Getting started with seqout

Query studies

SeqoutSearch() searches all archives with free text, filters, or both.

SeqoutSearch("cervical cancer single cell", db = "geo", limit = 5)

Query samples (cohorts)

SampleSearch() searches individual samples, filtering on harmonized metadata such as tissue, disease, and age across studies.

liver <- SampleSearch(
  organism = "Homo sapiens",
  tissue = "liver",
  age_min_years = 50,
  limit = 5
)
liver[, c("sample", "study_accession", "tissue", "disease", "sex", "age")]

For query operators and filters, see Search.

Everything pipes

Each function takes its subject first (an accession, a seqout_counts table, a list of cohort filters, or the connection when the call has no other subject), so calls chain with R’s native pipe.

# A PubMed ID or a DOI, told apart by the value, to the datasets that cite it
"34764296" |>
  Paper() |>
  nrow()

list(tissue = "liver", organism = "Homo sapiens") |>
  SampleSearch(limit = 3) |>
  subset(select = c(sample, study_accession, disease))

con is the last argument everywhere else.

Harmonized sample attributes

The enriched field holds harmonized sample-level and cell-level labels:

d$enriched[, c("sample", "tissue", "disease", "assay")]

For metadata fields, see Metadata.

List counts files

SeqoutListCounts() lists the matrix units for a GEO accession without downloading any matrix files. A unit is the smallest file group that seqout can parse as one matrix, such as a 10x triplet, .h5, .h5ad, or .rds file.

counts <- SeqoutListCounts("GSE297547")
m <- counts

nrow(m)
head(m[, c("unit", "sample", "format", "preferred", "has_metadata")], 4)

Load a matrix

SeqoutMatrix() reads one unit into a list of X, obs, and var.

rna <- SeqoutMatrix(counts, sample = "GSM8994520")
dim(rna$X)

Convert to Seurat

Seqout2Seurat() accepts a matrix unit, GSM accession, GSE accession, or SeqoutListCounts() table. Extra arguments go to CreateSeuratObject().

obj <- Seqout2Seurat("GSE297547", sample = "GSM8994520", min.cells = 3, min.features = 200)

For unit selection and Seurat workflows, see Counts.

Convert old gene IDs and repair Excel errors

SeqoutGeneNames() uses genevintage to select an annotation and convert IDs. SeqoutCorrectNames() repairs Excel-damaged symbols against a specified reference. Both preserve expression values and keep an audit; SeqoutGeneReport() reads it.

# GSE52529: original Ensembl IDs, FPKM not raw counts
old <- SeqoutListCounts("GSE52529") |>
  SeqoutMatrix(sample = "GSE52529_fpkm_matrix.txt.gz")
named <- old |> SeqoutGeneNames()
named$gene_annotation[[1]]$annotation

# GSE101942: verified Excel-damaged gene names
damaged <- SeqoutListCounts("GSE101942") |>
  SeqoutMatrix(sample = "GSE101942_IPSC_rawCounts.txt.gz")
repaired <- damaged |> SeqoutCorrectNames(species = "human", release = 116)
report <- SeqoutGeneReport(repaired)
report[report$status != "unchanged", ]

genevintage selects GENCODE 17 on GRCh37 for GSE52529 without a version argument. See Gene names: old identifiers and Excel errors for runnable excerpts and a comparison of historical and newer symbols.