Streaming data into the demultiplexer
13 January 2026
Package
posDemux 0.99.4
1 Why streaming?
Given that sequence files can consist of gigabytes of data,
storing everything in memory at once can prove to be too much for the computer.
Fortunately, for demultiplexing of barcodes, we can demultiplex the barcodes
independently of each other.
In theory, we could read the entries from a FASTQ file one by one,
and for each of them call the demultiplexer
and output the barcode assignment to a file.
However, due to overhead in file operations and the R interpreter,
the most efficient option is to process the files in chunks
of a predetermined size.
If you have not read the main package vignette
yet (vignette("demultiplexing")), please do so before proceeding.
1.1 Which chunk size to use?
The chunk size should be large enough for the constant overhead of initiating a
file read and interpreting the required R instructions to be neglectable.
Still, the chunk size should be small enough to only make a
modest memory footprint. In practice, with modern computers
having gigabytes of RAM, we find a chunk size of \(2\cdot10^5\) reads to be a
reasonable choice.
The example datasets used in this vignette are far too small
to require streaming
(including a dataset of realistic size would make
the package exceed the maximum package size),
so for purely demonstrational purposes, we choose a chunk size of \(10^4\) reads.
1.2 Overview of the streaming API
The streaming API of posDemux is focused around two user-supplied functions:
- The loader: This function is responsible for
grabbing a chunk of the input file
of the sequences and returning a
XStringSetobject of the sequences. Also, this function determines when to terminate the streaming, typically done when there are no sequences left. - The archiver: This function is responsible for writing the results of the demultiplexer of a chunk to file, typically in the form of a barcode table.
Once the loader and archiver are specified and combined with the demultiplexer,
they can generate the barcode table chunk by chunk.
However, we still want to generate the summary statistics and frequency table
as if all the reads were demultiplexed simultaneously.
If the chunks are demultiplexed independently,
create_summary_res() and create_freq_table() don’t work as intended.
That is where the core function of the streaming API;
streaming_demultiplex() comes into play.
It accepts the loader and the archiver as callback functions,
communicates with the demultiplexer,
and does the bookkeeping to construct
the summary statistics and frequency table.
Hence, for each chunk, the loader provides the sequences to demultiplex,
the streaming logic feeds these reads into the demultiplexer,
updates is record of observed barcodes
and provides the results to the archiver which writes to the barcode table.
This process is illustrated in Figure 1.
For communicating between the loader and archiver, a user-defined state object
is passed through streaming_demultiplex().
The initial value of this object is provided to streaming_demultiplex()
as the argument state_init.
Figure 1: Components of the streaming API
2 Streaming using default callbacks
Although the option of specifying the loader and
archiver gives great flexibility,
it does so at the cost of convenience as the loader and archiver can
be challenging to write.
For this reason, posDemux provides a default set of
streaming callbacks available through streaming_callbacks().
Actually, this is a function factory,
which returns a list of the following elements:
state_init: The initial state to provide tostreaming_demultiplex()loader: Loader functionarchiver: Archiver function
2.1 Assumptions
The callbacks are designed to be useful for a typical use case where:
- The reads are contained as a single FASTQ file on disk.
- The barcode table contains columns with the read names (
read), sequences of all payloads (eg.UMI) and the barcode assignments (eg.BC3,BC2,BC1). - Read names with spaces are truncated by the loader such that only the part of the read name before the space is kept. This is done because the information preceding the space is typically enough to ambiguously identify the read, while the information after the space usually contains irrelevant auxiliary information.
- Progress of how many reads have been processed can optionally be displayed by the archiver for each chunk.
If this does not fill your needs, please refer to the section Streaming using custom callbacks.
2.2 Obtaining the callbacks
As said, streaming_callbacks() is a function factory,
we must specify the file path to the FASTQ input file,
and the barcode table output file for it to generate useful callbacks.
In addition, the chunk size is regulated through the callbacks.
We use the same dataset as in the main package
vignette (vignette("demultiplexing")) and obtain the callbacks as follows:
library(posDemux)
input_fastq <- system.file("extdata",
"PETRI-seq_forward_reads.fq.gz",
package = "posDemux"
)
output_barcode_table <- tempfile(
pattern = "barcode_table",
fileext = ".txt"
)
default_callbacks <- streaming_callbacks(
input_file = input_fastq,
output_table_file = output_barcode_table,
chunk_size = 1e+4,
verbose = TRUE
)2.3 Running the demultiplexing
With the callbacks in place, we specify the barcodes, and the segments and feed them into the streaming framework:
suppressPackageStartupMessages({
library(purrr)
library(Biostrings)
})
barcode_files <- system.file(
"extdata/PETRI-seq_barcodes",
c(bc1 = "bc1.fa", bc2 = "bc2.fa", bc3 = "bc3.fa"),
package = "posDemux"
)
names(barcode_files) <- paste0("bc", 1L:3L)
barcode_index <- map(barcode_files, readDNAStringSet)
barcodes <- barcode_index[c("bc3", "bc2", "bc1")]
sequence_annotation <- c(UMI = "P", "B", "A", "B", "A", "B", "A")
segment_lengths <- c(7L, 7L, 15L, 7L, 14L, 7L, NA_integer_)
streaming_summary_res <- streaming_demultiplex(
state_init = default_callbacks$state_init,
loader = default_callbacks$loader,
archiver = default_callbacks$archiver,
barcodes = barcodes,
allowed_mismatches = 1L,
segments = sequence_annotation,
segment_lengths = segment_lengths
)
#> Tue Jan 13 19:27:42 2026 => Initializing FASTQ stream and output table
#> Tue Jan 13 19:27:49 2026 => Processed 10000 reads, successfully demultiplexed 9117 reads so far...
#> Tue Jan 13 19:27:50 2026 => Processed 20000 reads, successfully demultiplexed 18259 reads so far...
#> Tue Jan 13 19:27:50 2026 => Processed 30000 reads, successfully demultiplexed 27362 reads so far...
#> Tue Jan 13 19:27:51 2026 => Processed 40000 reads, successfully demultiplexed 36487 reads so far...
#> Tue Jan 13 19:27:51 2026 => Processed 50000 reads, successfully demultiplexed 45629 reads so far...
#> Tue Jan 13 19:27:51 2026 => Processed 56895 reads, successfully demultiplexed 51906 reads so far...
#> Tue Jan 13 19:27:51 2026 => Done demultiplexing2.4 Displaying the result from streaming
From the return value of streaming_demultiplex(),
we obtain the frequency table
head(streaming_summary_res$freq_table)
#> bc3 bc2 bc1 frequency cumulative_frequency fraction cumulative_fraction
#> 1 bc3_37 bc2_4 bc1_30 270 270 0.005201711 0.005201711
#> 2 bc3_45 bc2_36 bc1_65 246 516 0.004739336 0.009941047
#> 3 bc3_25 bc2_34 bc1_25 235 751 0.004527415 0.014468462
#> 4 bc3_40 bc2_62 bc1_42 221 972 0.004257697 0.018726159
#> 5 bc3_69 bc2_95 bc1_37 219 1191 0.004219165 0.022945324
#> 6 bc3_56 bc2_94 bc1_29 217 1408 0.004180634 0.027125958and summary printout:
streaming_summary_res$summary_res
#> Total number of reads: 56895
#> Number of reads failing to demultiplex: 4989 (8.77%)
#> Observed number of unique barcode combinations:978
#> Number of possible barcode combinations: 884736
#> Estimated number of features: 978.5
#> Observed feature to barcode ratio: 0.001105
#> Corrected feature to barcode ratio: 0.001106
#> Estimated number of observed barcode combinations
#> corresponding to more than one feature: 0.5 (0.06%)
#> Number of barcode sets: 3
#> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#> Barcode set: bc3
#> Barcode width: 7
#> Number of possible barcodes: 96
#> Number of allowed mismatches: 1
#> Number of reads with 0 mismatches: 51923 (91.26%)
#> Number of reads with 1 mismatches: 671 (1.18%)
#> Number of reads above mismatch threshold: 4301 (7.56%)
#> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#> Barcode set: bc2
#> Barcode width: 7
#> Number of possible barcodes: 96
#> Number of allowed mismatches: 1
#> Number of reads with 0 mismatches: 51918 (91.25%)
#> Number of reads with 1 mismatches: 567 (1%)
#> Number of reads above mismatch threshold: 4410 (7.75%)
#> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
#> Barcode set: bc1
#> Barcode width: 7
#> Number of possible barcodes: 96
#> Number of allowed mismatches: 1
#> Number of reads with 0 mismatches: 51930 (91.27%)
#> Number of reads with 1 mismatches: 642 (1.13%)
#> Number of reads above mismatch threshold: 4323 (7.6%)
#> - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -3 Streaming the barcode table
3.1 Considerations about size of tables
The frequency table from a realistic PETRI-seq dataset is relatively small (just a few megabytes) and should therefore not pose any substantial memory usage problems. On the other hand, the barcode table will have one row for each read processed, making its memory footprint closer to the size of the original FASTQ-file. To this end, the default archiver writes to the barcode table on disk for each chunk, thus avoiding keeping the table in memory. However, the barcode table will be required by some downstream step of the pipeline. Typically, this will be frequency filtering. The good news is that the cutoff determination process only needs the light frequency table. The bad news is that the full barcode table is required when determining which reads to keep after the cutoff is selected.
If we want to avoid heavy load on RAM, we must stream the barcode table in chunks, determine for each chunk which reads to keep, and appending the results to another file. ## Subsetting the frequency table
We begin this example by subsetting the frequency table.
In order not to repeat ourselves,
we refer to the main package vignette (vignette("demultiplexing"))
for the considerations behind this process.
freq_table <- streaming_summary_res$freq_table
bc_cutoff <- 500L
selected_freq_table <- freq_table[seq_len(bc_cutoff), ]3.2 Writing to database
We typically want to create a copy of the barcode table including only
those reads which are selected by the barcode frequency
and thus match a row in selected_freq_table.
We could use the most direct approach and
provide the selected barcode table in the same file format,
just with desired rows kept.
However, this could become impractical for downstream analysis
as using this new table as a lookup would require us to load it all into memory,
causing the same problem one level down.
For memory-constrained environments,
we therefore suggest another idea:
Dumping the data into a SQLite database which later can be queried.
For simplicity, we paste all the barcode designations together in one column.
We begin by setting up the database:
suppressPackageStartupMessages({
library(DBI)
})
output_database_path <- tempfile(
pattern = "selected_barcode_table",
fileext = ".sqlite"
)
output_db <- dbConnect(RSQLite::SQLite(), output_database_path)For doing the streaming of the barcode table and combining it
with outputting to the database, we use the chunked package:
chunk_size <- 1e4
suppressPackageStartupMessages({
library(chunked)
library(magrittr)
})
read_table_chunkwise(
file = output_barcode_table,
header = TRUE,
sep = "\t",
chunk_size = chunk_size
) %>%
# For use within a pipeline, it is more convenient to use inner_join()
# than posDemux::row_match() and it achieves the save result
inner_join(selected_freq_table) %>%
mutate(
celltag = do.call(
paste,
c(barcodes %>% names() %>% all_of() %>% across(), sep = "_")
)
) %>%
select(read, UMI, celltag) %>%
write_chunkwise(dbplyr::src_dbi(output_db), "selected_barcodes")
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> Joining with `by = join_by(bc3, bc2, bc1)`
#> # Source: table<`selected_barcodes`> [?? x 3]
#> # Database: sqlite 3.51.1 [/tmp/RtmpCJE3sd/selected_barcode_table19bf6871f95853.sqlite]
#> read UMI celltag
#> <chr> <chr> <chr>
#> 1 seq_1 GCCTAAC bc3_57_bc2_51_bc1_94
#> 2 seq_2 CCAAGCG bc3_72_bc2_94_bc1_95
#> 3 seq_4 CCTAACG bc3_85_bc2_81_bc1_17
#> 4 seq_5 GCTCGTC bc3_49_bc2_64_bc1_77
#> 5 seq_6 TGGAGAA bc3_3_bc2_45_bc1_33
#> 6 seq_7 ACTTCGA bc3_17_bc2_57_bc1_71
#> 7 seq_8 GCCCGCA bc3_69_bc2_65_bc1_49
#> 8 seq_9 CCCATGT bc3_38_bc2_87_bc1_83
#> 9 seq_10 TGCCGGT bc3_76_bc2_36_bc1_85
#> 10 seq_11 CAACTCA bc3_15_bc2_84_bc1_16
#> # ℹ more rowsFinally, we make a index on the barcodes such that lookups are fast and close the database connection:
dbExecute(output_db, "CREATE INDEX idx ON selected_barcodes(read)") %>%
invisible()
dbDisconnect(output_db)4 Streaming using custom callbacks
4.1 Desired properties
In cases where the default callbacks are not sufficient, it is possible for the user to write custom callback functions. For our example, we will use the same sample dataset as before, but pretend the reads for some reason come in a FASTA file (which the default loader does not support). In addition, we want the UMIs to go into its own FASTA files instead of being a column in the barcode table. Unlike the default callbacks, we don’t truncate read names.
4.2 The state object
For progress printouts, we want the state object of the streaming to contain the total number of reads and the number of reads where demultiplexing was successful. Additionally, for our specific case, the loader needs to know where to begin reading the FASTA file for each chunk. For the final numbers, this is not really necessary because the required information is provided by the streaming framework itself when finishing. Thus, we want the state object to contain the following fields which are both integers:
total_readsdemultiplexed_reads
total_reads will be updated by the loader,
whereas demultiplexed_reads will be updated by
the archiver at the end of each chunk.
Furthermore, the archiver needs to write the column headers
to the barcode table when first writing to it,
so it must know whether it is working at its first chunk.
This gives rise to the logical flag output_table_initialized.
We thus initialize the state as follows:
custom_state_init <- list(
total_reads = 0L, demultiplexed_reads = 0L,
output_table_initialized = FALSE
)4.3 The loader
The loader is a function which accepts the state as its sole parameter and outputs a list with three fields:
state: To be passed into the archiver.sequences: The sequences in the chunk as aBiostrings::DNAStringSetobject.should_terminate: A logical flag to signal to the streaming framework that it should terminate. Note that this also means the loader is in charge of the termination criterion.
We decide to hard-code the loader to parse \(10^4\) reads for each chunk.
input_fasta <- system.file("extdata", "PETRI-seq_forward_reads.fa.gz",
package = "posDemux"
)
loader <- function(state) {
n_reads_per_chunk <- 1e4
if (!state$output_table_initialized) {
message("Starting to load sequences")
}
chunk <- Biostrings::readDNAStringSet(
filepath = input_fasta,
format = "fasta",
nrec = n_reads_per_chunk,
skip = state$total_reads
)
n_reads_in_chunk <- length(chunk)
if (n_reads_in_chunk == 0L && state$output_table_initialized) {
# The case when the initial chunk is empty is given
# special treatment since
# we want to create the table regardless
# No more reads to process, make the outer framework return
final_res <- list(
state = state,
sequences = NULL,
should_terminate = TRUE
)
message("Done demultiplexing")
return(final_res)
}
state$total_reads <- state$total_reads + n_reads_in_chunk
list(
state = state,
sequences = chunk,
should_terminate = FALSE
)
}4.4 The archiver
The archiver has the state obtained from the loader as its first argument and
the results from filter_demultiplex_res() of the chunk as its second argument.
Its return value is the state to provide to the loader when it continues with
its next chunk.
output_umi_file <- tempfile(pattern = "umi.fa", fileext = ".txt")
custom_output_table <- tempfile(
pattern = "barcode_table_custom",
fileext = ".txt"
)
archiver <- function(state, filtered_res) {
barcode_matrix <- filtered_res$demultiplex_res$assigned_barcodes
barcode_names <- colnames(barcode_matrix)
read_names <- rownames(barcode_matrix)
# If the table has no rows, we may risk getting a NULL value
if (is.null(read_names)) {
read_names <- character()
}
barcode_table <- as.data.frame(barcode_matrix)
read_name_table <- data.frame(read = read_names)
umis <- filtered_res$demultiplex_res$payload$UMI
chunk_table <- cbind(read_name_table, barcode_table)
if (!state$output_table_initialized) {
append <- FALSE
state$output_table_initialized <- TRUE
} else {
append <- TRUE
}
Biostrings::writeXStringSet(
umis,
filepath = output_umi_file, append = TRUE
)
readr::write_tsv(
x = chunk_table,
file = custom_output_table,
append = append,
col_names = !append,
eol = "\n"
)
state <- within(state, {
demultiplexed_reads <- demultiplexed_reads + nrow(barcode_matrix)
paste0(
"Processed {total_reads} reads,", " ",
"successfully demultiplexed {demultiplexed_reads} reads so far..."
) %>%
glue::glue() %>%
message()
})
state
}4.5 Putting it all together
With the initial state and the callbacks being defined, we are now ready to put them to use:
custom_summary_res <- streaming_demultiplex(
state_init = custom_state_init,
loader = loader,
archiver = archiver,
barcodes = barcodes,
allowed_mismatches = 1L,
segments = sequence_annotation,
segment_lengths = segment_lengths
)
#> Starting to load sequences
#> Processed 10000 reads, successfully demultiplexed 9117 reads so far...
#> Processed 20000 reads, successfully demultiplexed 18259 reads so far...
#> Processed 30000 reads, successfully demultiplexed 27362 reads so far...
#> Processed 40000 reads, successfully demultiplexed 36487 reads so far...
#> Processed 50000 reads, successfully demultiplexed 45629 reads so far...
#> Processed 56895 reads, successfully demultiplexed 51906 reads so far...
#> Done demultiplexing4.6 Verifying results
We finally load the files we wrote to in order to verify that they contain what we desire. We load the barcode table:
readr::read_tsv(custom_output_table, show_col_types = FALSE)
#> # A tibble: 51,906 × 4
#> read bc3 bc2 bc1
#> <chr> <chr> <chr> <chr>
#> 1 seq_1 bc3_57 bc2_51 bc1_94
#> 2 seq_2 bc3_72 bc2_94 bc1_95
#> 3 seq_4 bc3_85 bc2_81 bc1_17
#> 4 seq_5 bc3_49 bc2_64 bc1_77
#> 5 seq_6 bc3_3 bc2_45 bc1_33
#> 6 seq_7 bc3_17 bc2_57 bc1_71
#> 7 seq_8 bc3_69 bc2_65 bc1_49
#> 8 seq_9 bc3_38 bc2_87 bc1_83
#> 9 seq_10 bc3_76 bc2_36 bc1_85
#> 10 seq_11 bc3_15 bc2_84 bc1_16
#> # ℹ 51,896 more rowsand the UMI sequences:
Biostrings::readDNAStringSet(output_umi_file, format = "fasta")
#> DNAStringSet object of length 51906:
#> width seq names
#> [1] 7 GCCTAAC seq_1
#> [2] 7 CCAAGCG seq_2
#> [3] 7 CCTAACG seq_4
#> [4] 7 GCTCGTC seq_5
#> [5] 7 TGGAGAA seq_6
#> ... ... ...
#> [51902] 7 ATGGATC seq_56890
#> [51903] 7 ACGGCTT seq_56891
#> [51904] 7 ATGAGCG seq_56892
#> [51905] 7 ACCTGCG seq_56893
#> [51906] 7 GCCTTGA seq_568955 Reproducibility
The posDemux package was made possible thanks to:
This package was developed using biocthis.
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