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ChemistryQuantitativeAnalysis.jl is a package for quantitative analysis of chemicals based on tabular data.

For an interactive frontend, see InteractiveQuantification.jl.

Tabular data wrapper

This package provides two wrappers for data, ColumnDataTable{A, S, N, T} and RowDataTable{A, S, N, T} which are subtypes of AbstractDataTable{A, S, N, T}. ColumnDataTable indicates that part of columns represent analytes, and all rows reprsent samples. RowDataTable indicates that part of columns represent samples, and all rows represent analytes. Both types behave like the underlying table object.

Fields ColumnDataTable{A, S, N, T} RowDataTable{A, S, N, T}
analytecol - Symbol, column name whose elements are analytes.
samplecol Symbol, column name whose elements are samples. -
analyte Vector{A}, analytes in user-defined types. same
sample Vector{S}, samples in user-defined types. same
table Tabular data of type T same

ColumnDataTable{A, S, N, T} can be constructed in the following ways:

  1. ColumnDataTable(analytetype::TypeOrFn, samplecol::Symbol, table; analytename = setdiff(propertynames(table), [samplecol]))
  2. ColumnDataTable(table, analytetype::TypeOrFn, samplecol::Symbol; analytename = setdiff(propertynames(table), [samplecol]))
  3. ColumnDataTable(samplecol::Symbol, table; analytename = setdiff(propertynames(table), [samplecol]))
  4. ColumnDataTable(table, samplecol::Symbol; analytename = setdiff(propertynames(table), [samplecol]))

RowDataTable{A, S, N, T} can be constructed in the following ways:

  1. RowDataTable(analytecol::Symbol, sampletype::TypeOrFn, table; samplename = setdiff(propertynames(table), [analytecol]))
  2. RowDataTable(table, analytecol::Symbol, sampletype::TypeOrFn; samplename = setdiff(propertynames(table), [analytecol]))
  3. RowDataTable(analytecol::Symbol, table; samplename = setdiff(propertynames(table), [analytecol]))
  4. RowDataTable(table, analytecol::Symbol; samplename = setdiff(propertynames(table), [analytecol]))

analaytename and samplename will be converted to a vector of string, and then converted to the desired type using cqaconvert(analytetype, analaytename) and cqaconvert(sampletype, samplename).


AnalysisMethod{A, M, C, D} is used for storing method, containing all analytes, their internal standards and calibration curve setting, and data for fitting calibration curve.

Property Description
analytetable M <: Table with at least 3 columns, analytes identical to property analytes, isd, matching each analyte to index of its internal standard, and calibration matching each analyte to index of other analyte for fitting its calibration curve. -1 indicates the analyte itself is internal standard, and 0 indicates no internal standard. For example, a row (analytes = AnalyteX, isd = 2, calibration = 3) means that internal standard of AnalyteX is the second analyte, and it will be quantified using calibration curve of the third analyte.
signal Symbol, propertyname for extracting signal data from an AnalysisTable
pointlevel Vector{Int} matching each point to level. It can be empty if there is only one level in conctable.
conctable C <: AbstractDataTable{A, Int} containing concentration data for each level. Sample names must be symbol or string of integers for multiple levels. One level indicates using SingleCalibration.
signaltable D <: AbstractDataTable{A, S} containig signal for each point. It can be nothing if signal data is unecessary.
analyte AbstractVector{A}, analytes in user-defined types.
isd AbstractVector{<: A} that each analytes are internal standards.
nonisd AbstractVector{<: A} that each analytes are not internal standards.
point AbstractVector{S}, calibration points, identical to signaltable.samples. If signaltable is nothing, this value is nothing too.
level AbstractVector{Int}, calibration levels, identical to conctable.samples.

Constructors of AnalysisMethod:

  1. AnalysisMethod(analytetable::Table, signal::Symbol, pointlevel::Vector{Int}, conctable::AbstractDataTable{A, Int}, signaltable::Union{AbstractDataTable, Nothing})
  2. AnalysisMethod(conctable::AbstractDataTable{A, Int}, signaltable::ColumnDataTable, signal::Symbol, levelname::Symbol; kwargs...)
  3. AnalysisMethod(conctable::AbstractDataTable{A, Int}, signaltable::Nothing, signal::Symbol; kwargs...)
  4. AnalysisMethod(conctable::AbstractDataTable, signaltable::Union{AbstractDataTable, Nothing}, signal::Symbol, pointlevel::AbstractVector{Int}; kwargs...)

kwargs will be columns in analytetable. levelname is the column name for pointlevel if signaltable is a ColumnDataTable.


AnalysisTable{A, S, T} is basically a Dictionary{Symbol, <: AbstractDataTable{T}} which data can be extracted using proeperty syntax. For example, at[:area] === at.area.

Field Description
analyte Vector{A}, analytes in user-defined types.
sample Vector{S}, samples in user-defined types.
tables Dictionary{Symbol, <: AbstractDataTable{T}}, a dictionary mapping data type to datatable.

The key for signal data is determined by method.signal. Default names for relative signal, true concentration, estimated concentration and accuracy are relative_signal, true_concentration, estimated_concentration and accuracy.

AnalysisTable{A, S, T} can be constructed in the following ways:

  1. AnalysisTable(keys::AbstractVector{Symbol}, tables::AbstractVector{<: AbstractDataTable{A, S}})
  2. analysistable(iter)

iter is an iterable iter of key-value Pairs (or other iterables of two elements, such as a two-tuples). Keys should be Symbols, and values should be AbstractDataTables.


This package provides two calibration types, MultipleCalibration{A, N, T} and SingleCalibration{A, N} which are subtypes of AbstractCalibration{A, N}.


This type fits and stores calibration curve. It can be created from a AnalysisMethod{A, S} containing calibration data, an analyte A using function calibration.

Field Description
analyte Tuple{A, Any}. First element is the analyte being quantified, and the second element is its internal standard for which nothing indicates no internal standard.
type Bool determines whether fitting a linear line (true) or quadratic curve (false).
zero Bool determines whether forcing the curve crossing (0, 0) (true) or ignoring it (false).
weight Float64 represents the exponential applying to each element of x as a weighting vector.
formula FormulaTerm, the formula for fitting calibration curve.
table TypedTable.Table, the clean up calibration data, containing 7 columns.
model GLM object

The columns in table:

Column Description
id Point name
level The index of concentration level. The larger, the higher concentraion it represents.
y Signal or relative signal
x True concentraion
Predicted concentration
accuracy Accuracy, i.e. x̂/x.
include Whether this point is included or not

To predict concentration, call inv_predict. To calculate accuracy, call accuracy. type, zero, and weigtht can be modified directly. To change internal standard, modify analyte. After any modification, call update_calibration! with method to update the model.


This type contains data for single pont calibration.

Field Description
analyte Tuple{A} is the analyte with known concentration (internal standard).
conc Float64, concentration of analyte.


Batch{A, M, C, D} represents a batch for quantitative analysis.

Property Description
method M <: AnalysisMethod{A}, method.
calibration C <: Union{AbstractVector{MultipleCalibration{<: A}}, AbstractVector{SingleCalibration{<: A}}}
data Data for analysis, D <: Union{AnalysisTable{<: A}, Nothing}.
analyte AbstractVector{A}, analytes in user-defined types, identical to method.analytetable.analyte.
isd AbstractVector{<: A}, analytes which are internal standards, identical to method.analytetable.analyte.
nonisd AbstractVector{<: A} that each analytes are not internal standards.
point AbstractVector{S} or Nothing, calibration points, identical to method.point.
level AbstractVector{Int}, calibration levels, identical to method.level.

Constructors for Batch{A, M, C, D}:

  1. Batch(method::M, calibration::C, data::D = nothing)
  2. Batch(method::AnalysisMethod, data = nothing; type = true, zero = false, weight = 0)
  3. Batch(batch::Batch, at::AnalysisTable)

To calculate relative signal, concentration or accuracy and save the result, call update_relative_signal!, update_inv_predict! (in combination, update_quantification!) and update_accuracy!, respectively.

Reading and writting data to disk

To use data on disk, user should create a directory in the following structure:

│  ├──true_concentration.dt
│  │  ├──config.txt
│  │  └──table.txt
│  ├──area.dt
│  │  ├──config.txt
│  │  └──table.txt
│  ├──analytetable.txt
│  └──config.txt
│  ├──1.mcal
│  └──2.mcal

There is a function mkbatch which create a valid batch directory programatically.

Config files have the following general forms



The header delim determines the default delimiter for table.txt in this directory and subdirectories. and calibration is not necessary for initializing a batch. The former can be added to the batch directly in julia, and the latter will be generated after calibration.


All *.dt files will be read as ColumnDataTable or RowDataTable. They contain config.txt and table.txt.

Config file for ColumnDataTable needs the following headers.





Config file for RowDataTable needs the following headers.






It must contain two *dt files. true_concentration.dt contains true concentration for each analyte and level. The sample names must be integers. Another *.dt file is signal data for each analyte and calibration point. The file name is determined by config.txt.

Config file for needs the following headers.





signal specifys which .dt file serving as signal data. For the above file, will become method.signaltable.

pointlevel maps each point to level which should be integers.

level specifys the column representing property pointlevel of AnalysisMethod. It only works for which signaltable is ColumnDataTable; otherwise, it falls back to use pointlevel.

analytetable.txt needs to contain analyte names, index of their internal standards, and index of of other analytes whose calibration curve is used. The column names are fixed for these three columns.

analytes isd   calibration other_information
analyte1 isd1  calibration_analyte_id1 other_information1
analyte2 isd2  calibration_analyte_id2 other_information2

The delimiter should be "\t", and the order of columns does not matter.


It can contain multiple *.dt. The file names must start from an integer, _ and name.dt, e.g. 0_area.dt. The integer is for the order of reading into AnalysisTable, and name will be the key. The name of signal data is determined in

Reading and writing Batch

To read a batch into julia, call

julia> batch ="batch_name.batch", T; table_type, analytetype, delim)

T is the sink function for tabular data; it should create an object following Tables.jl interface. table_type is T parameter in the type signature of Batch which determines the underlying table type, analytetype is a concrete type for analyte, sampletype is a concrete type for sample, and delim specifies delimiter for tabular data if config[:delim] does not exist.

For analytetype and sampletype, string(cqaconvert(analytetype, x)) and string(cqaconvert(sampletype, x)) should equal x if x is a valid string. Additionally, tryparse have to be extended for CSV parsing:

  • tryparse(::Type{analytetype}, x::AbstractString) is neccessary for RowDataTable.
  • tryparse(::Type{sampletype}, x::AbstractString) is neccessary for ColumnDataTable.

To write batch to disk, call ChemistryQuantitativeAnalysis.write. There is a keyword argument delim controling delimiter of tables.

julia> ChemistryQuantitativeAnalysis.write("batch_name.batch", batch; delim = '\t')

There will be a folder calibration containing multiple *.mcal or *.scal folders. The former is for MultipleCalibration and the latter is for SingleCalibration.


using ChemistryQuantitativeAnalysis, TypedTables, DataFrames
const CQA = ChemistryQuantitativeAnalysis
import Base: show, convert, tryparse

# Custom Analyte type
struct AnalyteG1
struct AnalyteG2
struct AnalyteOther
const AnalyteTest = Union{AnalyteG1, AnalyteG2, AnalyteOther} # Use Union rather than abstarct type because csv parser only support concrete type
show(io::IO, analyte::AnalyteTest) = print(io,

# Analyte parser
function (::Type{AnalyteTest})(name::String)
    g = match(r"^G(\d)\(.*\)$", name)
    isnothing(g) && return AnalyteOther(name)
    g = parse(Int, first(g))
    g == 1 ? AnalyteG1(name) : g == 2 ? AnalyteG2(name) : AnalyteOther(name)
tryparse(::Type{AnalyteTest}, s::String) = AnalyteTest(s) # For reading data from disk

# Generate data
analytes = typedmap(AnalyteTest, ["G1(drug_a)", "G2(drug_a)", "G1(drug_b)", "G2(drug_b)"])
conc = Float64[1, 2, 5, 10, 20, 50, 100]
signal1 = vcat(Float64[1, 2, 5, 10, 20, 50, 100], [1, 2, 5, 10, 20, 50, 100] .+ 0.1, [1, 2, 5, 10, 20, 50, 100] .- 0.1)
signal2 = vcat(Float64[1, 2, 5, 10, 20, 50, 100] .^ 2, [1, 2, 5, 10, 20, 50, 100] .^ 2 .+ 0.1, [1, 2, 5, 10, 20, 50, 100] .^ 2 .- 0.1)

# Create method
conctable = ColumnDataTable(
         "level" => collect(1:7), 
         "G1(drug_a)" => conc,
         "G1(drug_b)" => conc .* 10), 
   analytetype = AnalyteTest
signaltable = ColumnDataTable(
         "point" => reshape([string(a, "_", b) for (a, b) in Iterators.product(1:7, 1:3)], 21), 
         "level" => repeat(1:7, 3),
         "G1(drug_a)" => signal1,
         "G2(drug_a)" => repeat([5.0], 21),
         "G1(drug_b)" => signal2,
         "G2(drug_b)" => repeat([2.0], 21)), 
   analytetype = AnalyteTest
method = AnalysisMethod(conctable, signaltable, :area, :point; analyte = analytes, isd = [2, -1, 4, -1], calibration = [1, -1, 3, -1])

# Create sample data
cdata = AnalysisTable([:area], [
            "Sample" => ["S1", "S2", "S3"], 
            "G1(drug_a)" => Float64[6, 24, 54],
            "G2(drug_a)" => Float64[5, 6, 6],
            "G1(drug_b)" => Float64[200, 800, 9800],
            "G2(drug_b)" => Float64[2, 2, 2]), 
         analytetype = AnalyteTest
rdata = AnalysisTable([:area], [
            "Analyte" => analytes, 
            "S1" => Float64[6, 6, 200, 2],
            "S2" => Float64[24, 6, 800, 2],
            "S3" => Float64[54, 6, 9800, 2]
) # Less efficient for quantification

# Create batch
cbatch = Batch(method, cdata)
rbatch = Batch(method, rdata)

# Calibration
cbatch.calibration # a vector of `MultipleCalibration`
cbatch.calibration[2].type = false # Use quadratic regression for the second analyte
rbatch.calibration[AnalyteG1("G1(drug_b)")].type = false # Use quadratic regression for `AnalyteG1("G1(drug_b)")`
update_calibration!(cbatch, 2)
update_calibration!(rbatch, AnalyteG1("G1(drug_b)"))

# Quantification
update_relative_signal!(cbatch) # A new data `` is created.
update_inv_predict!(cbatch) # Fit `` into calibration curve to create ``.
update_quantification!(cbatch) # equivalent to `update_inv_predict!(update_relative_signal!(cbatch))`

# Utils
analyteobj(cdata.area) # analytes of type `AnalyteTest`
sampleeobj(cdata) # samples of type `String`
analytename(cdata) # analytes of type `Symbol`
samplename(cdata.area) # samples of type `Symbol`
cdata.area[AnalyteTest("G2(drug_a)"), "S1"] = 6 # Set value using `dt[analyte, sample]`
cdata.area[AnalyteTest("G2(drug_a)"), "S1"] == 6
getanalyte(cdata.area, AnalyteG1("G1(drug_b)")) # get data of `AnalyteG1("G1(drug_b)")`
getanalyte(cdata.area, 1) # get data of first analyte
getsample(cdata.area, "S2")