diff --git a/cylinter/cylinter_config 1.29.23 PM.yml b/cylinter/cylinter_config 1.29.23 PM.yml deleted file mode 100644 index e144e2f..0000000 --- a/cylinter/cylinter_config 1.29.23 PM.yml +++ /dev/null @@ -1,344 +0,0 @@ -# GENERAL PROGRAM CONFIGURATIONS - -inDir: /Users//Desktop/cylinter_demo -# Path to CyLinter input directory containing multi-channel -# image files (TIF or OME-TIF), segmentation outlines (OME-TIF), -# segmentation masks (TIF), and corresponding single-cell feature tables (CSV) - -outDir: /Users//Desktop/cylinter_demo/output -# CyLinter output directory. Path is created if it does not exist. - -sampleMetadata: - "1": ["1", "Normal kidney cortex", "NKC", "CANCER-FALSE", 1] - "15": ["15", "Glioblastoma", "GBM", "CANCER-TRUE", 1] - "18": ["18", "Mesothelioma", "MTO", "CANCER-TRUE", 1] - "68": ["68", "Tonsil", "TSL", "CANCER-FALSE", 3] -# Sample metadata dictionary: keys = file names; values = list of strings. -# First elements: sample names (str) -# Second elements: descriptive text of experimental condition (str) -# Third elements: abbreviation of experimental condition (str) -# Fourth elements: comma-delimited string of arbitrary binary declarations -# for computing t-statistics between two groups of samples (str dytpe) -# Fifth elements: replicate number specifying biological or -# technical replicates (int) - -samplesToExclude: [] -# (list of strs) Sample names to exclude from analysis specified -# according to the first elements of sampleMetadata configuration. - -counterstainChannel: "DNA1" -# (str) Name of marker in markers.csv file for use in visualizing nuclear counterstain - -markersToExclude: ["Rabbit IgG", "Goat IgG", "Mouse IgG", "CD56", "CD13", - "pAUR", "CCNE", "CDKN2A", "PCNA_1", "CDKN1B_2", - "CD63", "CD32", "CCNA2", "CDKN1C", "PCNA_1", - "CDKN1B_1", "CCND1", "cPARP", "pCREB", - "CCNB1", "PCNA_2", "CDK2" - ] -# (list of strs) Immunomarkers to exclude from analysis -# Does not include nuclear dyes. They are needed for the -# cycleCorrelation module to remove cell dropout. - -############################################################################### -# MODULE-SPECIFIC CONFIGURATIONS - -# selectROIs------------------------------------------------------------------- -delintMode: True -# (bool) Whether to drop (True; negative selection) or -# retain (False; positive selection) cells selected by ROIs. - -showAbChannels: True -# (bool) Whether to show all immunomarker channels (True) when Napari -# is open (may be memory limiting) or show cycle 1 DNA only (False). - -samplesForROISelection: ["1", "15", "18", "68"] -# (list of strs) Sample names for ROI selection specified -# according to the first elements of sampleMetadata configuration. - -autoArtifactDetection: True -# (bool) Whether to display tools for automated artifact detection in Napari window - -artifactDetectionMethod: "classical" -# (str) Algorithm used for automated artifact detection (current option: "classical"). -# Multi-layer perceptron method ("MLP") currently under development. - - -# intensityFilter------------------------------------------------------------------- -numBinsIntensity: 50 -# (int) Number of bins for DNA intensity histograms. - - -# areaFilter------------------------------------------------------------------- -numBinsArea: 50 -# (int) Number of bins for DNA area histograms. - - -# cycleCorrelation------------------------------------------------------------------- -numBinsCorrelation: 50 -# (int) Number of bins for DNA1/DNAn histograms. - - -# pruneOutliers------------------------------------------------------------------- -hexbins: False -# (bool) Whether to use hexbins (True) or scatter plots (False) to plot -# single-cell signal intensities. Scatter plots allow for higher resolution, -# but may require longer rendering times. - -hexbinGridSize: 20 -# (int) Hexbin grid size when hexins=True. -# Higher values increase bin resolution. - - -# metaQC (optional)------------------------------------------------------------------- -metaQC: False -# (bool) Whether to perform data reclassification based on -# unsupervised clustering results of combinations of clean and -# noisy (previously-redacted) data. - -embeddingAlgorithmQC: "UMAP" -# (str) Embedding algorithm used for clustering (options: "TSNE" or "UMAP"). - -channelExclusionsClusteringQC: [] -# (list of strs) Immunomarkers to exclude from clustering. - -samplesToRemoveClusteringQC: [] -# (list of strs) Samples to exclude from clustering. - -fracForEmbeddingQC: 1.0 -# (float) Fraction of cells to be embedded (range: 0.0-1.0) -# Limits the amount of data passed to downstream modules. - -dimensionEmbeddingQC: 2 -# (int) Dimension of the embedding (fixed to 2 in current version). - -topMarkersQC: "clusters" -# (str) Normalization axis ("channels" or "clusters") used to define -# highest expressed markers per cluster. - -colormapAnnotationQC: "Sample" -# (str) Metadata annotation to colormap the embedding: Sample or Condition. - -metricQC: "euclidean" -# (str) Distance metric for computing embedding. -# Choose from valid metrics used by scipy.spatial.distance.pdist: -# "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", -# "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", -# "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", -# "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "yule". - -# -------------------------------------- -# tSNE-specific configurations: -# https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html -perplexityQC: 50.0 -# (float) Related to the number of nearest neighbors used in other -# manifold learning algorithms. Larger datasets usually require -# larger perplexity. Different values can result in significantly -# different results. - -earlyExaggerationQC: 12.0 -# (float) For larger values, the space between natural clusters -# will be larger in the embedded space. - -learningRateTSNEQC: 200.0 -# (float) tSNE learning rate (typically between 10.0 and 1000.0). - -randomStateQC: 5 -# (int) Determines the random number generator for reproducible results -# across multiple function calls. - -# -------------------------------------- -# UMAP-specific configurations: -# https://umap-learn.readthedocs.io/en/latest/api.html -nNeighborsQC: 6 -# (int) The size of local neighborhood (in terms of number of -# neighboring sample points) used for manifold approximation. -# Larger values result in more global views of the manifold, -# while smaller values result in more local data being preserved. -# In general values should be in the range 2 to 100. - -learningRateUMAPQC: 1.0 -# (float) The initial learning rate for the embedding optimization. - -minDistQC: 0.1 -# (float) The effective minimum distance between embedded points. -# Smaller values will result in a more clustered/clumped -# embedding where nearby points on the manifold are drawn -# closer together, while larger values will result on a more -# even dispersal of points. The value should be set relative -# to the spread value, which determines the scale at which -# embedded points will be spread out. - -repulsionStrengthQC: 5.0 -# (float) Weighting applied to negative samples in low dimensional -# embedding optimization. Values higher than one will -# result in greater weight being given to negative samples. - - -# PCA------------------------------------------------------------------- -channelExclusionsPCA: [] -# (strs) Immunomarkers to exclude from PCA analysis. - -samplesToRemovePCA: [] -# (list of strs) Samples to exclude from PCA analysis. - -dimensionPCA: 2 -# (int) Number of PCs to compute. - -pointSize: 90.0 -# (float) scatter point size for sample scores plot. - -labelPoints: True -# (bool) Annotate scatter points with condition abbreviations -# from sampleMetadata configuration. - -distanceCutoff: 0.15 -# (float) Maximum distance between data points in PCA scores plot to -# be annotated with a common label. Useful for increasing visual clarity -# of PCA plots containing many data points. Applicable when -# labelPoints is True. - -conditionsToSilhouette: [] -# (list of strs) List of abbreviated condition names whose corresponding -#scores plot points will be greyed out, left unannotated, and sent to the back -# of the plot (zorder). Useful for increasing visual clarity of PCA -# plots containing many data points. - - -# gating (optional)------------------------------------------------------------------- -gating: True -# (bool) Whether to perform SYLARAS-style gating on single-cell data. -# Cell Syst. 2020 Sep 23;11(3):272-285.e9 PMID: 32898474 - -channelExclusionsGating: [] -# (list of strs) Immunomarkers to exclude from gating. - -samplesToRemoveGating: [] -# (list of strs) Samples to exclude from gating. - -vectorThreshold: 100 -# (int) vizualize Boolean vectors with cell counts >= vectorThreshold - -classes: - Tumor: - definition: [+pan-CK, +KI67] - subsets: [] -# (dict) Boolean immunophenotype signatures. -# +marker = immunopositive , -marker = immunonegative, marker = don't care - - -# clustering------------------------------------------------------------------- -embeddingAlgorithm: "UMAP" -# (str) Embedding algorithm to use for clustering (options: "TSNE" or "UMAP"). - -channelExclusionsClustering: [] -# (list of strs) Immunomarkers to exclude from clustering. - -samplesToRemoveClustering: [] -# (list of strs) Samples to exclude from clustering. - -normalizeTissueCounts: True -# (bool) Make the number of cells per tissue for clustering more similar -# through sample-weighted random sampling. - -fracForEmbedding: 1.0 -# (float) Fraction of cells to be embedded (range: 0.0-1.0). -# Limits amount of data passed to downstream modules. - -dimensionEmbedding: 2 -# (int) Dimension of the embedding (options: 2 or 3). - -topMarkers: "clusters" -# (str) Normalization axis ("channels" or "clusters") used to define -# highest expressed markers per cluster. - -colormapAnnotationClustering: "Sample" -# (str) Metadata annotation to colormap the embedding: Sample or Condition. - -metric: "euclidean" -# (str) Distance metric for computing embedding. -# Choose from valid metrics used by scipy.spatial.distance.pdist: -# "braycurtis", "canberra", "chebyshev", "cityblock", "correlation", "cosine", -# "dice", "euclidean", "hamming", "jaccard", "jensenshannon", "kulsinski", -# "mahalanobis", "matching", "minkowski", "rogerstanimoto", "russellrao", -# "seuclidean", "sokalmichener", "sokalsneath", "sqeuclidean", "yule". - -# -------------------------------------- -# tSNE-specific configurations: -# https://scikit-learn.org/stable/modules/generated/sklearn.manifold.TSNE.html -perplexity: 50.0 -# (float) Related to the number of nearest neighbors used in other -# manifold learning algorithms. Larger datasets usually require -# larger perplexity. Different values can result in significantly -# different results. - -earlyExaggeration: 12.0 -# (flaot) For larger values, the space between natural clusters -# will be larger in the embedded space. - -learningRateTSNE: 200.0 -# (float) tSNE learning rate (typically between 10.0 and 1000.0). - -randomStateTSNE: 5 -# (int) Determines the random number generator for reproducible results -# across multiple function calls. - -# -------------------------------------- -# UMAP-specific configurations: -# https://umap-learn.readthedocs.io/en/latest/api.html -nNeighbors: 6 -# (int) The size of local neighborhood (in terms of number of -# neighboring sample points) used for manifold approximation. -# Larger values result in more global views of the manifold, -# while smaller values result in more local data being preserved. -# In general values should be in the range 2 to 100. - -learningRateUMAP: 1.0 -# (float) The initial learning rate for the embedding optimization. - -minDist: 0.1 -# (float) The effective minimum distance between embedded points. -# Smaller values will result in a more clustered/clumped -# embedding where nearby points on the manifold are drawn -# closer together, while larger values will result on a more -# even dispersal of points. The value should be set relative -# to the spread value, which determines the scale at which -# embedded points will be spread out. - -repulsionStrength: 5.0 -# (float) Weighting applied to negative samples in low dimensional -# embedding optimization. Values higher than one will -# result in greater weight being given to negative samples. - -randomStateUMAP: 5 -# (int) Determines the random number generator for reproducible results -# across multiple function calls. - - -# frequencyStats------------------------------------------------------------------- -controlGroups: ["CANCER-FALSE"] -# (list of strs) Corresponds to control groups for each binary declaration -# specified as the third elements of sampleMetadata values. - -denominatorCluster: null -# (None type) Cluster to be used as the denominator when computing cluster -# frequency ratios. Set to null first, change to cluster integer number -# to normalize cluster frequencies to a particular cluster if desired. - -FDRCorrection: False -# (bool) Whether to compute p-vals and false discovery rate (FDR)-corrected -# q-vals (True) or compute uncorrected p-vals only (False). - - -# curateThumbnails------------------------------------------------------------- -numThumbnails: 25 -# (int) Number of examples per cluster to be curated. - -topMarkersThumbnails: "clusters" -# (str) Normalization axis ("channels" or "clusters") used to define -# highest expressed markers per cluster. - -windowSize: 30 -# (int) Number of pixels in x and y dimensions per thumbnail. - -segOutlines: True -# (bool) Whether to overlay cell segmentation outlines on thumbnail images. diff --git a/recipe/meta.yaml b/recipe/meta.yaml index 0df026c..8d0dde2 100644 --- a/recipe/meta.yaml +++ b/recipe/meta.yaml @@ -1,5 +1,5 @@ {% set name = "cylinter" %} -{% set version = "0.0.48_test" %} +{% set version = "0.0.48" %} package: name: "{{ name|lower }}" @@ -7,7 +7,7 @@ package: source: git_url: https://github.com/labsyspharm/cylinter.git - git_tag: v0.0.48_test + git_tag: v0.0.48 build: number: 0