In bhagwataditya/importomics: Unified statistal Modeling of Omics Data

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Abstract {-}

autonomics is created to make cross-platform omics analysis intuitive and enjoyable. It contains import + preprocess + analyze + visualize one-liners for RNAseq, MS proteomics, SOMAscan and Metabolon platforms and a generic importer for data from any rectangular omics file. With a focus on not only automation but also customization, these importers have a flexible formula/ block/contrastdefs interface which allows to define any statistical formula, fit any general linear model, quantify any contrast, and use random effects or precision weights if required, building on top of the powerful limma platform for statistical analysis. It also offers exquisite support for analyzing complex designs such as the innovative contrastogram visualization to unravel and summarize the differential expression structure in complex designs. By autonomating repetitive tasks, generifying common steps, and intuifying complex designs, it makes cross-platform omics data analysis a fun experience. Try it and enjoy :-).

Autonomics offers import + preprocess + analyze + visualize one-liners for RNAseq, MS proteomics, SOMAscan and Metabolon platforms, as well a generic importer for data from any rectangular omics file. We will discuss each of these in more detail in the following sections, but all of them follow the following general steps:

• read
  • exprs, features, samples
  • extract subgroups from sampleids / samplefile
  • wrap these into a SummarizedExperiment
• preprocess
  • normalize
  • log2 transform
  • impute in a platform-aware fashion
  • filter
• analyze
  • plot sample distributions
  • plot detections (absent/present)
  • plot pca biplots
  • fit the linear model ~0+subgroup
  • quantify contrasts between subsequent factor levels and plot volcanoes

RNAseq

Autonomics provides ready-to-use importers for both count as well as BAM files, which read / preprocess / analyze RNAseq data. Specific to RNAseq is counting reads using Rsubread::featureCounts (for read_rna_bams)), normalizing for library composition (cpm) or gene length (tpm), estimating voom precision weights, and using these in linear modeling. Let's illustrate both of these on example datasets:

read_rnaseq_counts {-}

Billing et al. (2019) studied the differentiation of embryonic (E00) into mesenchymal stem cells (E00.8, E01, E02, E05, E15, E30), with similar properties as bone-marrow mesenchymal stem cells (M00). Importing the RNAseq counts:

require(autonomics, quietly = TRUE)
file <- system.file('extdata/billing19.rnacounts.txt', package = 'autonomics')
object <-  read_rnaseq_counts(file = file, fit = 'limma', plot = TRUE, label = 'gene')

read_rnaseq_bams {-}

Billing et al. (2016) compared three types of stem cells: embryonic (E), embryonic differentiated into mesenchymal (EM), and bone-marrow mesenchymal (BM). Importing a downsized version of the RNAseq BAM files (with only a subset of all reads):

# not run to avoid issues with R CMD CHECK 
if (requireNamespace('Rsubread')){
  object <- read_rnaseq_bams(
                dir    = download_data('billing16.bam.zip'), 
                paired = TRUE, 
                genome = 'hg38', 
                pca    = TRUE, 
                fit   = 'limma', 
                plot   = TRUE)
}

cpm/tmm/voom effects on power {-}

Proper preprocessing leads to increased power:

  file <- system.file('extdata/billing19.rnacounts.txt', package = 'autonomics')
# log2counts
  object <- read_rnaseq_counts(file, 
       cpm = FALSE, voom = FALSE, fit = 'limma', verbose = FALSE, plot = FALSE)
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
# log2cpm
  object <- read_rnaseq_counts(file,
       cpm = TRUE,  voom = FALSE, fit = 'limma', verbose = FALSE, plot = FALSE)
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
# log2cpm + voom
  object <- read_rnaseq_counts(file,  # log2 cpm + voom
       cpm = TRUE,  voom = TRUE,  fit = 'limma', verbose = FALSE, plot = FALSE)
  colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])

Proteingroups/phosphosites

A popular approach in (DDA) MS proteomics data analysis is to use MaxQuant to produce proteinGroups.txt and phospho (STY)Sites.txt files. These can be imported using read_proteingroups / read_phosphosites, which :

• Read
  • quantification cols: LFQ intensities, Normalized Ratios, or Reporter intensities
  • feature cols: id, Majority protein IDs, Protein names, Gene names, Contaminant, Potential contaminant, Reverse
  • subgroups from sampleids / samplefile
• Preprocess
  • Convert 0 -> NA (they represent lack of detection)
  • Log2 transform
  • Rm contaminants/reverse/unreplicated features
  • Impute consistent NAs with values drawn from a half-normal distribution around zero
  • For phosphosites: compute occupancies by subtracting protein expressions
• Analyze
  • plot sample distributions
  • plot detections
  • pca biplot
  • Fit a linear model
  • Quantify contrasts between subsequent factor levels and plot volcanoes

LFQ intensities {-}

An LFQ intensities example is the Fukuda et al. (2020) dataset, which compares the proteome of 30d zebrafish juveniles versus adults using label-free quantification. In the volcano plot measured values are shown with circles, imputed values as triangles.

file <- system.file('extdata/fukuda20.proteingroups.txt', package = 'autonomics')
object <- read_maxquant_proteingroups(file = file, plot = TRUE)

Normalized ratios {-}

Normalized ratios were used in the proteomics profiling of the earlier described Billing et al. (2019) study, which examined the differentiation of embryonic stem cells (E00) into mesenchymal stem cells (E01,E02, E05, E15, E30), and compared these to bone-marrow derived mesenchymal stem cells (M00). The proteomics profiling experiment was performed using MS1 labeling: a light (L) labeled internal standard was created by mixing all samples in equal amounts, the subsequent samples were either medium (M) or heavy (H) labeled. Not all label combinations were of interest, and the select_subgroups argument allows to specifies which subgroup combinations to retain:

file <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
subgroups <- c('E00_STD', 'E01_STD', 'E02_STD', 'E05_STD', 'E15_STD', 'E30_STD', 'M00_STD')
object <- read_maxquant_proteingroups(file = file, subgroups = subgroups, plot = TRUE)

The phosphosites can be read in a similar fashion:

fosfile <- system.file('extdata/billing19.phosphosites.txt',  package = 'autonomics')
profile <- system.file('extdata/billing19.proteingroups.txt', package = 'autonomics')
subgroups <- c('E00_STD', 'E01_STD', 'E02_STD', 'E05_STD', 'E15_STD', 'E30_STD', 'M00_STD')
object <- read_maxquant_phosphosites(fosfile = fosfile, profile = profile, subgroups = subgroups, plot = TRUE)

Metabolon

Metabolon delivers metabolomics results in the form of an xlsx file, with values of interest likely in the (second) OrigScale sheet. Features are laid out in rows, samples in columns, and additional feature/sample data are self-contained in the file. Metabolon data can be read/analyzed with the autonomics function read_metabolon, as illustrated below on the dataset of Halama and and Atkin (2018), who investigates the effects of hypoglycemia on a number of subjects (SUB) at four different time points (t0, t1, t2, t3):

file <- system.file('extdata/atkin.metabolon.xlsx', package = 'autonomics')
object <- read_metabolon(file,  subgroupvar = 'subgroup', block = 'Subject', plot = TRUE)

Somascan

Somascan results are returned in a txt file with extension .adat. Features are laid out in columns and samples in rows. Rectangles with feature/sample data are also contained in the file. The autonomics function read_somascan reads/analyzes SOMAscan files, additionally filtering samples/features for quality and type. This is illustrated on the above described dataset from Halama and and Atkin (2018), who investigated the effects of hypoglycemia on a number of subjects (SUB) at four different time points (t0, t1, t2, t3):

file <- system.file('extdata/atkin.somascan.adat', package = 'autonomics')
object <- read_somascan(file, block = 'Subject', plot = TRUE)

SessionInfo {-}

sessionInfo()

References {-}

A M Billing, S S Dib, A M Bhagwat, I T da Silva, R D Drummond, S Hayat, R Al-Mismar, H Ben-Hamidane, N Goswami, K Engholm-Keller, M R Larsen, K Suhre, A Rafii, J Graummann (2019). Mol Cell Proteomics. 18(10):1950-1966. doi: 10.1074/mcp.RA119.001356.

A M Billing, H Ben Hamidane, S S Dib, R J Cotton, A M Bhagwat, P Kumar, S Hayat, N A Yousri, N Goswami, K Suhre, A Rafii, J Graumann (2016). Comprehensive transcriptomics and proteomics characterization of human mesenchymal stem cells reveals source specific cellular markers. Sci Rep. 9;6:21507. doi: 10.1038/srep21507.

R Fukuda, R Marin-Juez, H El-Sammak, A Beisaw, R Ramadass, C Kuenne, S Guenther, A Konzer, A M Bhagwat, J Graumann, D YR Stainier (2020). EMBO Rep. 21(8): e49752. doi: 10.15252/embr.201949752

A Halama, M Kulinski, S Dib, S B Zaghlool, K S Siveen, A Iskandarani, J Zierer 3, K S Prabhu, N J Satheesh, A M Bhagwat, S Uddin, G Kastenmüller, O Elemento, S S Gross, K Suhre (2018). Accelerated lipid catabolism and autophagy are cancer survival mechanisms under inhibited glutaminolysis. Cancer Lett., 430:133-147. doi:10.1016/j.canlet.2018.05.017

A Halama, H Kahal, A M Bhagwat, J Zierer, T Sathyapalan, J Graumann, K Suhre, S L Atkin (2018). Metabolic and proteomics signatures of hypoglycaemia in type 2 diabetes. Diabetes, obesity and metabolism, https://doi.org/10.1111/dom.13602

bhagwataditya/importomics documentation built on Oct. 29, 2024, 3:19 p.m.