qviewparsR

qviewparsR is a pure-R parser for the binary .Q-View project file format used in chemiluminescent multiplex ELISA plate imaging and quantification. It extracts the embedded report and returns it as tidy tibbles.

No Java runtime, no H2 database driver, no system dependencies beyond a working R installation.

Installation

qviewparsR is pure R — no compiled code, no Java runtime, no H2 database driver, and no system libraries — so it installs the same way on Windows, macOS, and Linux. The only hard prerequisite is R >= 4.1.0.

From CRAN (once released) you get a ready-to-use binary:

install.packages("qviewparsR")

Or the development version from GitHub:

# install.packages("pak")
pak::pak("CTTIR/qviewparsR")

Platform notes

Windows — no Rtools required: qviewparsR itself compiles nothing, and its CRAN dependencies install as pre-built binaries.
macOS — nothing beyond R (no XQuartz needed).
Linux — the package is pure R, but a few dependencies (dplyr, readr, tidyr, openxlsx2) contain C++ and build from source unless you use a binary repository such as the Posit Public Package Manager or r2u — which avoids needing a compiler. Otherwise install a build toolchain (e.g. build-essential on Debian/Ubuntu).

Installing the package pulls in its required imports automatically. The optional features need a few extra packages: plotting uses ggplot2 (plus patchwork for the overview figure), and the interactive app (qview_app()) uses shiny, bslib, and DT.

Quick start

library(qviewparsR)

qv <- read_qview("path/to/project.Q-View")
qv                                # compact summary
qv$analytes                       # spot_number, analyte, unit, lod, lloq, ...
qv$pixel_intensities              # long-format replicate readings
qv$plate_layout                   # one row per well

plot(qv, type = "plate_map")      # plate visualisation (needs ggplot2)
plot(qv, type = "intensity_heatmap")
plot(qv, type = "replicate_scatter")

# Export (pipe-friendly: each writer returns qv invisibly)
qv |>
  write_qview_xlsx("out.xlsx") |>
  write_qview_csv("out_csv/")  |>
  write_qview_rds("out.rds")

# Per-analyte mean / SD / CV per well-type group
summary(qv)

# Interactive front-end (upload, visualise, download)
qview_app()

What is a `.Q-View` file?

A .Q-View file is a single-file container that bundles an embedded H2 SQL database (Java, version 0.5/B) with binary LOB files holding the chemiluminescent plate images. The file is not a ZIP archive, XML, or CSV — it is a proprietary binary container with a plain-text manifest header followed by concatenated H2 segments.

Container layout

+-----------------------------------------+
| Bytes 0 - ~290:  text manifest header   |
|   - container version                   |
|   - declared file entries (size + name) |
+-----------------------------------------+
| Segment 1: main H2 SQL database         |
|   - 36 tables (see schema below)        |
|   - the rendered CSV report (CLOB)      |
+-----------------------------------------+
| Segment 2: LOB file 1 (image data)      |
+-----------------------------------------+
| Segment 3: LOB file 2 (more LOB data)   |
+-----------------------------------------+

Each H2 segment starts with a -- H2 0.5/B -- triplet marker. The database uses 2048-byte pages.

Embedded H2 schema (key tables)

qviewparsR recovers data through the embedded CSV report; the table diagram below documents the underlying schema for reference.

Group	Table	Purpose
Project / plate	`PROJECT`	Project metadata (creator, version, timezone)
	`PLATE`	Plate identifiers
	`PLATEDEFINITION`	Plate geometry (rows, columns, well diameter)
	`PRODUCT`	Product / lot identifiers, linked plate / plex definitions
Well & spot layout	`WELL`	Well coordinates (pixel + row/col)
	`SPOT`	Per-spot pixel intensity (raw + negative-subtracted), masking flags
	`PLEXDEFINITION` / `PLEXSPOT`	Spot layout per well
Analytes / standards	`ANALYTE`	Analyte names
	`PRODUCTANALYTE`	Spot number -> analyte mapping (key table)
	`ANALYTESTANDARD` / `ANALYTESTANDARDANALYTE`	Standard curve concentrations
Sample assignment	`WELLGROUP`	Sample IDs and type flags (standard / negative / sample / control)
	`WELLGROUPWELL`	Well-to-group mapping with dilution factors
Pixel-intensity cache	`SPOTPIXELINTENSITY`	Cached per-image spot intensity
	`NEGATIVESPOTPIXELINTENSITY`	Negative-spot intensity before subtraction
Curve fitting	`CURVEFITOPTION`	Regression model settings (4PL, 5PL, HDR, weighting)
	`REGRESSIONSOLUTION`	Fitted curve parameters
Image / camera	`IMAGE` / `IMAGEDETAILS` / `CAMERA`	Image and imager metadata
Report	`REPORTCONFIGURATION`	CSV report column flags
	`REPORTHISTORY`	The fully rendered CSV report (CLOB)
	`REPORTINFO`	Signatures and approval state

Naming convention

When Q-View imports a well-assignment template CSV it prefixes the identifiers internally:

Template value	Q-View internal name
`Cal 1` … `Cal N`	`ICal 1` … `ICal N`
`Low`	`GLow`
`High`	`HHigh`
`FD24277364`, all-digit IDs	`NFD24277364`, `N1211498458`

strip_qview_prefix() reverses this transformation, and read_qview(path, strip_prefix = TRUE) applies it across the whole returned object.

Output structure

read_qview() returns a list with class qview:

Slot	Description
`metadata`	Project, plate, image, imager, product, user, software version, template name, container version, file path, parse timestamp
`manifest`	One row per declared file entry inside the container
`segments`	Byte ranges of the three H2 segments
`analytes`	`spot_number`, `analyte`, `unit`, `lod`, `lloq`, `uloq`, `assay_control_low/high`
`well_groups`	`well_group`, `sample_id`, type flags (`is_standard`, `is_negative`, `is_sample`, `is_control`), `well_type` factor
`pixel_intensities`	Long-format per-well replicate readings
`summary_statistics`	Per-group `average`, `std_dev`, `cv` rows
`concentrations`	Long-format concentrations, or `NULL` if the report is qualitative
`curve_fit`	Per-analyte regression model
`report_csv`	Raw CSV report lines
`plate_layout`	One row per plate well with sample assignment + well type

Function reference

Category	Functions
Reader	`read_qview()`
Helpers	`strip_qview_prefix()`, `well_label()`
Optional	`read_qview_template()`
Methods	`print.qview()`, `plot.qview()`
Export	`write_qview_xlsx()`, `write_qview_csv()`, `write_qview_rds()`
Summary	`summary.qview()` (mean / SD / CV per analyte x well type)
Shiny app	`qview_app()`

Citation

If you use qviewparsR in academic work, please cite:

Heller R, Mannes M (2026). qviewparsR: Read .Q-View Multiplex ELISA Project Files. R package version 1.0.0. https://github.com/CTTIR/qviewparsR

BibTeX:

@Manual{qviewparsR,
  title  = {qviewparsR: Read .Q-View Multiplex ELISA Project Files},
  author = {Raban Heller and Marco Mannes},
  year   = {2026},
  note   = {R package version 1.0.0},
  url    = {https://github.com/CTTIR/qviewparsR}
}

You can always retrieve the up-to-date entry directly from R:

citation("qviewparsR")

Use of LLM tools

Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.