File size: 3,833 Bytes
b4d2dc7 71f8eed b4d2dc7 71f8eed b4d2dc7 01d7f93 b4d2dc7 01d7f93 71f8eed 01d7f93 b4d2dc7 01d7f93 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | library(ArrayExpress)
library(illuminaHumanv3.db)
library(tidyverse)
library(here)
options(timeout = 1200)
outdir <- "data/dilgom/E-TABM-1036"
dir.create(outdir, recursive = TRUE, showWarnings = FALSE)
out_dilgom <- ArrayExpress::getAE("E-TABM-1036", path = outdir)
sdrf <- janitor::clean_names(read_tsv(out_dilgom$sdrf))
dilgom_metadata <- tibble(
sample_id = sdrf$source_name,
accession = "E-TABM-1036",
age_band = sdrf$characteristics_age,
sex = sdrf$characteristics_sex
)
expr_mat_colnames <- c("IlluminaID", str_split(readLines(out_dilgom$processedFiles, n = 1), "\t")[[1]][-1])
expr_mat_dilgom <- read_tsv(out_dilgom$processedFiles,
skip = 2,
col_names = expr_mat_colnames
)
dilgom_expr_long <- expr_mat_dilgom |>
pivot_longer(-IlluminaID, names_to = "sample_id", values_to = "value") |>
arrange(sample_id, IlluminaID)
# get probeset metadata
your_probes_v3 <- expr_mat_dilgom$IlluminaID
con_v3 <- illuminaHumanv3_dbconn()
extra_v3 <- DBI::dbGetQuery(con_v3, "SELECT * FROM ExtraInfo")
anno_v3 <- AnnotationDbi::select(
illuminaHumanv3.db,
keys = your_probes_v3,
columns = c("SYMBOL", "ENTREZID", "ENSEMBL", "GENENAME", "UNIPROT"),
keytype = "PROBEID"
)
anno_v3_collapsed <- anno_v3 |>
group_by(PROBEID) |>
summarise(
ENSEMBL = paste(unique(na.omit(ENSEMBL)), collapse = ";"),
ENTREZID = first(ENTREZID),
SYMBOL = first(SYMBOL),
GENENAME = first(GENENAME),
UNIPROT = paste(unique(na.omit(UNIPROT)), collapse = ";"),
.groups = "drop"
) |>
mutate(across(c(ENSEMBL, UNIPROT), ~ na_if(.x, "")))
extra_v3_selected <- extra_v3 |>
as_tibble() |>
filter(IlluminaID %in% your_probes_v3) |>
distinct(IlluminaID, .keep_all = TRUE) |>
select(
IlluminaID, ProbeQuality, CodingZone, ProbeSequence,
SecondMatches, OtherGenomicMatches, RepeatMask,
OverlappingSNP, GenomicLocation
)
dilgom_feature_metadata <- tibble(IlluminaID = your_probes_v3) |>
left_join(extra_v3_selected, by = "IlluminaID") |>
left_join(anno_v3_collapsed, by = c("IlluminaID" = "PROBEID"))
# Check
stopifnot(nrow(dilgom_feature_metadata) == length(your_probes_v3))
stopifnot(!any(duplicated(dilgom_feature_metadata$IlluminaID)))
# Expected row count
stopifnot(nrow(dilgom_expr_long) == nrow(expr_mat_dilgom) * (ncol(expr_mat_dilgom) - 1))
# Every sample in expression data has a metadata row, and vice versa
expr_samples_dilgom <- dilgom_expr_long |> distinct(sample_id)
meta_samples_dilgom <- dilgom_metadata |> distinct(sample_id)
nrow(anti_join(expr_samples_dilgom, meta_samples_dilgom, by = "sample_id")) # expect 0
nrow(anti_join(meta_samples_dilgom, expr_samples_dilgom, by = "sample_id")) # expect 0 -- no documented QC exclusions here, unlike GAinS
# No duplicate sample rows in metadata
stopifnot(!any(duplicated(dilgom_metadata$sample_id)))
# feature_metadata is one row per unique probe, no fan-out
stopifnot(nrow(dilgom_feature_metadata) == length(your_probes_v3))
stopifnot(!any(duplicated(dilgom_feature_metadata$IlluminaID)))
# Every probe in expression data has an annotation row
stopifnot(all(unique(dilgom_expr_long$IlluminaID) %in% dilgom_feature_metadata$IlluminaID))
# No unexpected NAs in join keys
stopifnot(!anyNA(dilgom_expr_long$IlluminaID), !anyNA(dilgom_expr_long$sample_id))
stopifnot(!anyNA(dilgom_metadata$sample_id))
# write out
# dir.create("data/dilgom/parquet", recursive = TRUE, showWarnings = FALSE)
arrow::write_parquet(dilgom_metadata, "~/projects/hf_sepsis_collection/dilgom/sample_metadata.parquet")
arrow::write_parquet(dilgom_feature_metadata, "~/projects/hf_sepsis_collection/dilgom/feature_metadata.parquet")
arrow::write_parquet(dilgom_expr_long, "~/projects/hf_sepsis_collection/dilgom/expression.parquet")
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