Get Started
21 April 2024
Contents
Compiled date: 2024-04-21
Last edited: 2024-01-21
License: GPL-3
1 Installation
To install the Bioconductor version of the POMA package, run the following code:
# install.packages("BiocManager")
BiocManager::install("POMA")2 Load POMA
library(POMA)
library(ggtext)
library(magrittr)3 The POMA Workflow
The POMA package functions are organized into three sequential, distinct blocks: Data Preparation, Pre-processing, and Statistical Analysis.
3.1 Data Preparation
The SummarizedExperiment package from Bioconductor offers well-defined computational data structures for representing various types of omics experiment data (Morgan et al. 2020). Utilizing these data structures can significantly improve data analysis. POMA leverages SummarizedExperiment objects, enhancing the reusability of existing methods for this class and contributing to more robust and reproducible workflows.
The workflow begins with either loading or creating a SummarizedExperiment object. Typically, your data might be stored in separate matrices and/or data frames. The PomaCreateObject function simplifies this step by quickly building a SummarizedExperiment object for you.
# create an SummarizedExperiment object from two separated data frames
target <- readr::read_csv("your_target.csv")
features <- readr::read_csv("your_features.csv")
data <- PomaCreateObject(metadata = target, features = features)Alternatively, if your data is already in a SummarizedExperiment object, you can proceed directly to the pre-processing step. This vignette uses example data provided in POMA.
# load example data
data("st000336")st000336
> class: SummarizedExperiment
> dim: 31 57
> metadata(0):
> assays(1): ''
> rownames(31): x1_methylhistidine x3_methylhistidine ... pyruvate
> succinate
> rowData names(0):
> colnames(57): 1 2 ... 56 57
> colData names(2): group steroids3.2 Pre Processing
3.2.1 Missing Value Imputation
imputed <- st000336 %>%
PomaImpute(method = "knn", zeros_as_na = TRUE, remove_na = TRUE, cutoff = 20)
imputed
> class: SummarizedExperiment
> dim: 30 57
> metadata(0):
> assays(1): ''
> rownames(30): x1_methylhistidine x3_methylhistidine ... pyruvate
> succinate
> rowData names(0):
> colnames(57): 1 2 ... 56 57
> colData names(2): group steroids3.2.2 Normalization
normalized <- imputed %>%
PomaNorm(method = "log_pareto")
normalized
> class: SummarizedExperiment
> dim: 30 57
> metadata(0):
> assays(1): ''
> rownames(30): x1_methylhistidine x3_methylhistidine ... pyruvate
> succinate
> rowData names(0):
> colnames(57): 1 2 ... 56 57
> colData names(2): group steroids3.2.2.1 Normalization effect
PomaBoxplots(imputed, x = "samples") # data before normalization
PomaBoxplots(normalized, x = "samples") # data after normalization
PomaDensity(imputed, x = "features") # data before normalization
PomaDensity(normalized, x = "features") # data after normalization
3.2.3 Outlier Detection
PomaOutliers(normalized)$polygon_plot
pre_processed <- PomaOutliers(normalized)$data
pre_processed
> class: SummarizedExperiment
> dim: 30 52
> metadata(0):
> assays(1): ''
> rownames(30): X1_METHYLHISTIDINE X3_METHYLHISTIDINE ... PYRUVATE
> SUCCINATE
> rowData names(0):
> colnames(52): 1 2 ... 56 57
> colData names(2): group steroids# pre_processed %>%
# PomaUnivariate(method = "ttest") %>%
# magrittr::extract2("result")# imputed %>%
# PomaVolcano(pval = "adjusted", labels = TRUE)# pre_processed %>%
# PomaUnivariate(method = "mann") %>%
# magrittr::extract2("result")# PomaLimma(pre_processed, contrast = "Controls-DMD", adjust = "fdr")# poma_pca <- PomaMultivariate(pre_processed, method = "pca")# poma_pca$scoresplot +
# ggplot2::ggtitle("Scores Plot")# poma_plsda <- PomaMultivariate(pre_processed, method = "plsda")# poma_plsda$scoresplot +
# ggplot2::ggtitle("Scores Plot")# poma_plsda$errors_plsda_plot +
# ggplot2::ggtitle("Error Plot")# poma_cor <- PomaCorr(pre_processed, label_size = 8, coeff = 0.6)
# poma_cor$correlations
# poma_cor$corrplot
# poma_cor$graph# PomaCorr(pre_processed, corr_type = "glasso", coeff = 0.6)$graph# alpha = 1 for Lasso
# PomaLasso(pre_processed, alpha = 1, labels = TRUE)$coefficientPlot# poma_rf <- PomaRandForest(pre_processed, ntest = 10, nvar = 10)
# poma_rf$error_tree# poma_rf$confusionMatrix$table# poma_rf$MeanDecreaseGini_plot4 Session Information
sessionInfo()
> R version 4.4.0 beta (2024-04-15 r86425)
> Platform: x86_64-pc-linux-gnu
> Running under: Ubuntu 22.04.4 LTS
>
> Matrix products: default
> BLAS: /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
>
> locale:
> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
> [3] LC_TIME=en_GB LC_COLLATE=C
> [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: America/New_York
> tzcode source: system (glibc)
>
> attached base packages:
> [1] stats4 stats graphics grDevices utils datasets methods
> [8] base
>
> other attached packages:
> [1] magrittr_2.0.3 SummarizedExperiment_1.33.3
> [3] Biobase_2.63.1 GenomicRanges_1.55.4
> [5] GenomeInfoDb_1.39.14 IRanges_2.37.1
> [7] S4Vectors_0.41.6 BiocGenerics_0.49.1
> [9] MatrixGenerics_1.15.1 matrixStats_1.3.0
> [11] patchwork_1.2.0 ggtext_0.1.2
> [13] POMA_1.13.26 BiocStyle_2.31.0
>
> loaded via a namespace (and not attached):
> [1] tidyselect_1.2.1 viridisLite_0.4.2 dplyr_1.1.4
> [4] farver_2.1.1 fastmap_1.1.1 janitor_2.2.0
> [7] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4
> [10] cluster_2.1.6 compiler_4.4.0 rlang_1.1.3
> [13] sass_0.4.9 tools_4.4.0 utf8_1.2.4
> [16] yaml_2.3.8 knitr_1.46 S4Arrays_1.3.7
> [19] labeling_0.4.3 DelayedArray_0.29.9 xml2_1.3.6
> [22] abind_1.4-5 withr_3.0.0 purrr_1.0.2
> [25] grid_4.4.0 fansi_1.0.6 colorspace_2.1-0
> [28] ggplot2_3.5.0 scales_1.3.0 MASS_7.3-60.2
> [31] tinytex_0.50 cli_3.6.2 rmarkdown_2.26
> [34] vegan_2.6-4 crayon_1.5.2 generics_0.1.3
> [37] httr_1.4.7 commonmark_1.9.1 cachem_1.0.8
> [40] stringr_1.5.1 zlibbioc_1.49.3 splines_4.4.0
> [43] parallel_4.4.0 impute_1.77.0 BiocManager_1.30.22
> [46] XVector_0.43.1 vctrs_0.6.5 Matrix_1.7-0
> [49] jsonlite_1.8.8 bookdown_0.39 magick_2.8.3
> [52] tidyr_1.3.1 jquerylib_0.1.4 glue_1.7.0
> [55] lubridate_1.9.3 stringi_1.8.3 gtable_0.3.4
> [58] UCSC.utils_0.99.7 munsell_0.5.1 tibble_3.2.1
> [61] pillar_1.9.0 htmltools_0.5.8.1 GenomeInfoDbData_1.2.12
> [64] R6_2.5.1 evaluate_0.23 lattice_0.22-6
> [67] markdown_1.12 highr_0.10 gridtext_0.1.5
> [70] snakecase_0.11.1 bslib_0.7.0 Rcpp_1.0.12
> [73] SparseArray_1.3.5 nlme_3.1-164 permute_0.9-7
> [76] mgcv_1.9-1 xfun_0.43 pkgconfig_2.0.3References
Morgan, Martin, Valerie Obenchain, Jim Hester, and Hervé Pagès. 2020. SummarizedExperiment: SummarizedExperiment Container. https://bioconductor.org/packages/SummarizedExperiment.