Last updated: 2023-11-07
Checks: 7 0
Knit directory: muse/
This reproducible R Markdown analysis was created with workflowr (version 1.7.1). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
The command set.seed(20200712) was run prior to running
the code in the R Markdown file. Setting a seed ensures that any results
that rely on randomness, e.g. subsampling or permutations, are
reproducible.
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.
Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.
Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.
The results in this page were generated with repository version 748c6d1. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.
Note that you need to be careful to ensure that all relevant files for
the analysis have been committed to Git prior to generating the results
(you can use wflow_publish or
wflow_git_commit). workflowr only checks the R Markdown
file, but you know if there are other scripts or data files that it
depends on. Below is the status of the Git repository when the results
were generated:
Ignored files:
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: r_packages_4.3.2/
Untracked files:
Untracked: analysis/cell_ranger.Rmd
Untracked: analysis/sleuth.Rmd
Untracked: analysis/tss_xgboost.Rmd
Untracked: code/multiz100way/
Untracked: data/HG00702_SH089_CHSTrio.chr1.vcf.gz
Untracked: data/HG00702_SH089_CHSTrio.chr1.vcf.gz.tbi
Untracked: data/ncrna_NONCODE[v3.0].fasta.tar.gz
Untracked: data/ncrna_noncode_v3.fa
Untracked: data/netmhciipan.out.gz
Untracked: export/davetang039sblog.WordPress.2023-06-30.xml
Untracked: export/output/
Untracked: women.json
Unstaged changes:
Modified: analysis/graph.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/gsva.Rmd) and HTML
(docs/gsva.html) files. If you’ve configured a remote Git
repository (see ?wflow_git_remote), click on the hyperlinks
in the table below to view the files as they were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 748c6d1 | Dave Tang | 2023-11-07 | Enrichment scores |
| html | a077e6c | Dave Tang | 2023-10-23 | Build site. |
| Rmd | 115752b | Dave Tang | 2023-10-23 | Technical notes |
| html | 14e3e6b | Dave Tang | 2023-10-23 | Build site. |
| Rmd | 9b9327a | Dave Tang | 2023-10-23 | No progress bar |
| html | b0af093 | Dave Tang | 2023-10-23 | Build site. |
| Rmd | aeb90e1 | Dave Tang | 2023-10-23 | GSVA |
Following the vignette.
Gene set variation analysis (GSVA) is a particular type of gene set enrichment method that works on single samples and enables pathway-centric analyses of molecular data by performing a conceptually simple but powerful change in the functional unit of analysis, from genes to gene sets. The GSVA package provides the implementation of four single-sample gene set enrichment methods, concretely zscore, plage, ssGSEA and its own called GSVA. While this methodology was initially developed for gene expression data, it can be applied to other types of molecular profiling data. In this vignette we illustrate how to use the GSVA package with bulk microarray and RNA-seq expression data.
Gene set variation analysis (GSVA) provides an estimate of pathway activity by transforming an input gene-by-sample expression data matrix into a corresponding gene-set-by-sample expression data matrix.
This resulting expression data matrix can be then used with classical analytical methods such as differential expression, classification, survival analysis, clustering or correlation analysis in a pathway-centric manner. One can also perform sample-wise comparisons between pathways and other molecular data types such as microRNA expression or binding data, copy-number variation (CNV) data or single nucleotide polymorphisms (SNPs).
Install GSVA. (Dependencies are listed in the Imports section in the DESCRIPTION file.)
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("GSVA", quietly = TRUE))
BiocManager::install("GSVA")Load package.
library(GSVA)
packageVersion("GSVA")[1] '1.50.0'Generate example expression matrix.
p <- 10000
n <- 30
set.seed(1984)
X <- matrix(
rnorm(p*n),
nrow=p,
dimnames=list(paste0("g", 1:p), paste0("s", 1:n))
)
X[1:5, 1:5] s1 s2 s3 s4 s5
g1 0.4092032 1.4676435 0.3515056 1.53512312 -1.279009469
g2 -0.3230250 -1.8501416 -0.9198650 1.40036448 0.086613315
g3 0.6358523 1.6084120 1.6380322 0.23799146 0.216628121
g4 -1.8461288 -0.2928844 0.4651573 -0.09766558 -0.009887299
g5 0.9536474 -0.4816006 0.1807824 1.03141311 0.206414282Generate 100 gene sets that are contain from 10 to up to 100 genes
sampled from 1:p.
set.seed(1984)
gs <- as.list(sample(10:100, size=100, replace=TRUE))
gs <- lapply(gs, function(n, p){
paste0("g", sample(1:p, size=n, replace=FALSE))
}, p)
names(gs) <- paste0("gs", 1:length(gs))
sapply(gs, length) gs1 gs2 gs3 gs4 gs5 gs6 gs7 gs8 gs9 gs10 gs11 gs12 gs13
49 29 67 90 94 87 41 26 86 77 97 90 45
gs14 gs15 gs16 gs17 gs18 gs19 gs20 gs21 gs22 gs23 gs24 gs25 gs26
47 54 83 11 75 95 99 94 89 93 50 49 87
gs27 gs28 gs29 gs30 gs31 gs32 gs33 gs34 gs35 gs36 gs37 gs38 gs39
36 61 84 99 58 30 63 29 35 29 69 41 46
gs40 gs41 gs42 gs43 gs44 gs45 gs46 gs47 gs48 gs49 gs50 gs51 gs52
38 17 48 72 15 81 100 93 37 99 89 43 36
gs53 gs54 gs55 gs56 gs57 gs58 gs59 gs60 gs61 gs62 gs63 gs64 gs65
84 83 40 72 90 86 37 23 69 96 20 93 36
gs66 gs67 gs68 gs69 gs70 gs71 gs72 gs73 gs74 gs75 gs76 gs77 gs78
21 46 76 71 57 48 25 73 26 46 29 53 69
gs79 gs80 gs81 gs82 gs83 gs84 gs85 gs86 gs87 gs88 gs89 gs90 gs91
69 42 76 30 16 49 35 12 83 99 88 66 10
gs92 gs93 gs94 gs95 gs96 gs97 gs98 gs99 gs100
51 82 73 97 59 59 42 10 64 Calculate GSVA enrichment scores using the gsva()
function, which does all the work and requires the following two input
arguments:
The first argument to the
gsva()function is the gene expression data matrix and the second the collection of gene sets. Thegsva()function can take the input expression data and gene sets using different specialized containers that facilitate the access and manipulation of molecular and phenotype data, as well as their associated metadata. Another advanced features include the use of on-disk and parallel backends to enable, respectively, using GSVA on large molecular data sets and speed up computing time.
The gsva() function will apply the following filters
before the actual calculations take place:
Inf for the maximum size.When method="gsva" is used (the default), the following
parameters can be tuned:
kcdf: The first step of the GSVA algorithm brings gene
expression profiles to a common scale by calculating an expression
statistic through a non-parametric estimation of the CDF across samples.
Such a non-parametric estimation employs a kernel function and the
kcdf parameter allows the user to specify three possible
values for that function:mx.diff: The last step of the GSVA algorithm calculates
the gene set enrichment score from two Kolmogorov-Smirnov random walk
statistics. This parameter is a logical flag that allows the user to
specify two possible ways to do such calculation:TRUE, the default value, where the enrichment score is
calculated as the magnitude difference between the largest positive and
negative random walk deviations;FALSE, where the enrichment score is calculated as the
maximum distance of the random walk from zero.abs.ranking: Logical flag used only when
mx.diff=TRUE. By default, abs.ranking=FALSE
and it implies that a modified Kuiper statistic is used to calculate
enrichment scores, taking the magnitude difference between the largest
positive and negative random walk deviations. When
abs.ranking=TRUE the original Kuiper statistic is used, by
which the largest positive and negative random walk deviations are added
together. In this case, gene sets with genes enriched on either extreme
(high or low) will be regarded as highly activated.
tau: Exponent defining the weight of the tail in the
random walk. By default tau=1. When
method="ssgsea", this parameter is also used and its
default value becomes then tau=0.25 to match the
methodology described in (Barbie et al. 2009).
In general, the default values for the previous parameters are suitable for most analysis settings, which usually consist of some kind of normalized continuous expression values.
es_gsva <- gsva(X, gs, verbose=FALSE)Warning: Calling gsva(expr=., gset.idx.list=., method=., ...) is deprecated;
use a method-specific parameter object (see '?gsva').dim(es_gsva)[1] 100 30Median enrichment scores.
apply(es_gsva, 2, median) s1 s2 s3 s4 s5 s6
0.009061514 -0.008458424 -0.005713628 -0.021937531 0.006417182 0.019422486
s7 s8 s9 s10 s11 s12
0.010140618 0.005812097 0.006495584 0.008887644 0.024577619 -0.031697634
s13 s14 s15 s16 s17 s18
0.001642502 0.008919786 -0.022622470 0.027695420 -0.015799537 -0.011108686
s19 s20 s21 s22 s23 s24
-0.013956442 -0.015493300 -0.004809844 -0.014081494 0.026845336 0.023895676
s25 s26 s27 s28 s29 s30
0.006358240 0.010642450 -0.012690144 -0.005999451 -0.005058572 -0.012403422 ssgsea (Barbie et al. 2009). Single sample GSEA (ssGSEA) is a non-parametric method that calculates a gene set enrichment score per sample as the normalized difference in empirical cumulative distribution functions (CDFs) of gene expression ranks inside and outside the gene set. By default, the implementation in the GSVA package follows the last step described in (Barbie et al. 2009, online methods, pg. 2) by which pathway scores are normalized, dividing them by the range of calculated values. This normalization step may be switched off using the argument ssgsea.norm in the call to the gsva() function; see below.
es_ssgsea <- gsva(X, gs, method = "ssgsea", verbose=FALSE)Warning: Calling gsva(expr=., gset.idx.list=., method=., ...) is deprecated;
use a method-specific parameter object (see '?gsva').[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
[1] "Normalizing..."apply(es_ssgsea, 2, median) s1 s2 s3 s4 s5 s6 s7 s8
0.1056332 0.1105148 0.1149790 0.1045303 0.1192256 0.1274702 0.1192217 0.1287768
s9 s10 s11 s12 s13 s14 s15 s16
0.1316429 0.1257247 0.1293241 0.1083643 0.1222038 0.1012195 0.1018693 0.1174057
s17 s18 s19 s20 s21 s22 s23 s24
0.1103184 0.1112410 0.1164373 0.1126160 0.1212393 0.1067666 0.1167117 0.1418775
s25 s26 s27 s28 s29 s30
0.1221909 0.1235449 0.1112199 0.1012381 0.1254514 0.1210392 Create another test matrix.
p <- 10000
n <- 2
set.seed(1984)
X <- matrix(
rnorm(n = p*n, mean = 10, sd = 10),
nrow=p,
dimnames=list(paste0("g", 1:p), paste0("s", 1:n))
)
X[1:50, 's1'] <- rnorm(n = 50, mean = 50, sd = 55)
X[51:100, 's1'] <- rnorm(n = 50, mean = 2, sd = 2)
X[1:50, 's2'] <- rnorm(n = 50, mean = 100, sd = 5)
X[51:100, 's2'] <- rnorm(n = 50, mean = 2, sd = 0.5)
X[1:5, ] s1 s2
g1 69.3328103 96.46911
g2 -0.5925752 96.19110
g3 140.0917737 102.47525
g4 75.5836499 103.64442
g5 59.9430328 99.50861Create testing gene lists with higher and lower expression patterns and check their enrichment scores.
gene_set <- list(
gs1 = paste0("g", 1:50),
gs2 = paste0("g", 51:100),
gs3 = paste0("g", 101:150)
)
test_ssgsea <- gsva(X, gene_set, method = "ssgsea", verbose=FALSE)Warning: Calling gsva(expr=., gset.idx.list=., method=., ...) is deprecated;
use a method-specific parameter object (see '?gsva').[1] "Calculating ranks..."
[1] "Calculating absolute values from ranks..."
[1] "Normalizing..."test_ssgsea s1 s2
gs1 0.50202664 0.62745511
gs2 -0.35383441 -0.37254489
gs3 0.01605471 0.08617871
sessionInfo()R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.3 LTS
Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
[5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
time zone: Etc/UTC
tzcode source: system (glibc)
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] IRanges_2.36.0 GSVA_1.50.0 BiocManager_1.30.22
[4] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] blob_1.2.4 Biostrings_2.70.1
[3] bitops_1.0-7 fastmap_1.1.1
[5] SingleCellExperiment_1.24.0 RCurl_1.98-1.12
[7] promises_1.2.1 rsvd_1.0.5
[9] XML_3.99-0.14 digest_0.6.33
[11] lifecycle_1.0.3 processx_3.8.2
[13] KEGGREST_1.42.0 RSQLite_2.3.2
[15] magrittr_2.0.3 compiler_4.3.2
[17] rlang_1.1.1 sass_0.4.7
[19] tools_4.3.2 utf8_1.2.4
[21] yaml_2.3.7 knitr_1.45
[23] S4Arrays_1.2.0 bit_4.0.5
[25] DelayedArray_0.28.0 abind_1.4-5
[27] BiocParallel_1.36.0 HDF5Array_1.30.0
[29] BiocGenerics_0.48.0 grid_4.3.2
[31] stats4_4.3.2 fansi_1.0.5
[33] git2r_0.32.0 beachmat_2.18.0
[35] xtable_1.8-4 Rhdf5lib_1.24.0
[37] SummarizedExperiment_1.32.0 cli_3.6.1
[39] rmarkdown_2.25 crayon_1.5.2
[41] rstudioapi_0.15.0 httr_1.4.7
[43] DelayedMatrixStats_1.24.0 DBI_1.1.3
[45] cachem_1.0.8 rhdf5_2.46.0
[47] stringr_1.5.0 zlibbioc_1.48.0
[49] parallel_4.3.2 AnnotationDbi_1.64.0
[51] XVector_0.42.0 matrixStats_1.0.0
[53] vctrs_0.6.4 Matrix_1.6-1.1
[55] jsonlite_1.8.7 BiocSingular_1.18.0
[57] callr_3.7.3 S4Vectors_0.40.1
[59] bit64_4.0.5 irlba_2.3.5.1
[61] GSEABase_1.64.0 jquerylib_0.1.4
[63] annotate_1.80.0 glue_1.6.2
[65] codetools_0.2-19 ps_1.7.5
[67] stringi_1.7.12 later_1.3.1
[69] GenomeInfoDb_1.38.0 GenomicRanges_1.54.1
[71] ScaledMatrix_1.10.0 tibble_3.2.1
[73] pillar_1.9.0 htmltools_0.5.6.1
[75] rhdf5filters_1.14.0 graph_1.80.0
[77] GenomeInfoDbData_1.2.11 R6_2.5.1
[79] sparseMatrixStats_1.14.0 rprojroot_2.0.3
[81] evaluate_0.22 Biobase_2.62.0
[83] lattice_0.21-9 png_0.1-8
[85] memoise_2.0.1 httpuv_1.6.12
[87] bslib_0.5.1 Rcpp_1.0.11
[89] SparseArray_1.2.0 whisker_0.4.1
[91] xfun_0.40 fs_1.6.3
[93] MatrixGenerics_1.14.0 getPass_0.2-2
[95] pkgconfig_2.0.3