ChemistryQuantitativeAnalysis.jl is a package for quantitative analysis of chemicals based on tabular data.
For command line interfaces, see juliaquant.
This package provides two wrappers for data, SampleDataTable{A, S, N, T} and AnalyteDataTable{A, S, N, T} which are subtypes of AbstractDataTable{A, S, N, T}. SampleDataTable indicates that part of columns represent analytes, and all rows reprsent samples. AnalyteDataTable indicates that part of columns represent samples, and all rows represent analytes. Both types behave like the underlying table object.
| Fields | SampleDataTable{A, S, N, T} |
AnalyteDataTable{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 |
SampleDataTable{A, S, N, T} can be constructed in the following ways:
SampleDataTable(analytetype::TypeOrFn, samplecol::Symbol, table; analytename = setdiff(propertynames(table), [samplecol]))SampleDataTable(table, analytetype::TypeOrFn, samplecol::Symbol; analytename = setdiff(propertynames(table), [samplecol]))SampleDataTable(samplecol::Symbol, table; analytename = setdiff(propertynames(table), [samplecol]))SampleDataTable(table, samplecol::Symbol; analytename = setdiff(propertynames(table), [samplecol]))
AnalyteDataTable{A, S, N, T} can be constructed in the following ways:
AnalyteDataTable(analytecol::Symbol, sampletype::TypeOrFn, table; samplename = setdiff(propertynames(table), [analytecol]))AnalyteDataTable(table, analytecol::Symbol, sampletype::TypeOrFn; samplename = setdiff(propertynames(table), [analytecol]))AnalyteDataTable(analytecol::Symbol, table; samplename = setdiff(propertynames(table), [analytecol]))AnalyteDataTable(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:
AnalysisMethod(analytetable::Table, signal::Symbol, pointlevel::Vector{Int}, conctable::AbstractDataTable{A, Int}, signaltable::Union{AbstractDataTable, Nothing})AnalysisMethod(conctable::AbstractDataTable{A, Int}, signaltable::SampleDataTable, signal::Symbol, levelname::Symbol; kwargs...)AnalysisMethod(conctable::AbstractDataTable{A, Int}, signaltable::Nothing, signal::Symbol; kwargs...)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 SampleDataTable.
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:
AnalysisTable(keys::AbstractVector{Symbol}, tables::AbstractVector{<: AbstractDataTable{A, S}})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 |
x̂ |
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}:
Batch(method::M, calibration::C, data::D = nothing)Batch(method::AnalysisMethod, data = nothing; type = true, zero = false, weight = 0)Batch(batch::Batch, at::AnalysisTable)Batch(dt::AbstractDataTable; signal::Symbol = :area, calid = r"Cal_(\d)_(\d*-*\d*)", order = "LR", f2c = 1, parse_decimal = x -> replace(x, "-" => "."))
The last method allows user to use encoded sample names to generate AnalysisMethod. Note that the returned batch does not have any calibration curves, which allows user to modify analytetable, and then apply init_calibration! to start calibrate.
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.
Use the function ui_init which activates an environment for ui and run interactive_calibrate! on the batch.
Two windows will pop out.
One is the main GUI.
Calibration curve settings, and the apearance can be modified through the interface. There are also widgets for saving figure, calibration settings.
Calibration points can be deleted or added back by left click.
Another window is a table containing data for each points.
By default, this function blocks the calling task (REPL) until the gui is closed. To run it asynchronously, set keyword argument async true.
To use data on disk, user should create a directory in the following structure:
batch_name.batch
├──config.txt
├──method.am
│ ├──true_concentration.sdt
│ │ ├──config.txt
│ │ └──table.txt
│ ├──area.sdt
│ │ ├──config.txt
│ │ └──table.txt
│ ├──analytetable.txt
│ └──config.txt
├──calibration
│ ├──1.mcal
│ └──2.mcal
└──data.at
├──0_quantity1.sdt
├──1_quantity2.sdt
└──2_quantity3.sdt
There is a function mkbatch which create a valid batch directory programatically.
Config files have the following general forms
[header1]
value
[header2]
value1
value2
value3
⋮
The header delim determines the default delimiter for table.txt in this directory and subdirectories.
data.at 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 *.sdt files can be replaced with *.adt files.
All *.sdt files will be read as SampleDataTable.
Config file needs the following headers.
[delim]
\t
[Sample]
sample_col_name
[Analyte]
analyte_col_name_1
analyte_col_name_2
⋮
All *.adt files will be read as AnalyteDataTable.
Config file needs the following headers.
[delim]
\t
[Analyte]
analyte_col_name
[Sample]
sample_col_name_1
sample_col_name_2
⋮
It must contain two .sdt or .adt files. true_concentration.sdt or true_concentration.adt contains true concentration for each analyte and level. The sample names must be integers.
Another file is signal data for each analyte and calibration point. The file name is determined by config.txt.
Config file for method.am needs the following headers.
[signal]
area
[delim]
\t
[levelname]
level
[pointlevel]
level_for_1st_point
level_for_2nd_point
⋮
signal specifys which .sdt or .adt file serving as signal data. For the above file, method.am/area.sdt or method.am/area.adt 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 SampleDataTable; 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 *.sdt or *.adt. The file names must start from an integer with _ following the name, e.g. 0_area.sdt. The integer is for the order of reading into AnalysisTable, and name will be the key. The name of signal data is determined in method.am/config.txt.
To read a batch into julia, call ChemistryQuantitativeAnalysis.read.
julia> batch = ChemistryQuantitativeAnalysis.read("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 forAnalyteDataTable.tryparse(::Type{sampletype}, x::AbstractString)is neccessary forSampleDataTable.
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
name::String
end
struct AnalyteG2
name::String
end
struct AnalyteOther
name::String
end
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.name)
# 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)
end
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 = SampleDataTable(
DataFrame(
"level" => collect(1:7),
"G1(drug_a)" => conc,
"G1(drug_b)" => conc .* 10),
:level;
analytetype = AnalyteTest
)
signaltable = SampleDataTable(
DataFrame(
"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)),
:point;
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], [
SampleDataTable(
DataFrame(
"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]),
:Sample;
analytetype = AnalyteTest
)
]
)
rdata = AnalysisTable([:area], [
AnalyteDataTable(
DataFrame(
"Analyte" => analytes,
"S1" => Float64[6, 6, 200, 2],
"S2" => Float64[24, 6, 800, 2],
"S3" => Float64[54, 6, 9800, 2]
),
:Analyte
)
]
) # 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 `cbatch.data.relative_signal` is created.
update_inv_predict!(cbatch) # Fit `cbatch.data.relative_signal` into calibration curve to create `cbatch.data.estimated_concentration`.
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
collect(eachanalyte(cdata.area))
collect(eachsample(cdata.area))
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")
dynamic_range(cbatch.calibration[1])
signal_range(rbatch.calibration[2])
lod(rbatch.calibration[2])
loq(rbatch.calibration[2])
formula_repr(cbatch.calibration[2])