autonomics: platform-aware analysis
read + preprocess + analyze + plot cross-platform omics data
9 July 2024
Contents
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
1 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)
##
## Attaching package: 'autonomics'
## The following objects are masked from 'package:stats':
##
## biplot, loadings
## The following object is masked from 'package:base':
##
## beta
file <- system.file('extdata/billing19.rnacounts.txt', package = 'autonomics')
object <- read_rnaseq_counts(file = file, fit = 'limma', plot = TRUE, label = 'gene')
## Read /tmp/RtmpJJDNTi/Rinst134f727a111cd7/autonomics/extdata/billing19.rnacounts.txt
## Infer subgroup from sample_ids
## Infer subgroup from sample_ids
## Preprocess
## Keep 24/24 features: count >= 10 (~subgroup)
## counts: add 0.5
## cpm: tmm scale libsizes
## cpm
## voom: ~subgroup
## counts: rm 0.5
## Add PCA
## LinMod
## Code subgroup
## level
## coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
## Intercept 1 . . . . . . .
## E00.8-E00 -1 1 . . . . . .
## E01-E00 -1 . 1 . . . . .
## E02-E00 -1 . . 1 . . . .
## E05-E00 -1 . . . 1 . . .
## E15-E00 -1 . . . . 1 . .
## E30-E00 -1 . . . . . 1 .
## M00-E00 -1 . . . . . . 1
## lmFit(~subgroup, weights = assays(object)$weights)
## coefficient fit downfdr upfdr downp upp
## <fctr> <fctr> <int> <int> <int> <int>
## 1: Intercept limma 0 24 0 24
## 2: E00.8-E00 limma 1 2 1 4
## 3: E01-E00 limma 3 2 5 3
## 4: E02-E00 limma 2 2 3 4
## 5: E05-E00 limma 1 2 2 2
## 6: E15-E00 limma 8 9 8 10
## 7: E30-E00 limma 7 10 8 10
## 8: M00-E00 limma 6 7 6 7
## Plot summary
## Warning: ggrepel: 18 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
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)
## LinMod
## Code subgroup
## level
## coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
## Intercept 1 . . . . . . .
## E00.8-E00 -1 1 . . . . . .
## E01-E00 -1 . 1 . . . . .
## E02-E00 -1 . . 1 . . . .
## E05-E00 -1 . . . 1 . . .
## E15-E00 -1 . . . . 1 . .
## E30-E00 -1 . . . . . 1 .
## M00-E00 -1 . . . . . . 1
colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr upfdr
## 7 13
# log2cpm
object <- read_rnaseq_counts(file,
cpm = TRUE, voom = FALSE, fit = 'limma', verbose = FALSE, plot = FALSE)
## LinMod
## Code subgroup
## level
## coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
## Intercept 1 . . . . . . .
## E00.8-E00 -1 1 . . . . . .
## E01-E00 -1 . 1 . . . . .
## E02-E00 -1 . . 1 . . . .
## E05-E00 -1 . . . 1 . . .
## E15-E00 -1 . . . . 1 . .
## E30-E00 -1 . . . . . 1 .
## M00-E00 -1 . . . . . . 1
colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr upfdr
## 29 34
# log2cpm + voom
object <- read_rnaseq_counts(file, # log2 cpm + voom
cpm = TRUE, voom = TRUE, fit = 'limma', verbose = FALSE, plot = FALSE)
## LinMod
## Code subgroup
## level
## coefficient E00 E00.8 E01 E02 E05 E15 E30 M00
## Intercept 1 . . . . . . .
## E00.8-E00 -1 1 . . . . . .
## E01-E00 -1 . 1 . . . . .
## E02-E00 -1 . . 1 . . . .
## E05-E00 -1 . . . 1 . . .
## E15-E00 -1 . . . . 1 . .
## E30-E00 -1 . . . . . 1 .
## M00-E00 -1 . . . . . . 1
colSums(summarize_fit(fdt(object), 'limma')[-1, c(3,4)])
## downfdr upfdr
## 28 342 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)
## Read proteingroups /tmp/RtmpJJDNTi/Rinst134f727a111cd7/autonomics/extdata/fukuda20.proteingroups.txt
## contaminanthdrs /tmp/RtmpJJDNTi/Rinst134f727a111cd7/autonomics/extdata/contaminants.tsv
## maxquanthdrs
## Annotate Uncollapse 20 proIds into 32 proteins
## Drop REV_ 32 proteins
## Annotate 0 using uniprothdrs
## 1 using contaminanthdrs
## 29 using maxquanthdrs
## 0 using uniprotrestapi
## Filter 32 proteins
## within proId 31 proteins swissprot > trembl
## 25 proteins fullseq > fragment
## 20 proteins protein > transcript > homolog > prediction > uncertain
## Add REV__ 20 proteins
## Recollapse 20 proIds
## SumExp
## Replace 0->NA for 27/120 values (in 11/20 features of 6/6 samples)
## Standardize snames: LFQ intensity 30dpt.R1 -> 30dpt.R1
## Infer subgroup from sample_ids
## Retain 19/20 features: ~reverse == ""
## Retain 18/19 features: ~contaminant == ""
## Add PCA
## LinMod
## Code subgroup
## level
## coefficient X30dpt Adult
## Intercept 1 .
## Adult-X30dpt -1 1
## lmFit(~subgroup)
## coefficient fit downfdr upfdr downp upp
## <fctr> <fctr> <int> <int> <int> <int>
## 1: Intercept limma 0 18 0 18
## 2: Adult-X30dpt limma 2 2 3 2
## Plot summary
## Warning: ggrepel: 17 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps