Liver disease patient cohort by Hampe et al. 2014
Last updated: 2021-02-27
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Knit directory: liver-disease-atlas/
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Introduction
Here we analysis a patient cohort covering patients with different etiologies of chronic liver disease generated by Hampe et al. 2014.
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-hampe14-misc"
output_path <- "output/human-hampe14-misc"
# 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() # Discarding in total 1 arrays: GSM1501017_A1891-04.CEL.gz
#> Loading required package: pd.hugene.1.1.st.v1
#> Loading required package: RSQLite
#> Loading required package: DBI
#> Platform design info loaded.
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#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501125_A1970-35.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501126_A1934-10.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501127_A1934-11.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501128_A1934-12.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501129_A1934-13.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501130_A1934-14.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501131_A1934-15.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501132_A1934-16.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501133_A1970-36.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501134_A1970-37.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501135_A1970-38.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501136_A1970-39.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501137_A1970-40.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501138_A1970-41.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501139_A1970-42.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501140_A1970-43.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501141_A1970-44.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501142_A1970-45.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501143_A1970-46.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501144_A1970-47.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501145_A1970-48.CEL.gz
#> Reading in : /Users/cholland/Google Drive/Projects/liver-disease-atlas/data/human-hampe14-misc/GSM1501146_A1970-49.CEL.gz
#> Background correcting...
#> OK
#> Normalizing... OK
#> Summarizing... OK
#> Extracting...
#> Estimates... OK
#> StdErrors... OK
#> Weights..... OK
#> Residuals... OK
#> Scale....... OK
#> Warning in ma_qc(.): Discarding in total 1 arrays: GSM1501017_A1891-04.CEL.gzNormalization 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 GSE61260.
# extract metadata from GEO
df <- getGEO("GSE61260")
#> Found 1 file(s)
#> GSE61260_series_matrix.txt.gz
#>
#> ── Column specification ────────────────────────────────────────────────────────
#> cols(
#> .default = col_character()
#> )
#> ℹ Use `spec()` for the full column specifications.
#> File stored at:
#> /var/folders/62/y2c8xnr53ln52nm6f4yryb_m0000gp/T//Rtmp7Ur7GE/GPL11532.soft
meta <- df$GSE61260_series_matrix.txt.gz %>%
pData() %>%
rownames_to_column("sample") %>%
as_tibble() %>%
select(sample,
group = "diseasestatus:ch1", gender = "Sex:ch1",
age = "age:ch1", bmi = "bmi:ch1"
) %>%
mutate(
group = str_to_lower(group),
group = str_remove(group, "healthy "),
group = str_remove(group, "normal "),
group = factor(group, levels = c(
"control", "obese", "nash", "nafld",
"pbc", "psc"
))
) %>%
mutate(
gender = as_factor(gender),
age = as.numeric(age),
bmi = as.numeric(bmi)
) %>%
filter(sample %in% colnames(expr))
#> select: renamed 4 variables (group, gender, age, bmi) and dropped 40 variables
#> mutate: converted 'group' from character to factor (0 new NA)
#> mutate: converted 'gender' 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)
#> filter: removed one row (1%), 133 rows remaining
# 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 etiologies. 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 4 columns (group, gender, age, bmi)
#> > rows only in x 0
#> > rows only in y ( 0)
#> > matched rows 133
#> > =====
#> > rows total 133
saveRDS(pca_result, here(output_path, "pca_result.rds"))
plot_pca(pca_result, feature = "group") +
my_theme()
| Version | Author | Date |
|---|---|---|
| 6dbc304 | christianholland | 2020-12-20 |
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,
nash_vs_ctrl = nash - control,
nafld_vs_ctrl = nafld - control,
pbc_vs_ctrl = pbc - control,
psc_vs_ctrl = psc - 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 48,235 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)
| Version | Author | Date |
|---|---|---|
| 6dbc304 | christianholland | 2020-12-20 |
Time spend to execute this analysis: 08:50 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