| Title: | Tissue-Specific Enrichment Analysis |
|---|---|
| Description: | Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission. |
| Authors: | Guangsheng Pei |
| Maintainer: | Guangsheng Pei <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0 |
| Built: | 2025-01-31 03:08:06 UTC |
| Source: | https://github.com/cran/deTS |
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.
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 deTS, 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: | deTS |
| Type: | Package |
| Title: | Tissue-Specific Enrichment Analysis |
| Version: | 1.0 |
| Date: | 2019-02-06 |
| Author: | Guangsheng Pei |
| Maintainer: | Guangsheng Pei <[email protected]> |
| Imports: | pheatmap, RColorBrewer |
| Description: | Tissue-specific enrichment analysis to assess lists of candidate genes or RNA-Seq expression profiles. Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission. |
| License: | GPL (>= 2) |
| NeedsCompilation: | no |
| Packaged: | 2019-02-12 16:32:49 UTC; gpei |
| Depends: | R (>= 2.10) |
| Date/Publication: | 2019-02-22 13:30:10 UTC |
| Repository: | https://guangshengpei.r-universe.dev |
| RemoteUrl: | https://github.com/cran/deTS |
| RemoteRef: | HEAD |
| RemoteSha: | e8840a7f82a0b51c170d3f3665c8db525671e3a4 |
Index of help topics:
ENCODE_z_score ENCODE z-score to define tissue-specific genes
GTEx_t_score GTEx t-score to define tissue-specific genes
GWAS_gene Gene symbol query data for single sample
GWAS_gene_multiple Gene symbol query data for multiple samples
correction_factor Gene average expression level and standard
deviation in GTEx data
deTS-package Tissue-Specific Enrichment Analysis
Tissue-Specific Enrichment Analysis
query_ENCODE ENCODE raw query data
query_GTEx GTEx raw query data
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
Guangsheng Pei
Maintainer: Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(GTEx_t_score) data(ENCODE_z_score) library(pheatmap) data(GWAS_gene) query_gene_list = GWAS_gene tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "bonferroni") tsea_t_summary = tsea.summary(tsea_t) data(GWAS_gene_multiple) query_gene_list = GWAS_gene_multiple[,1:2] tsea_t_multi = tsea.analysis.multiple(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "BH") data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") 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)data(GTEx_t_score) data(ENCODE_z_score) library(pheatmap) data(GWAS_gene) query_gene_list = GWAS_gene tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "bonferroni") tsea_t_summary = tsea.summary(tsea_t) data(GWAS_gene_multiple) query_gene_list = GWAS_gene_multiple[,1:2] tsea_t_multi = tsea.analysis.multiple(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "BH") data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") 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)
Gene average expression level and standard deviation in GTEx data
data("correction_factor")data("correction_factor")
A data frame with 14725 observations on the following 2 variables.
avg.alla factor with gene average expression level
sd.alla factor with gene standard deviation of expression level
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) Tissue-Specific Enrichment Analysis deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(correction_factor)data(correction_factor)
ENCODE z-score matrix to define tissue-specific genes
data("ENCODE_z_score")data("ENCODE_z_score")
A data frame with z-score of 14031 genes in 44 ENCODE tissues.
Row is genes symbol and column is tissue names.
Adrenal Gland Body of Pancreas Breast Epithelium Camera-type Eye Cerebellum
C1orf112 -0.674 -0.440 -0.246 3.892 1.333
FGR -0.078 -0.345 0.159 -0.354 -0.407
CFH -0.093 -0.365 -0.134 -0.133 -0.160
FUCA2 3.028 1.467 0.040 0.228 -0.601
NFYA -0.637 -0.872 0.053 2.364 0.619
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(ENCODE_z_score)data(ENCODE_z_score)
GTEx t-score matrix to define tissue-specific genes
data("GTEx_t_score")data("GTEx_t_score")
A data frame with t-score of 14725 genes in 47 GTEx tissues.
Row is genes symbol and column is tissue names.
Adipose - Subcutaneous Adipose - Visceral (Omentum) Adrenal Gland Artery - Aorta Artery - Coronary
OR4F5 -0.524 -0.597 0.134 -1.109 -0.588
SAMD11 -9.921 -1.734 3.633 3.595 0.017
KLHL17 -6.812 -4.553 -3.084 -0.744 0.494
PLEKHN1 -7.785 -6.882 -3.915 -6.570 -4.892
C1orf170 -7.113 -6.257 -4.465 -5.897 -4.004
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(GTEx_t_score)data(GTEx_t_score)
An example of input gene symbol query data for single sample tissue-specific enrichment analysis
data("GWAS_gene")data("GWAS_gene")
The format is:
"A1BG" "A1BG-AS1" "A1CF" "A2M" "A2M-AS1" "A2ML1" "A2MP1" "A3GALT2" "A4GALT" "A4GNT" "AA06" "AAAS" "AACS" "AACSP1" "AADAC" ...
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(GWAS_gene)data(GWAS_gene)
An example of input gene symbol query data for multiple samples tissue-specific enrichment analysis
data("GWAS_gene_multiple")data("GWAS_gene_multiple")
A data frame with 22134 genes if associated with following 5 neuropsychiatric disorders GWAS traits.
Row is genes symbol and column is sample names.
ADHD ASD BD MDD SCZ
A1BG 0 0 0 0 0
A1BG-AS1 0 0 0 0 0
A1CF 0 1 0 0 0
A2M 0 0 0 0 0
A2M-AS1 0 0 0 0 0
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(GWAS_gene_multiple)data(GWAS_gene_multiple)
An example of RNA-Seq query data from ENCODE data for tissue-specific enrichment analysis
data("query_ENCODE")data("query_ENCODE")
A data frame with average expression level of 18661 genes in 44 ENCODE tissues.
Row is genes symbol and column is sample names.
Adrenal Gland Body of Pancreas Breast Epithelium Camera-type Eye Cerebellum
TSPAN6 11.64 5.390 11.04 24.65 13.238
TNMD 0.01 0.147 2.24 12.43 0.090
DPM1 18.82 9.812 14.21 24.02 10.505
SCYL3 3.81 2.593 5.63 10.50 3.783
C1orf112 1.64 2.308 2.86 14.61 7.345
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(query_ENCODE)data(query_ENCODE)
An example of RNA-Seq query data from GTEx data for tissue-specific enrichment analysis
data("query_GTEx")data("query_GTEx")
A data frame with average expression level of 18067 gene in 47 GTEx tissues.
Row is genes symbol and column is sample names.
Adipose - Subcutaneous Adipose - Visceral (Omentum) Adrenal Gland Artery - Aorta Artery - Coronary
OR4F5 0.0317 0.0284 0.0469 0.0133 0.0225
SAMD11 0.4451 2.3056 3.8928 3.5822 2.5632
NOC2L 21.9084 21.0439 19.4613 19.4929 19.8367
KLHL17 4.1406 4.4075 4.4227 5.0840 5.3749
PLEKHN1 0.4531 0.3452 1.1795 0.3081 0.3722
nothing
nothing
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
data(query_GTEx)data(query_GTEx)
Tissue-specific enrichment analysis by Fisher's Exact Test for given gene list.
tsea.analysis(query_gene_list, score, ratio = 0.05, p.adjust.method = "BH")tsea.analysis(query_gene_list, score, ratio = 0.05, p.adjust.method = "BH")
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 recommended "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") |
Tissue-specific enrichment analysis by Fisher's Exact Test for given gene list.
A data frame with p-value of tissue-specific enrichment result.
Rows stand for tissue names and columns stand for sample names.
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(GWAS_gene) data(GTEx_t_score) query_gene_list = GWAS_gene tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "bonferroni")data(GWAS_gene) data(GTEx_t_score) query_gene_list = GWAS_gene tsea_t = tsea.analysis(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "bonferroni")
Tissue-specific enrichment analysis by Fisher's Exact Test for multiple gene list.
tsea.analysis.multiple(query_gene_list, score, ratio = 0.05, p.adjust.method = "BH")tsea.analysis.multiple(query_gene_list, score, ratio = 0.05, p.adjust.method = "BH")
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 recommended "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") |
Tissue-specific enrichment analysis by Fisher's Exact Test for multiple gene list.
A data frame with p-value of tissue-specific enrichment result.
Rows stand for tissue names and columns stand for sample names.
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z. Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(GWAS_gene_multiple) data(GTEx_t_score) query_gene_list = GWAS_gene_multiple tsea_t_multi = tsea.analysis.multiple(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "BH")data(GWAS_gene_multiple) data(GTEx_t_score) query_gene_list = GWAS_gene_multiple tsea_t_multi = tsea.analysis.multiple(query_gene_list, GTEx_t_score, 0.05, p.adjust.method = "BH")
Tissue-specific enrichment analysis to decode whether a given RNA-seq sample (RPKM) with potential confounding effects based on expression profiles.
tsea.expression.decode(query_mat_normalized_score, score, ratio = 0.05, p.adjust.method = "BH")tsea.expression.decode(query_mat_normalized_score, score, ratio = 0.05, p.adjust.method = "BH")
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 recommended "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") |
Tissue-specific enrichment analysis for RNA-Seq expression profiles.
A data frame with p-value of tissue-specific enrichment result for RNA-Seq expression profiles.
Rows stand for tissue names and columns stand for sample names.
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) data(ENCODE_z_score) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor, ENCODE_z_score, 0.05, p.adjust.method = "BH")data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) data(ENCODE_z_score) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") tseaed_in_ENCODE = tsea.expression.decode(query_mat_zscore_nor, ENCODE_z_score, 0.05, p.adjust.method = "BH")
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 distribution before t-test.
tsea.expression.normalization(query_mat, correction_factor, normalization = "abundance")tsea.expression.normalization(query_mat, correction_factor, normalization = "abundance")
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") |
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.
A data frame with normalized RNA-Seq expression profiles.
Rows stand for tissue names and columns stand for sample names.
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score")data(query_GTEx) query_matrix = query_GTEx[,1:2] data(correction_factor) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score")
Heat map plot for tissue-specific enrichment analysis result.
tsea.plot(tsea_result, threshold = 0.05)tsea.plot(tsea_result, threshold = 0.05)
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. |
Heat map plot for tissue-specific enrichment analysis result
Heatmap plot
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(GWAS_gene_multiple) data(GTEx_t_score) 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.plot(tsea_t_multi, 0.05)data(GWAS_gene_multiple) data(GTEx_t_score) 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.plot(tsea_t_multi, 0.05)
Tissue-specific enrichment analysis result summary (list the top 3 most enriched tissues) from the given gene list or RNA-seq expression profiles.
tsea.summary(tsea_result)tsea.summary(tsea_result)
tsea_result |
the result of tissue-specific enrichment analysis, which produced by "tsea.analysis", "tsea.analysis.multiple" or "tsea.expression.decode". |
Tissue-specific enrichment analysis result summary
A data frame with summary result of top 3 most enriched tissues.
Rows stand for sample names and columns stand for top 3 most enriched tissues (with p-value).
nothing
Guangsheng Pei
Pei G., Dai Y., Zhao Z., Jia P. (2019) deTS: Tissue-Specific Enrichment Analysis to decode tissue specificity. Bioinformatics, In submission.
https://github.com/bsml320/deTS
data(query_GTEx) query_matrix = query_GTEx data(correction_factor) data(ENCODE_z_score) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") 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)data(query_GTEx) query_matrix = query_GTEx data(correction_factor) data(ENCODE_z_score) query_mat_zscore_nor = tsea.expression.normalization(query_matrix, correction_factor, normalization = "z-score") 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)