FlowSets won the best poster award at ISMB/ECCB 2023 in the BioVis-Track!
FlowSets is a Python package for visualizing and analyzing gene expression patterns using fuzzy set theory. It enables the identification and visualization of gene expression flows across experimental conditions or clusters, and supports pathway enrichment analysis for genes according to a membership following specific expression patterns.
from flowset import *
# Read in data as polars dataframe
data = pl.read_csv(
'small_example/deseq2_results_25deg_all_comparisons_cleaned.csv',
null_values=['NA'],
schema={
"baseMean": pl.Float32,
"log2FoldChange": pl.Float32,
"lfcSE": pl.Float32,
"stat": pl.Float32,
"pvalue": pl.Float32,
"padj": pl.Float32,
"comparison": pl.Utf8,
"gene_id": pl.Utf8
}
)
# Fuzzify the log2FoldChange values for each gene and comparison
# Here all states are fuzzified with the same
explDFWide, mfFuzzy = LegacyFuzzifier.fuzzify(
data, #df
stepsize=0.01,
symbol_column="gene_id", # column name refering to feature
meancolName="log2FoldChange", # column name refering to signal
clusterColName="comparison", # column name refering to state
mfLevels = ["strong_down","down","neutral","up", "strong_up"], # linguistic variables which should be created
centers=[-2, -1, 0, 1, 2], # centers for the fuzzy sets
sdcolName=None, exprcolName=None, # these parameters are not in use, they are meant for single cell
)
# Create a FlowAnalysis (FlowSets) object for the fuzzified data
# The series is defined by tuples with the name in dataframe (clusterColName) and displayed name in FlowSets
def_series = (
("HSF1.KD vs Wildtype",'KO1 vs WT'),
("Double.KDKO vs Wildtype",'KO1+2 vs WT'),
("MSN24.KO vs Wildtype",'KO2 vs WT')
)
fa = FlowAnalysis(explDFWide, "gene_id", def_series, mfFuzzy)
# Plot the flow memberships for all genes
fa.plot_flows(figsize=(15, 10), outfile="./small_example/plots/complete_flow.png")
solis_genes = ["YAL005C", "YBR101C", "YDR171W", "YDR214W", "YDR258C", "YFL016C", "YGR142W", "YLL024C", "YLL026W", "YLR216C", "YMR186W", "YNL007C", "YNL064C", "YNL281W", "YOR027W", "YOR298C-A", "YPL240C", "YPR158W"]
fa.plot_flows(genes=solis_genes, title="Solis et al. 2016 - KO1 dependent genes", figsize=(10, 8), outfile="./small_example/plots/geneset_flow.png")
# Find genes with specific flow patterns and perform pathway analysis
relFlow = fa.flow_finder(
["?","?"],
minLevels=[None,None,"down"],
maxLevels=["down","down","up"],
verbose=False
)
fa.plot_flow_memberships(
use_edges=relFlow,
color_genes=solis_genes,
outfile="./small_example/plots/pattern_memberships.png"
)
pw_file = "small_example/goslim.gmt"
pwScores = fa.analyse_pathways(
use_edges=relFlow,
genesets_file=pw_file,
additional_genesets=[("solis annotated genes", solis_genes)]
)
pwScores_signif = pwScores.sort_values("pw_coverage_pval", ascending=True).head(20)
display(pwScores_signif)
# Show as ORA plot
fa.plotORAresult(pwScores_signif, "GOslim", numResults=10, figsize=(6,6), outfile="./small_example/plots/goslim_pathway_analysis.png")
- (Differential) Expression data are read in for each gene and each cluster (or state).
- Values are fuzzified by user-defined membership classes, min-max scaling, or quantiles.
- Relevant flows are defined using a simple grammar with
flow_finder, specifying desired differences between levels. - For each flow or group of flows, gene set enrichment analysis is performed. Gene sets are binned by size, and for each bin, flow memberships are calculated. A z-score is computed for each gene set (relative to others in the bin), which is transformed into a p-value for all positive-z-score (overrepresented) gene sets.
A more detailed description is available in the working copy of our manuscript article.
This project is licensed under the MIT License.
If you use FlowSets in your research, please cite our manuscript (see WorkingVersionFlowsets.pdf).
