gszep / flowatlas.jl Goto Github PK
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License: MIT License
an interactive explorer for flow cytometry data
License: MIT License
@mad3cientist upload 2 examples with images showing manual anomaly gating.
The metadata from the workspace file currently shows
<Group name="tLN" >
<Criteria>
<Criterion keyword="TUBE NAME" function="Contains" value="tln" />
</Criteria>
<ManuallyIncludedSamples>
<SampleRef sampleID="11" />
</ManuallyIncludedSamples>
<SampleRefs>
<SampleRef sampleID="33" />
<SampleRef sampleID="25" />
<SampleRef sampleID="11" />
</SampleRefs>
<Keywords/>
</Group>
Density thresholding on FSC-W
vs FSC-A
then on SSC-W
vs SSC-A
to get singlets.
Then on Z-UV
negative to get live cells. @mad3cientist Upload 2 examples into a separate folder under data/testing
I am several versions behind JSServe and would probably benefit from all the new functionality
Cool project! I was wondering if it was possible to open a folder with .fcs
files without a .wsp
file?
Something like:
julia> readdir()
5-element Vector{String}:
"Specimen_001_1 BFP_002.fcs"
"Specimen_001_1 CTL_001.fcs"
"Specimen_001_2 BFP_004.fcs"
"Specimen_001_2 CTL_003.fcs"
julia> FlowAtlas.run(""; files = "*.fcs")
[ Info: Loading FCS files...
ERROR: AssertionError: no such file:
Stacktrace:
[1] load(path::String; files::String, cols::Symbol, kwargs::@Kwargs{})
@ FlowWorkspace ~/.julia/packages/FlowWorkspace/8HLjp/src/Utils.jl:32
[2] load
@ ~/.julia/packages/FlowWorkspace/8HLjp/src/Utils.jl:30 [inlined]
[3] run(path::String; files::String, port::Int64, url::String, cols::Symbol, drop::Vector{…}, nlevels::Int64, nbins::Int64, heatmapQuantileEdge::Float64, violinQuantileEdge::Float64, channelScheme::ColorSchemes.ColorScheme{…}, labelScheme::ColorSchemes.ColorScheme{…}, hold::Bool)
@ FlowAtlas ~/.julia/packages/FlowAtlas/yxybS/src/FlowAtlas.jl:65
[4] top-level scope
@ REPL[22]:1
Some type information was truncated. Use `show(err)` to see complete types.
My package environment on Julia 1.10:
(jl_TZ3iwY) pkg> st
Status `/tmp/jl_TZ3iwY/Project.toml`
[ef7debb3] FlowAtlas v0.1.13
I would love to use this tool to visualize my populations and draw gates, but I understand if that's out of the scope of this project.
For example, TRegs can be split into +/- CD69. @mad3cientist upload into comments examples of subpopulation that have not been split properly. Report metadata on number of merged clusters that have been assigned to one label and have the option of exploring unmatched clusters. Relation of ROC curve? Perhaps we would like to explore ROC vs cluster number.
if a measure of mixing between groups for labelled populations goes below a certain threshold, throw a warning to the user notifying them about which samples and which population contains the batch effect.
Initial list here:
https://scikit-learn.org/stable/modules/clustering.html
If still unsatisfactory then do:
Parse GateML to generate labels, compare labels to predictions
Visualise accuracies using confusion matrix, generate precision recall curve (ROC-curve) by changing confidence thresholds in profile projection
I will move the colour bar a couple of pixels to the left
For more efficient data access when rendering tiles
We would like warnings for the following two cases
we need to decide what to do about missing data. Substituting zeros in the channel columns seems arbitrary.. what is the appropriate way of dealing with this?
also change the number of significant figures on percentage scale
@gszep needs to optionally split on Foxp3-IgM
if -
exists in channel name, to Foxp3
and IgM
as separate channels. For 390C
fill IgM
channel with NAN
@mad3cientist we need to update all .fcs
files (except 390C) with cleaned singlet live cells.
One of the changes in this set of files SimonDanisch/Bonito.jl@v1.2.3...v1.2.4 prevents FlowAtlas.jl from displaying any tiles at all. For now I will restrict the version to be < 1.2.4
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I'll open a PR within a few hours, please be patient!
The feature extractor currently uses a gaussian mixtures to shift and scale channels independently per sample to +/- 1. If reference files are present, use those to scale autoflouresnce to -1, leaving relative intensities in positive region alone.
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