot alpha.
color

dot color.
opt_pancan

specify one dataset for some molercular profiles

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",
  use_optimal_cutoff = FALSE,
  values = c("grey", "#E31A1C", "#377DB8"),
  opt_pancan = .opt_pancan
)

Arguments

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
measure

a survival measure, e.g. "OS".
data_type

choose gene profile type, including "mRNA","transcript","methylation","miRNA","protein","cnv"
use_optimal_cutoff

use surv_cutpoint from survminer package for thresholding samples in each cancer type.
values

the color to fill tumor or normal
opt_pancan

specify one dataset for some molercular profiles

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

mut_Gene

the queried gene to determine grouping based on mutation and wild status
Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

choose gene profile type, including "mRNA", "transcript", "methylation", "miRNA".
Mode

choose one visualize mode to represent data
Show.P.value

TRUE or FALSE whether to count P value
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill mutation or wild status
draw_quantiles

draw quantiles for violinplot
trim

whether to trim the violin
opt_pancan

specify one dataset for some molercular profiles

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

mut_Gene

the queried gene to determine grouping based on mutation and wild status
Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
data_type

choose gene profile type, including "mRNA", "transcript", "methylation", "miRNA".
Mode

choose one visualize mode to represent data
Show.P.value

TRUE or FALSE whether to count P value
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill mutation or wild status
draw_quantiles

draw quantiles for violinplot
trim

whether to trim the violin
Cancer

select cancer cohort(s).
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Mode

"Boxplot" or "Violinplot" to represent data
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Show.P.value

TRUE or FALSE whether to count P value
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill tumor or normal
TCGA.only

include samples only from TCGA dataset
draw_quantiles

draw quantiles for violinplot
trim

whether trim the violin
include.Tumor.only

if TRUE, include "UVM" and "MESO" these two types with matched normals samples.
opt_pancan

specify one dataset for some molercular profiles

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

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
Mode

"Boxplot" or "Violinplot" to represent data
data_type

choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA".
Show.P.value

TRUE or FALSE whether to count P value
Show.P.label

TRUE or FALSE present p value with number or label *, ⁠**⁠, ⁠***⁠ and ⁠****⁠
Method

default method is wilcox.test
values

the color to fill tumor or normal
TCGA.only

include samples only from TCGA dataset
Cancer

select cancer cohort(s).
opt_pancan

specify one dataset for some molercular profiles

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",
  use_optimal_cutoff = FALSE,
  values = c("grey", "#E31A1C", "#377DB8"),
  opt_pancan = .opt_pancan
)

Arguments

Gene

a molecular identifier (e.g., "TP53") or a formula specifying genomic signature ("TP53 + 2 * KRAS - 1.3 * PTEN").
measure

a survival measure, e.g. "OS".
data_type

choose gene profile type, including "mRNA","transcript","methylation","miRNA","protein","cnv"
use_optimal_cutoff

use surv_cutpoint from survminer package for thresholding samples in each cancer type.
values

the color to fill tumor or normal
opt_pancan

specify one dataset for some molercular profiles

Value

a ggplot object

Examples

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

## End(Not run)