TSEA Manual
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Package ‘TSEA’
August 21, 2018
Type Package
Title Tissue-Specific Enrichment Analysis
Version 1.0
Date 2018-08-21
Author Guangsheng Pei
Maintainer Guangsheng Pei <peiguangsheng@gmail.com>
Imports pheatmap, RColorBrewer
Description Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-
Seq expression profiles.
License GPL (>= 2)
NeedsCompilation no
Rtopics documented:
TSEA-package ....................................... 1
tsea.analysis......................................... 4
tsea.analysis.multiple.................................... 6
tsea.expression.decode ................................... 7
tsea.expression.normalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
tsea.plot........................................... 9
tsea.summary ........................................ 10
Index 12
TSEA-package Tissue-Specific Enrichment Analysis Tissue-Specific Enrichment Anal-
ysis
Description
Tissue-specific enrichment analysis to assess lists of candidate genes and tissue-specific expression
decode analysis for RNA-seq data to decode RNA expression matrices tissue heterogeneity.
1
2TSEA-package
Details
Since disease and physiological condition are often associated with a specific tissue, understanding
the tissue-specific genes (TSG) expression patterns will substantially reduce false discoveries in
biomedical research. However, due to cell complexity in human system, heterogeneous tissues are
frequently collected. Making it difficult to distinguish gene expression variability and mislead result
interpretation. Here, we present TSEA, an R package that conducts Tissue-Specific Enrichment
Analysis (TSEA) using two built-in reference panels: the Genotype-Tissue Expression (GTEx) data
and the ENCyclopedia Of DNA Elements (ENCODE) data. We implemented two major functions
in TSEA to assess lists of candidate genes or expression matrices.
The DESCRIPTION file:
Package: TSEA
Type: Package
Title: Tissue-Specific Enrichment Analysis
Version: 1.0
Date: 2018-08-21
Author: Guangsheng Pei
Maintainer: Guangsheng Pei <peiguangsheng@gmail.com>
Imports: pheatmap, RColorBrewer
Description: Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles.
License: GPL (>= 2)
Index of help topics:
TSEA-package Tissue-Specific Enrichment Analysis
Tissue-Specific Enrichment Analysis
tsea.analysis Tissue-specific enrichment analysis for query
gene list
tsea.analysis.multiple
Tissue-specific enrichment analysis for multi
query gene lists
tsea.expression.decode
Tissue-specific enrichment analysis for RNA-Seq
expression profiles
tsea.expression.normalization
RNA-Seq expression profiles normalization
tsea.plot Tissue-specific enrichment analysis result
heatmap plot
tsea.summary Tissue-specific enrichment analysis result
summary
Author(s)
Guangsheng Pei
Maintainer: Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
TSEA-package 3
See Also
https://github.com/bsml320/TSEA/blob/master/README.md
Examples
#Download the built-in data from https://github.com/bsml320/TSEA/tree/master/data
#load("data/GTEx_t_score.rda")
#load("data/ENCODE_z_score.rda")
#library(pheatmap)
#==================================================================
#Example 1: Lists of candidate genes for single sample.
#load("data/GWAS_gene.rda")
#query_gene_list = GWAS_gene
#TSEA in GTEx panel
#tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, ratio = 0.05,
#p.adjust.method = "bonferroni")
#write.csv(tsea_t,"1.1.GWAS_TSEA_in_GTEx_panel.csv")
#pdf ("1.2.GWAS_TSEA_in_GTEx_panel.pdf", 5, 9, onefile = FALSE)
#tsea.plot(tsea_t, threshold = 0.05)
#dev.off()
#tsea_t_summary = tsea.summary(tsea_t)
#write.csv(tsea_t_summary,"1.3.GWAS_summary_in_GTEx_panel.csv")
#TSEA in ENCODE panel
#tsea_z = tsea.analysis(query_gene_list, ENCODE_z_score, ratio = 0.05,
#p.adjust.method = "bonferroni")
#write.csv(tsea_z,"1.4.GWAS_TSEA_in_ENCODE_panel.csv")
#pdf ("1.5.GWAS_TSEA_in_ENCODE_panel.pdf", 5, 9, onefile = FALSE)
#tsea.plot(tsea_z, threshold = 0.05)
#dev.off()
#tsea_z_summary = tsea.summary(tsea_z)
#write.csv(tsea_z_summary,"1.6.GWAS_summary_in_ENCODE_panel.csv")
#==================================================================
#Example 2: Lists of candidate genes for multiple samples.
#load("data/GWAS_gene_multiple.rda")
#query_gene_list = GWAS_gene_multiple
#TSEA in GTEx panel
#tsea_t_multi = tsea.analysis.multiple(query_gene_list,
#GTEx_t_score, ratio = 0.05, p.adjust.method = "BH")
#write.csv(tsea_t_multi,"2.1.GWAS_multi_TSEA_in_GTEx_panel.csv")
#pdf ("2.2.GWAS_multi_TSEA_in_GTEx_panel.pdf", 6, 6, onefile = FALSE)
#tsea.plot(tsea_t_multi, threshold = 0.05)
#dev.off()
#tsea_t_multi_summary = tsea.summary(tsea_t_multi)
#write.csv(tsea_t_multi_summary,"2.3.GWAS_multi_summary_in_GTEx_panel.csv")
#TSEA in ENCODE panel
#tsea_z_multi = tsea.analysis.multiple(query_gene_list,
#ENCODE_z_score, ratio = 0.05, p.adjust.method = "BH")
#write.csv(tsea_z_multi,"2.4.GWAS_multi_TSEA_in_ENCODE_panel.csv")
#pdf ("2.5.GWAS_multi_TSEA_in_ENCODE_panel.pdf", 7, 7, onefile = FALSE)
#tsea.plot(tsea_z_multi, threshold = 0.05)
4tsea.analysis
#dev.off()
#tsea_z_multi_summary = tsea.summary(tsea_z_multi)
#write.csv(tsea_z_multi_summary,"2.6.GWAS_multi_summary_in_ENCODE_panel.csv")
#==================================================================
#Example 3: RNA expression profiles TSEA in ENCODE panel.
#load("data/query_GTEx.rda")
#query_matrix = query_GTEx
#load("data/correction_factor.rda")
#RNA expression profiles z-score normalization
#query_mat_zscore_nor = tsea.expression.normalization(query_matrix,
#correction_factor, normalization = "z-score")
#RNA expression profiles TSEA in ENCODE panel
#tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor,
#ENCODE_z_score, ratio = 0.05, p.adjust.method = "BH")
#write.csv(tseaed_in_ENCODE,"3.1.RNAseq_TSEA_in_ENCODE_panel.csv")
#pdf ("3.2.RNAseq_TSEA_in_ENCODE_panel.pdf", 10, 9, onefile = FALSE)
#tsea.plot(tseaed_in_ENCODE, threshold = 0.05)
#dev.off()
#tseaed_in_ENCODE_summary = tsea.summary(tseaed_in_ENCODE)
#write.csv(tseaed_in_ENCODE_summary,"3.3.RNAseq_summary_in_ENCODE_panel.csv")
#==================================================================
#Example 4: RNA expression profiles TSEA in GTEx panel.
#load("data/query_ENCODE.rda")
#query_matrix = query_ENCODE
#RNA expression profiles abundance normalization
#query_mat_abundance_nor = tsea.expression.normalization(query_matrix,
#correction_factor, normalization = "abundance")
#RNA expression profiles TSEA in GTEx panel
#tseaed_in_GTEx = tsea.expression.decode(query_mat_abundance_nor,
#GTEx_t_score, ratio = 0.05, p.adjust.method = "BH")
#write.csv(tseaed_in_GTEx,"4.1.RNAseq_TSEA_in_GTEx_panel.csv")
#pdf ("4.2.RNAseq_TSEA_in_GTEx_panel.pdf", 10, 9, onefile = FALSE)
#tsea.plot(tseaed_in_GTEx, threshold = 0.05)
#dev.off()
#tseaed_in_GTEx_summary = tsea.summary(tseaed_in_GTEx)
#write.csv(tseaed_in_GTEx_summary,"4.3.RNAseq_summary_in_GTEx_panel.csv")
tsea.analysis Tissue-specific enrichment analysis for query gene list
Description
Tissue-specific enrichment analysis by Fisher’s Exact Test for given gene list.
Usage
tsea.analysis(query_gene_list, score, ratio = 0.05,
p.adjust.method = "BH")
tsea.analysis 5
Arguments
query_gene_list
a gene symbol list object.
score a gene tissue-specific score matrix, c("GTEx_t_score" or "ENCODE_z_score"),
can be loaded by data(GTEx) or data(ENCODE), the default value is recom-
mended "GTEx_t_score".
ratio the threshold to define tissue-specific genes (with top t-score or z-score), the
default value is 0.05.
p.adjust.method
p.adjust.method, c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none")
Details
Tissue-specific enrichment analysis by Fisher’s Exact Test for given gene list.
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
Examples
#load("data/GWAS_gene.rda")
#query_gene_list = GWAS_gene
#tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, 0.05,
#p.adjust.method = "bonferroni")
6tsea.analysis.multiple
tsea.analysis.multiple
Tissue-specific enrichment analysis for multi query gene lists
Description
Tissue-specific enrichment analysis by Fisher’s Exact Test for multiple gene list.
Usage
tsea.analysis.multiple(query_gene_list, score, ratio = 0.05,
p.adjust.method = "BH")
Arguments
query_gene_list
a 0~1 gene~sample table object, row should be gene symbol, column should be
sample name. In the table, gene labeled with 1 indicated it is target gene for a
given sample, while 0 indicated it is not target in a given sample.
score a gene tissue-specific score matrix, c("GTEx_t_score" or "ENCODE_z_score"),
can be loaded by data(GTEx) or data(ENCODE), the default value is recom-
mended "GTEx_t_score".
ratio the threshold to define tissue-specific genes (with top t-score or z-score), the
default value is 0.05.
p.adjust.method
p.adjust.method, c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none")
Details
Tissue-specific enrichment analysis by Fisher’s Exact Test for multiple gene list.
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
tsea.expression.decode 7
Examples
#load("data/GWAS_gene_multiple.rda")
#query_gene_list = GWAS_gene_multiple
#tsea_t_multi = tsea.analysis.multiple(query_gene_list,
#GTEx_t_score, 0.05, p.adjust.method = "BH")
tsea.expression.decode
Tissue-specific enrichment analysis for RNA-Seq expression profiles
Description
Tissue-specific enrichment analysis to decode whether a given RNA-seq sample (RPKM) with po-
tential confounding effects based on expression profiles.
Usage
tsea.expression.decode(query_mat_normalized_score, score,
ratio = 0.05, p.adjust.method = "BH")
Arguments
query_mat_normalized_score
a normalized RNA-seq RPKM object, which produced by "tsea.expression.normalization".
score a gene tissue-specific score matrix, c("GTEx_t_score" or "ENCODE_z_score"),
can be loaded by data(GTEx) or data(ENCODE), the default value is recom-
mended "GTEx_t_score".
ratio the threshold to define tissue-specific genes (with top t-score or z-score), the
default value is 0.05.
p.adjust.method
p.adjust.method, c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY",
"fdr", "none")
Details
Tissue-specific enrichment analysis for RNA-Seq expression profiles.
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
8tsea.expression.normalization
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
Examples
#load("data/query_GTEx.rda")
#query_matrix = query_GTEx
#load("data/correction_factor.rda")
#RNA expression profiles z-score normalization
#query_mat_zscore_nor = tsea.expression.normalization(query_matrix,
#correction_factor, normalization = "z-score")
#RNA expression profiles TSEA in ENCODE panel
#tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor,
#ENCODE_z_score, 0.05, p.adjust.method = "BH")
tsea.expression.normalization
RNA-Seq expression profiles normalization
Description
To avoid the data bias and adapt better data heterogeneity, before tsea.expression.decode() analysis,
the raw discrete RPKM value have to normalized to continuous variable meet the normal distribu-
tion before t-test.
Usage
tsea.expression.normalization(query_mat,
correction_factor, normalization = "abundance")
Arguments
query_mat a RNA-seq RPKM object, row name should be gene symbol, and column name
should be sample name.
correction_factor
correction_factor, a gene table object contain genes average expression level and
standard variance in GTEx database, can be loaded by data(correction_factor).
normalization
normalization methods, c("z-score", "abundance")
Details
As RNA-Seq samples are often heterogeneous, before in-depth analysis, it is necessary to decode
tissue heterogeneity to avoid samples with confounding effects. However, the raw discrete RPKM
value have to normalized to continuous variable meet the normal distribution before t-test.
tsea.plot 9
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
Examples
#load("data/query_GTEx.rda")
#query_matrix = query_GTEx
#load("data/correction_factor.rda")
#RNA expression profiles z-score normalization
#query_mat_zscore_nor = tsea.expression.normalization(query_matrix,
#correction_factor, normalization = "z-score")
#RNA expression profiles TSEA in ENCODE panel
#tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor,
#ENCODE_z_score, 0.05, p.adjust.method = "BH")
tsea.plot Tissue-specific enrichment analysis result heatmap plot
Description
Heat map plot for tissue-specific enrichment analysis result.
Usage
tsea.plot(tsea_result, threshold = 0.05)
Arguments
tsea_result the result of tissue-specific enrichment analysis, which produced by "tsea.analysis",
"tsea.analysis.multiple" or "tsea.expression.decode".
threshold the p-value threshold to define if the gene list or RNA-seq data enriched in a
given tissue, p-value greater than threshold will not be labeled in the plot. The
default value is 0.05.
10 tsea.summary
Details
Heat map plot for tissue-specific enrichment analysis result
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
Examples
#load("data/GWAS_gene_multiple.rda")
#query_gene_list = GWAS_gene_multiple
#TSEA in GTEx panel
#tsea_t_multi = tsea.analysis.multiple(query_gene_list,
#GTEx_t_score, 0.05, p.adjust.method = "BH")
#tsea.plot(tsea_t_multi, 0.05)
tsea.summary Tissue-specific enrichment analysis result summary
Description
Tissue-specific enrichment analysis result summary (list the top 3 most enriched tissues) from the
given gene list or RNA-seq expression profiles.
Usage
tsea.summary(tsea_result)
Arguments
tsea_result the result of tissue-specific enrichment analysis, which produced by "tsea.analysis",
"tsea.analysis.multiple" or "tsea.expression.decode".
tsea.summary 11
Details
Tissue-specific enrichment analysis result summary
Value
nothing
Note
nothing
Author(s)
Guangsheng Pei
References
Pei G., Dai Y., Zhao Z. Jia P. (2018) Tissue-Specific Enrichment Analysis (TSEA) to decode tissue
heterogeneity. Bioinformatics, In submission.
See Also
https://github.com/bsml320/TSEA/
Examples
#load("data/query_GTEx.rda")
#query_matrix = query_GTEx
#load("data/correction_factor.rda")
#RNA expression profiles z-score normalization
#query_mat_zscore_nor = tsea.expression.normalization(query_matrix,
#correction_factor, normalization = "z-score")
#RNA expression profiles TSEA in ENCODE panel
#tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor,
#ENCODE_z_score, 0.05, p.adjust.method = "BH")
#tseaed_in_ENCODE_summary = tsea.summary(tseaed_in_ENCODE)
Index
∗Topic TSEA
TSEA-package,1
∗Topic \textasciitildekwd1
tsea.analysis,4
tsea.analysis.multiple,6
tsea.expression.decode,7
tsea.expression.normalization,
8
tsea.plot,9
tsea.summary,10
∗Topic \textasciitildekwd2
tsea.analysis,4
tsea.analysis.multiple,6
tsea.expression.decode,7
tsea.expression.normalization,
8
tsea.plot,9
tsea.summary,10
TSEA (TSEA-package),1
TSEA-package,1
tsea.analysis,4
tsea.analysis.multiple,6
tsea.expression.decode,7
tsea.expression.normalization,8
tsea.plot,9
tsea.summary,10
12
