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] |
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 |
A default setting for pan-cancer studies
.opt_pancan
.opt_pancan
An object of class list
of length 16.
Analyze partial correlation of gene-drug association after controlling for tissue average expression.
analyze_gene_drug_response_asso(gene_list, combine = FALSE)
analyze_gene_drug_response_asso(gene_list, combine = FALSE)
gene_list |
a gene symbol list. |
combine |
if |
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.
## 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)
## 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
analyze_gene_drug_response_diff( gene_list, drug = "ALL", tissue = "ALL", combine = FALSE, cutpoint = c(50, 50) )
analyze_gene_drug_response_diff( gene_list, drug = "ALL", tissue = "ALL", combine = FALSE, cutpoint = c(50, 50) )
gene_list |
a gene symbol list. |
drug |
a drug name. Check examples. |
tissue |
a tissue name. Check examples. |
combine |
if |
cutpoint |
cut point (in percent) for High and Low group, default is |
a data.frame
.
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)
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
app_run(runMode = "client", port = getOption("shiny.port"))
app_run(runMode = "client", port = getOption("shiny.port"))
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
|
## Not run: app_run() ## End(Not run)
## Not run: app_run() ## End(Not run)
Run UCSC Xena Shiny App with specifc content
app_run2( runMode = "client", port = getOption("shiny.port"), content = c("a", "s", "q", "p", "d") )
app_run2( runMode = "client", port = getOption("shiny.port"), content = c("a", "s", "q", "p", "d") )
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
|
content |
Modules to enable.
|
## Not run: app_run2(content = "s") ## End(Not run)
## Not run: app_run2(content = "s") ## End(Not run)
Show Available Hosts
available_hosts()
available_hosts()
hosts
available_hosts()
available_hosts()
Prepare Mut/CNV data of pathways given specific samples
calc_pw_mut_cnv(tumor_samples = NULL)
calc_pw_mut_cnv(tumor_samples = NULL)
tumor_samples |
Select specific tumor samples. Default NULL, indicating all tumor samples. |
ABSOLUTE Result of CCLE Database
A data.frame
see "data_source" attribute.
data("ccle_absolute")
data("ccle_absolute")
Phenotype Info of CCLE Database
A data.frame
UCSC Xena.
data("ccle_info")
data("ccle_info")
Cleaned Phenotype Info of CCLE Database for grouping
A data.frame
UCSC Xena.
data("ccle_info_fine")
data("ccle_info_fine")
Run Correlation between Two Variables and Support Group by a Variable
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 )
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 )
data |
a |
split |
whether perform correlation grouped by a variable, default is 'FALSE' |
split_var |
a |
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, |
verbose |
if |
a data.frame
Yi Xiong
Run correlation between two variables in a batch mode and support group by a variable
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 )
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 )
data |
a |
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 |
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, |
parallel |
if |
verbose |
if |
a data.frame
Yi Xiong, Shixiang Wang
Run partial correlation
ezcor_partial_cor( data = NULL, split = FALSE, split_var = NULL, var1 = NULL, var2 = NULL, var3 = NULL, cor_method = "pearson", sig_label = TRUE, ... )
ezcor_partial_cor( data = NULL, split = FALSE, split_var = NULL, var1 = NULL, var2 = NULL, var3 = NULL, cor_method = "pearson", sig_label = TRUE, ... )
data |
a |
split |
whether perform correlation grouped by a variable, default is 'FALSE' |
split_var |
a |
var1 |
a |
var2 |
a |
var3 |
a |
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 |
a data.frame
Yi Xiong
ppcor::pcor.test()
which this function wraps.
Identifier includes gene/probe etc.
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") )
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") )
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
|
dataset |
a length-1 chracter representing a regular expression for matching
|
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 |
use_thresholded_data |
if |
type |
methylation type, one of "450K" and "27K".
for function |
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 |
a named vector or list
.
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
## 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)
## 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
keep_cat_cols(x, keep_sam_cols = TRUE, return_idx = TRUE)
keep_cat_cols(x, keep_sam_cols = TRUE, return_idx = TRUE)
x |
a |
keep_sam_cols |
if |
return_idx |
if |
a data.frame
or a list
.
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")
.
load_data(name)
load_data(name)
name |
a dataset name. Could be one of Builtin datasets:
Remote datasets stored in Zenodo:
|
a dataset, typically a data.frame
.
data1 <- load_data("tcga_surv") data1 data2 <- load_data("tcga_armcalls") data2
data1 <- load_data("tcga_surv") data1 data2 <- load_data("tcga_armcalls") data2
Quick molecule analysis and report generation
mol_quick_analysis(molecule, data_type, out_dir = ".", out_report = FALSE)
mol_quick_analysis(molecule, data_type, out_dir = ".", out_report = FALSE)
molecule |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
a list.
Phenotype Info of PCAWG Database
A data.frame
UCSC Xena.
data("pcawg_info")
data("pcawg_info")
Cleaned Phenotype Info of PCAWG Database for grouping
A data.frame
UCSC Xena.
data("pcawg_info_fine")
data("pcawg_info_fine")
Purity Data of PCAWG
A data.frame
UCSC Xena.
data("pcawg_purity")
data("pcawg_purity")
Download data for shiny general analysis
query_general_value( L1, L2, L3, database = c("toil", "pcawg", "ccle"), tpc_value_nonomics = NULL, opt_pancan = NULL, custom_metadata = NULL )
query_general_value( L1, L2, L3, database = c("toil", "pcawg", "ccle"), tpc_value_nonomics = NULL, opt_pancan = NULL, custom_metadata = NULL )
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 |
## 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)
## 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
query_molecule_value(dataset, molecule, host = NULL)
query_molecule_value(dataset, molecule, host = NULL)
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 ( |
host |
a UCSC Xena host, default is |
a named vector.
# What does dense matrix mean? table(UCSCXenaTools::XenaData$Type) # It is a the UCSC Xena dataset with "Type" equals to "genomicMatrix" ## Not run: dataset <- "ccle/CCLE_copynumber_byGene_2013-12-03" x <- query_molecule_value(dataset, "TP53") head(x) signature <- "TP53 + 2*KRAS - 1.3*PTEN" # a space must exist in the string y <- query_molecule_value(dataset, signature) head(y) ## End(Not run)
# What does dense matrix mean? table(UCSCXenaTools::XenaData$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
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 )
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 )
molecule |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
opt_pancan |
other extra parameters passing to the underlying functions. |
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.
a list.
mRNA–get_pancan_gene_value()
transcript–get_pancan_transcript_value()
protein–get_pancan_protein_value()
mutation–get_pancan_mutation_status()
cnv–get_pancan_cn_value()
methylation–get_pancan_methylation_value()
miRNA–get_pancan_miRNA_value()
mRNA–get_ccle_gene_value()
protein–get_ccle_protein_value()
mutation–get_ccle_mutation_status()
cnv–get_ccle_cn_value()
mRNA–get_pcawg_gene_value()
miRNA–get_pcawg_miRNA_value()
promoter–get_pcawg_promoter_value()
fusion–get_pcawg_fusion_value()
APOBEC–get_pcawg_APOBEC_mutagenesis_value()
## 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)
## 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
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 )
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 )
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 |
a list object with grouping samples and statistics
## 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)
## 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
Obtain ToilHub Info for Single Gene
query_toil_value_df(identifier = "TP53") query_toil_value_df(identifier = "TP53")
query_toil_value_df(identifier = "TP53") query_toil_value_df(identifier = "TP53")
identifier |
a length-1 character representing a gene symbol, ensembl gene id, or probe id. Gene symbol is highly recommended. |
a tibble
a tibble
## Not run: t <- query_toil_value_df() t ## End(Not run) ## Not run: t <- query_toil_value_df() t ## End(Not run)
## Not run: t <- query_toil_value_df() t ## End(Not run) ## Not run: t <- query_toil_value_df() t ## End(Not run)
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.
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", ... )
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", ... )
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 |
opt_pancan |
specify one dataset for some molercular profiles |
data |
a subset of result from |
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 |
cnv_type |
only used when profile is "cnv", can select from |
palette |
color palette, can be "hue", "grey", "RdBu", "Blues", "npg", "aaas", etc.
More see |
... |
other parameters passing to |
a data.frame
or a plot.
## 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)
## 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)
See tcga_surv
for TCGA survival data.
Generate from data-raw
data("tcga_clinical")
data("tcga_clinical")
See tcga_surv
for TCGA survival data.
Generate from data-raw
data("tcga_clinical_fine")
data("tcga_clinical_fine")
TCGA: Genome Instability Data
https://gdc.cancer.gov/about-data/publications/PanCanStemness-2018
data("tcga_genome_instability")
data("tcga_genome_instability")
Toil Hub: Merged TCGA GTEx Selected Phenotype
data("tcga_gtex")
data("tcga_gtex")
TCGA: Purity Data
https://www.nature.com/articles/ncomms9971#Sec14
data("tcga_purity")
data("tcga_purity")
TCGA Subtype Data
UCSC Xena.
data("tcga_subtypes")
data("tcga_subtypes")
Toil Hub: TCGA Survival Data
Generate from data-raw
data("tcga_surv")
data("tcga_surv")
TCGA: TMB (Tumor Mutation Burden) Data
https://gdc.cancer.gov/about-data/publications/panimmune
data("tcga_tmb")
data("tcga_tmb")
Toil Hub: TCGA TARGET GTEX Selected Phenotype
Generate from data-raw
data("toil_info")
data("toil_info")
Visualize CCLE Gene Expression Correlation
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 )
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 )
Gene1 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Gene2 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
use_log_y |
if |
use_regline |
if |
SitePrimary |
select cell line origin tissue. |
use_all |
use all sample, default |
alpha |
dot alpha. |
color |
dot color. |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
Visualize CCLE Gene Expression
vis_ccle_tpm( Gene = "TP53", data_type = "mRNA", use_log = FALSE, opt_pancan = .opt_pancan )
vis_ccle_tpm( Gene = "TP53", data_type = "mRNA", use_log = FALSE, opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
data_type |
support genomic profile for CCLE, currently "mRNA", "protein","cnv" are supported |
use_log |
if |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
Visualize the distribution difference of samples after dimensionality reduction analysis
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 )
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 )
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 |
a ggplot object or rawdata list
## 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)
## 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
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 )
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 )
Gene1 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Gene2 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
purity_adj |
whether performing partial correlation adjusted by purity |
alpha |
dot alpha. |
color |
dot color. |
filter_tumor |
whether use tumor sample only, default |
opt_pancan |
specify one dataset for some molercular profiles |
Visualize Gene-Gene Correlation in a TCGA Cancer Type
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 )
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 )
Gene1 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Gene2 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
cor_method |
correlation method. |
use_all |
use all sample, default |
alpha |
dot alpha. |
color |
dot color. |
opt_pancan |
specify one dataset for some molercular profiles |
Visualize cross-omics of one gene among pan-cancers
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 )
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 )
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. |
funkyheatmap
See analyze_gene_drug_response_asso for examples.
vis_gene_drug_response_asso( Gene = "TP53", x_axis_type = c("mean.diff", "median.diff"), output_form = c("plotly", "ggplot2") )
vis_gene_drug_response_asso( Gene = "TP53", x_axis_type = c("mean.diff", "median.diff"), output_form = c("plotly", "ggplot2") )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
x_axis_type |
set the value type for X axis. |
output_form |
|
plotly
or ggplot2
object.
See analyze_gene_drug_response_diff for examples.
vis_gene_drug_response_diff( Gene = "TP53", tissue = "lung", Show.P.label = TRUE, Method = "wilcox.test", values = c("#DF2020", "#DDDF21"), alpha = 0.5 )
vis_gene_drug_response_diff( Gene = "TP53", tissue = "lung", Show.P.label = TRUE, Method = "wilcox.test", values = c("#DF2020", "#DDDF21"), alpha = 0.5 )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
tissue |
select cell line origin tissue. |
Show.P.label |
|
Method |
default method is wilcox.test |
values |
the color to fill tumor or normal |
alpha |
set alpha for dots. |
a ggplot
object.
Heatmap for Correlation between Gene and Immune Signatures
vis_gene_immune_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Immune_sig_type = "Cibersort", Plot = "TRUE", opt_pancan = .opt_pancan )
vis_gene_immune_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Immune_sig_type = "Cibersort", Plot = "TRUE", opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
## Not run: p <- vis_gene_immune_cor(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_gene_immune_cor(Gene = "TP53") ## End(Not run)
Visualize Correlation between Gene and MSI (Microsatellite instability)
vis_gene_msi_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
vis_gene_msi_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
## Not run: p <- vis_gene_msi_cor(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_gene_msi_cor(Gene = "TP53") ## End(Not run)
Visualize Correlation between Gene and Pathway signature Score
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 )
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 )
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 |
a ggplot
object or dataframe
## Not run: vis_gene_pw_cor( Gene = "TP53", data_type = "mRNA", pw_name = "HALLMARK_ADIPOGENESIS", cancer_choose = "BRCA" ) ## End(Not run)
## 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
vis_gene_stemness_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
vis_gene_stemness_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
## Not run: p <- vis_gene_stemness_cor(Gene = "TP53") p ## End(Not run) ## To generate a radar plot, uncomment the following code # pdata <- p$data %>% # dplyr::mutate(cor = round(cor, digits = 3), p.value = round(p.value, digits = 3)) # # df <- pdata %>% # select(cor, cancer) %>% # pivot_wider(names_from = cancer, values_from = cor) # # ggradar::ggradar( # df[1, ], # font.radar = "sans", # values.radar = c("-1", "0", "1"), # grid.min = -1, grid.mid = 0, grid.max = 1, # # Background and grid lines # background.circle.colour = "white", # gridline.mid.colour = "grey", # # Polygons # group.line.width = 1, # group.point.size = 3, # group.colours = "#00AFBB") + # theme(plot.title = element_text(hjust = .5))
## Not run: p <- vis_gene_stemness_cor(Gene = "TP53") p ## End(Not run) ## To generate a radar plot, uncomment the following code # pdata <- p$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)
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 )
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 )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
## Not run: p <- vis_gene_TIL_cor(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_gene_TIL_cor(Gene = "TP53") ## End(Not run)
Visualize Correlation between Gene and TMB (Tumor Mutation Burden)
vis_gene_tmb_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
vis_gene_tmb_cor( Gene = "TP53", cor_method = "spearman", data_type = "mRNA", Plot = "TRUE", opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
## Not run: p <- vis_gene_tmb_cor(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_gene_tmb_cor(Gene = "TP53") ## End(Not run)
NOTE: the dataset must be dense matrix in UCSC Xena data hubs.
vis_identifier_cor( dataset1, id1, dataset2, id2, samples = NULL, use_ggstats = FALSE, use_simple_axis_label = TRUE, line_color = "blue", alpha = 0.5, ... )
vis_identifier_cor( dataset1, id1, dataset2, id2, samples = NULL, use_ggstats = FALSE, use_simple_axis_label = TRUE, line_color = "blue", alpha = 0.5, ... )
dataset1 |
the dataset to obtain |
id1 |
the first molecule identifier. |
dataset2 |
the dataset to obtain |
id2 |
the second molecule identifier. |
samples |
default is |
use_ggstats |
if |
use_simple_axis_label |
if |
line_color |
set the color for regression line. |
alpha |
set the alpha for dots. |
... |
other parameters passing to ggscatter. |
a (gg)plot object.
## 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)
## 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)
NOTE: the dataset must be dense matrix in UCSC Xena data hubs.
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" )
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" )
dataset |
the dataset to obtain identifiers. |
ids |
the molecule identifiers. |
grp_df |
When
|
samples |
default is |
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 |
a ggplot
object.
# vis_identifier_dim_dist(expr_dataset, ids, grp_df, DR_method="PCA")
# vis_identifier_dim_dist(expr_dataset, ids, grp_df, DR_method="PCA")
NOTE: the dataset must be dense matrix in UCSC Xena data hubs.
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(), ... )
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(), ... )
dataset |
the dataset to obtain identifiers. |
id |
the molecule identifier. |
grp_df |
When
|
samples |
default is |
fun_type |
select the function to compare groups. |
type |
A character specifying the type of statistical approach:
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: |
ggtheme |
A |
... |
other parameters passing to ggstatsplot::ggbetweenstats or ggstatsplot::ggwithinstats. |
a (gg)plot object.
## 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)
## 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)
NOTE: the dataset must be dense matrix in UCSC Xena data hubs.
vis_identifier_grp_surv( dataset = NULL, id = NULL, surv_df, samples = NULL, cutoff_mode = c("Auto", "Custom", "None"), cutpoint = c(50, 50), palette = "aaas", ... )
vis_identifier_grp_surv( dataset = NULL, id = NULL, surv_df, samples = NULL, cutoff_mode = c("Auto", "Custom", "None"), cutpoint = c(50, 50), palette = "aaas", ... )
dataset |
the dataset to obtain identifiers. |
id |
the molecule identifier. |
surv_df |
a
|
samples |
default is |
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 |
palette |
color palette, can be "hue", "grey", "RdBu", "Blues", "npg", "aaas", etc.
More see |
... |
other parameters passing to |
a (gg)plot object.
## 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)
## 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)
NOTE: the dataset must be dense matrix in UCSC Xena data hubs.
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", ... )
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", ... )
dataset |
the dataset to obtain identifiers. |
ids |
the molecule identifiers. |
samples |
default is |
matrix.type |
Character, |
type |
A character specifying the type of statistical approach:
You can specify just the initial letter. |
partial |
Can be |
sig.level |
Significance level (Default: |
p.adjust.method |
Adjustment method for p-values for multiple
comparisons. Possible methods are: |
color_low |
the color code for lower value mapping. |
color_high |
the color code for higher value mapping. |
... |
other parameters passing to ggstatsplot::ggcorrmat. |
a (gg)plot object.
## Not run: dataset <- "TcgaTargetGtex_rsem_isoform_tpm" ids <- c("TP53", "KRAS", "PTEN") vis_identifier_multi_cor(dataset, ids) ## End(Not run)
## 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
vis_pancan_anatomy( Gene = "TP53", Gender = c("Female", "Male"), data_type = "mRNA", option = "D", opt_pancan = .opt_pancan )
vis_pancan_anatomy( Gene = "TP53", Gender = c("Female", "Male"), data_type = "mRNA", option = "D", opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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:
|
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
Visualize cross-omics of one pathway among pan-cancers
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 )
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 )
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. |
funkyheatmap
Visualize molecular profile in PCAWG
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 )
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 )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Mode |
"Boxplot" or "Violinplot" to represent data |
data_type |
choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA". |
Show.P.value |
|
Show.P.label |
|
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 |
a ggplot
object
## Not run: p <- vis_pcawg_dist(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_pcawg_dist(Gene = "TP53") ## End(Not run)
Visualize Gene-Gene Correlation in TCGA
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 )
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 )
Gene1 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Gene2 |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
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 |
use_log_y |
if |
use_regline |
if |
dcc_project_code_choose |
select project code. |
use_all |
use all sample, default |
filter_tumor |
whether use tumor sample only, default |
alpha |
dot alpha. |
color |
dot color. |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
Visualize Single Gene Univariable Cox Result in PCAWG
vis_pcawg_unicox_tree( Gene = "TP53", measure = "OS", data_type = "mRNA", use_optimal_cutoff = FALSE, values = c("grey", "#E31A1C", "#377DB8"), opt_pancan = .opt_pancan )
vis_pcawg_unicox_tree( Gene = "TP53", measure = "OS", data_type = "mRNA", use_optimal_cutoff = FALSE, values = c("grey", "#E31A1C", "#377DB8"), opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
measure |
a survival measure, e.g. "OS". |
data_type |
choose gene profile type, including "mRNA","transcript","methylation","miRNA","protein","cnv" |
use_optimal_cutoff |
use |
values |
the color to fill tumor or normal |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
## Not run: p <- vis_pcawg_unicox_tree(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_pcawg_unicox_tree(Gene = "TP53") ## End(Not run)
Visualize molecular profile difference between mutation and wild status of queried gene
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 )
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 )
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 ( |
data_type |
choose gene profile type, including "mRNA", "transcript", "methylation", "miRNA". |
Mode |
choose one visualize mode to represent data |
Show.P.value |
|
Show.P.label |
|
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 |
a ggplot
object or a tibble data.frame
## 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)
## 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
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 )
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 )
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 ( |
data_type |
choose gene profile type, including "mRNA", "transcript", "methylation", "miRNA". |
Mode |
choose one visualize mode to represent data |
Show.P.value |
|
Show.P.label |
|
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 |
a ggplot
object or a tibble data.frame.
Visualize Pan-cancer TPM (tumor (TCGA) vs Normal (TCGA & GTEx))
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 )
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 )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Mode |
"Boxplot" or "Violinplot" to represent data |
data_type |
choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA". |
Show.P.value |
|
Show.P.label |
|
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 |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
## 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)
## 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))
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 )
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 )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
Mode |
"Boxplot" or "Violinplot" to represent data |
data_type |
choose gene profile type, including "mRNA", "transcript", "protein", "mutation", "cnv", "methylation", "miRNA". |
Show.P.value |
|
Show.P.label |
|
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 |
a ggplot
object.
Visualize Single Gene Univariable Cox Result from Toil Data Hub
vis_unicox_tree( Gene = "TP53", measure = "OS", data_type = "mRNA", use_optimal_cutoff = FALSE, values = c("grey", "#E31A1C", "#377DB8"), opt_pancan = .opt_pancan )
vis_unicox_tree( Gene = "TP53", measure = "OS", data_type = "mRNA", use_optimal_cutoff = FALSE, values = c("grey", "#E31A1C", "#377DB8"), opt_pancan = .opt_pancan )
Gene |
a molecular identifier (e.g., "TP53") or a formula specifying
genomic signature ( |
measure |
a survival measure, e.g. "OS". |
data_type |
choose gene profile type, including "mRNA","transcript","methylation","miRNA","protein","cnv" |
use_optimal_cutoff |
use |
values |
the color to fill tumor or normal |
opt_pancan |
specify one dataset for some molercular profiles |
a ggplot
object
## Not run: p <- vis_unicox_tree(Gene = "TP53") ## End(Not run)
## Not run: p <- vis_unicox_tree(Gene = "TP53") ## End(Not run)