NASH patient cohort by Hampe et al. 2013
Last updated: 2021-02-28
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Knit directory: liver-disease-atlas/
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Introduction
Here we analysis a patient cohort covering patients with mild and advanced NAFLD generated by Hampe et al. 2013.
Libraries and sources
These libraries and sources are used for this analysis.
library(hugene11sttranscriptcluster.db)
library(tidyverse)
library(tidylog)
library(here)
library(oligo)
library(annotate)
library(GEOquery)
library(limma)
library(biobroom)
library(AachenColorPalette)
library(cowplot)
library(lemon)
options("tidylog.display" = list(print))
source(here("code/utils-microarray.R"))
source(here("code/utils-utils.R"))
source(here("code/utils-plots.R"))Definition of global variables that are used throughout this analysis.
# i/o
data_path <- "data/human-hampe13-nash"
output_path <- "output/human-hampe13-nash"
# graphical parameters
# fontsize
fz <- 9Data processing
Load .CEL files and quality control
The array quality is controlled based on the relative log expression values (RLE) and the normalized unscaled standard errors (NUSE).
# load cel files and check quality
platforms <- readRDS(here("data/annotation/platforms.rds"))
raw_eset <- list.celfiles(here(data_path), listGzipped = T, full.names = T) %>%
read.celfiles() %>%
ma_qc()
#> Loading required package: pd.hugene.1.1.st.v1
#> Loading required package: RSQLite
#> Loading required package: DBI
#> Platform design info loaded.
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe13-nash/GSM1178970_A1359-01.CEL.gz
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#> Background correcting...
#> OK
#> Normalizing... OK
#> Summarizing... OK
#> Extracting...
#> Estimates... OK
#> StdErrors... OK
#> Weights..... OK
#> Residuals... OK
#> Scale....... OK
#> All arrays passed quality control.Normalization and probe annotation
Probe intensities are normalized with the rma() function. Probes are annotated with HGNC symbols.
eset <- rma(raw_eset)
#> Background correcting
#> Normalizing
#> Calculating Expression
# annotate microarray probes with hgnc symbols
expr <- ma_annotate(eset, platforms)
# overwrite column names GSMxxx_xxx-xx.CEL.gz -> GSMxxx
colnames(expr) <- str_extract(colnames(expr), "GSM[0-9]*")
# save normalized expression
saveRDS(expr, here(output_path, "normalized_expression.rds"))Build meta data
Meta information are downloaded from GEO with the accession ID GSE48452.
# extract metadata from GEO
df <- getGEO("GSE48452")
#> Found 1 file(s)
#> GSE48452_series_matrix.txt.gz
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_double()
#> )
#> ℹ Use `spec()` for the full column specifications.
#> File stored at:
#> /var/folders/62/y2c8xnr53ln52nm6f4yryb_m0000gp/T//RtmpU7xIxv/GPL11532.soft
meta <- df$GSE48452_series_matrix.txt.gz %>%
pData() %>%
rownames_to_column("sample") %>%
as_tibble() %>%
select(sample, group = "group:ch1", gender = "Sex:ch1", inflammation = "inflammation:ch1", fibrosis = "fibrosis:ch1", age = "age:ch1", bmi = "bmi:ch1", nas = "nas:ch1") %>%
mutate(
group = str_to_lower(group),
group = str_remove(group, "healthy "),
group = factor(group, levels = c("control", "obese", "steatosis", "nash"))
) %>%
mutate(
fibrosis = fct_explicit_na(fibrosis),
gender = as_factor(gender),
inflammation = fct_explicit_na(inflammation),
nas = fct_explicit_na(nas),
age = as.numeric(age),
bmi = as.numeric(bmi)
)
#> select: renamed 7 variables (group, gender, inflammation, fibrosis, age, …) and dropped 56 variables
#> mutate: converted 'group' from character to factor (0 new NA)
#> mutate: converted 'gender' from character to factor (0 new NA)
#> converted 'inflammation' from character to factor (0 new NA)
#> converted 'fibrosis' from character to factor (0 new NA)
#> converted 'age' from character to double (0 new NA)
#> converted 'bmi' from character to double (0 new NA)
#> converted 'nas' from character to factor (0 new NA)
# save meta data
saveRDS(meta, here(output_path, "meta_data.rds"))Exploratory analysis
PCA of normalized data
PCA plot of normalized expression data contextualized based on etiology. Only the top 1000 most variable genes are used as features.
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(output_path, "meta_data.rds"))
pca_result <- do_pca(expr, meta, top_n_var_genes = 1000)
#> left_join: added 7 columns (group, gender, inflammation, fibrosis, age, …)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 73
#> > ====
#> > rows total 73
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "group") +
my_theme()
Differential gene expression analysis
Running limma
Differential gene expression analysis via limma with the aim to identify the signature of different etiologies
# load expression and meta data
expr <- readRDS(here(output_path, "normalized_expression.rds"))
meta <- readRDS(here(output_path, "meta_data.rds"))
stopifnot(colnames(expr) == meta$sample)
# build design matrix
design <- model.matrix(~ 0 + group, data = meta)
rownames(design) <- meta$sample
colnames(design) <- levels(meta$group)
# define contrasts
contrasts <- makeContrasts(
obese_vs_ctrl = obese - control,
steatosis_vs_ctrl = steatosis - control,
nash_vs_ctrl = nash - control,
levels = design
)
limma_result <- run_limma(expr, design, contrasts) %>%
assign_deg()
#> Warning: `tbl_df()` is deprecated as of dplyr 1.0.0.
#> Please use `tibble::as_tibble()` instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_warnings()` to see where this warning was generated.
#> select: renamed 3 variables (contrast, logFC, pval) and dropped one variable
#> group_by: one grouping variable (contrast)
#> mutate (grouped): new variable 'fdr' (double) with 15,554 unique values and 0% NA
#> ungroup: no grouping variables
#> mutate: new variable 'regulation' (character) with 3 unique values and 0% NA
#> mutate: converted 'regulation' from character to factor (0 new NA)
deg_df <- limma_result %>%
mutate(
contrast = fct_inorder(contrast),
contrast_reference = "control"
)
#> mutate: changed 0 values (0%) of 'contrast' (0 new NA)
#> new variable 'contrast_reference' (character) with one unique value and 0% NA
saveRDS(deg_df, here(output_path, "limma_result.rds"))Volcano plots
Volcano plots visualizing the signature of different etiologies.
df <- readRDS(here(output_path, "limma_result.rds"))
df %>%
plot_volcano() +
my_theme(grid = "y", fsize = fz)
#> rename: renamed one variable (p)
Time spend to execute this analysis: 04:52 minutes.
sessionInfo()
#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Mojave 10.14.5
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> attached base packages:
#> [1] parallel stats4 stats graphics grDevices datasets utils
#> [8] methods base
#>
#> other attached packages:
#> [1] pd.hugene.1.1.st.v1_3.14.1 DBI_1.1.0
#> [3] RSQLite_2.2.1 lemon_0.4.5
#> [5] cowplot_1.1.0 AachenColorPalette_1.1.2
#> [7] biobroom_1.20.0 broom_0.7.3
#> [9] limma_3.44.3 GEOquery_2.56.0
#> [11] annotate_1.66.0 XML_3.99-0.5
#> [13] oligo_1.52.1 Biostrings_2.56.0
#> [15] XVector_0.28.0 oligoClasses_1.50.4
#> [17] here_1.0.1 tidylog_1.0.2
#> [19] forcats_0.5.0 stringr_1.4.0
#> [21] dplyr_1.0.2 purrr_0.3.4
#> [23] readr_1.4.0 tidyr_1.1.2
#> [25] tibble_3.0.4 ggplot2_3.3.2
#> [27] tidyverse_1.3.0 hugene11sttranscriptcluster.db_8.7.0
#> [29] org.Hs.eg.db_3.11.4 AnnotationDbi_1.50.3
#> [31] IRanges_2.22.2 S4Vectors_0.26.1
#> [33] Biobase_2.48.0 BiocGenerics_0.34.0
#> [35] workflowr_1.6.2
#>
#> loaded via a namespace (and not attached):
#> [1] colorspace_2.0-0 ellipsis_0.3.1
#> [3] rprojroot_2.0.2 GenomicRanges_1.40.0
#> [5] fs_1.5.0 rstudioapi_0.13
#> [7] farver_2.0.3 affyio_1.58.0
#> [9] bit64_4.0.5 fansi_0.4.1
#> [11] lubridate_1.7.9.2 xml2_1.3.2
#> [13] codetools_0.2-16 splines_4.0.2
#> [15] knitr_1.30 jsonlite_1.7.2
#> [17] dbplyr_2.0.0 BiocManager_1.30.10
#> [19] compiler_4.0.2 httr_1.4.2
#> [21] backports_1.2.1 assertthat_0.2.1
#> [23] Matrix_1.2-18 cli_2.2.0
#> [25] later_1.1.0.1 htmltools_0.5.0
#> [27] tools_4.0.2 gtable_0.3.0
#> [29] glue_1.4.2 GenomeInfoDbData_1.2.3
#> [31] affxparser_1.60.0 Rcpp_1.0.5
#> [33] cellranger_1.1.0 vctrs_0.3.6
#> [35] preprocessCore_1.50.0 iterators_1.0.13
#> [37] xfun_0.19 rvest_0.3.6
#> [39] lifecycle_0.2.0 renv_0.12.3
#> [41] zlibbioc_1.34.0 scales_1.1.1
#> [43] clisymbols_1.2.0 hms_0.5.3
#> [45] promises_1.1.1 SummarizedExperiment_1.18.2
#> [47] curl_4.3 yaml_2.2.1
#> [49] gridExtra_2.3 memoise_1.1.0
#> [51] stringi_1.5.3 foreach_1.5.1
#> [53] GenomeInfoDb_1.24.2 rlang_0.4.9
#> [55] pkgconfig_2.0.3 bitops_1.0-6
#> [57] matrixStats_0.57.0 evaluate_0.14
#> [59] lattice_0.20-41 labeling_0.4.2
#> [61] bit_4.0.4 tidyselect_1.1.0
#> [63] plyr_1.8.6 magrittr_2.0.1
#> [65] R6_2.5.0 generics_0.1.0
#> [67] DelayedArray_0.14.1 pillar_1.4.7
#> [69] haven_2.3.1 whisker_0.4
#> [71] withr_2.3.0 RCurl_1.98-1.2
#> [73] modelr_0.1.8 crayon_1.3.4
#> [75] rmarkdown_2.6 grid_4.0.2
#> [77] readxl_1.3.1 blob_1.2.1
#> [79] git2r_0.27.1 reprex_0.3.0
#> [81] digest_0.6.27 xtable_1.8-4
#> [83] ff_4.0.4 httpuv_1.5.4
#> [85] munsell_0.5.0