sctreeshap: a cluster tree data structure, and for shap analysis
Project description
sctreeshap
When doing single-cell RNA sequencing work, we firstly do clustering by community detection. Then we match the clusters to major cell types, which forms a cluster tree.
We usually need to select a branch for analysis, which helps us investigate on gene differential expressions on cell subtypes. sctreeshap constructs a data structure, which helps us quickly filter data whose clusters are under a specific branch (or in a specific cluster set). Moreover, it can run shap automatically to indicate marker genes.
Github repo:
https://github.com/ForwardStar/sctreeshap
v0.5.1 Update
- Bug fixes.
- Documentations updated.
Installing sctreeshap
Directly install by pip:
pip install sctreeshap
Or by conda:
conda create -n sctreeshap python=3.8
conda activate sctreeshap
pip install sctreeshap
Example
An example dataset, human brain MTG cell type, can be analyzed as default:
# Run in Jupyter Notebook
from sctreeshap import sctreeshap
sample = sctreeshap()
sample_dataset = sample.loadDefault()
print(sample_dataset)
## Select non-neuron branch
sample_dataset = sample.selectBranch(sample_dataset, 'n70')
## Run explainer
sample.explainMulti(sample_dataset)
The further details in the process are discussed in the following documents.
Data Input and Filtering
An sctreeshap object construction needs a python dict reflecting the tree structure. Here is an example of the cluster tree above.
from sctreeshap import sctreeshap
tree_arr = {
"n1": ('n2', 'n70'),
"n2": ('n26', 'n3'),
"n3": ('n4', 'n21'),
"n4": ('n7', 'n5'),
"n5": ('Exc L5-6 THEMIS DCSTAMP', 'n6'),
"n6": ('Exc L5-6 THEMIS CRABP1', 'Exc L5-6 THEMIS FGF10'),
"n7": ('n8', 'Exc L4-5 FEZF2 SCN4B'),
"n8": ('n9', 'n12'),
"n9": ('n10', 'Exc L5-6 THEMIS C1QL3'),
"n10": ('n11', 'Exc L2-3 LINC00507 FREM3'),
"n11": ('Exc L2 LAMP5 LTK', 'Exc L2-4 LINC00507 GLP2R'),
"n12": ('n13', 'n17'),
"n13": ('Exc L3-4 RORB CARM1P1', 'n14'),
"n14": ('Exc L3-5 RORB ESR1', 'n15'),
"n15": ('Exc L3-5 RORB COL22A1', 'n16'),
"n16": ('Exc L3-5 RORB FILIP1L', 'Exc L3-5 RORB TWIST2'),
"n17": ('n19', 'n18'),
"n18": ('Exc L5-6 RORB TTC12', 'Exc L4-6 RORB C1R'),
"n19": ('Exc L4-5 RORB FOLH1B', 'n20'),
"n20": ('Exc L4-6 RORB SEMA3E', 'Exc L4-5 RORB DAPK2'),
"n21": ('Exc L4-6 FEZF2 IL26', 'n22'),
"n22": ('Exc L5-6 FEZF2 ABO', 'n23'),
"n23": ('n24', 'Exc L5-6 FEZF2 EFTUD1P1'),
"n24": ('n25', 'Exc L6 FEZF2 OR2T8'),
"n25": ('Exc L6 FEZF2 SCUBE1', 'Exc L5-6 SLC17A7 IL15'),
"n26": ('n27', 'n53'),
"n27": ('n48', 'n28'),
"n28": ('n41', 'n29'),
"n29": ('n37', 'n30'),
"n30": ('n31', 'n34'),
"n31": ('n32', 'Inh L1-3 VIP GGH'),
"n32": ('n33', 'Inh L1-3 VIP CCDC184'),
"n33": ('Inh L1-3 VIP CHRM2', 'Inh L2-4 VIP CBLN1'),
"n34": ('n36', 'n35'),
"n35": ('Inh L2-4 VIP SPAG17', 'Inh L1-4 VIP OPRM1'),
"n36": ('Inh L1-2 VIP LBH', 'Inh L2-3 VIP CASC6'),
"n37": ('n39', 'n38'),
"n38": ('Inh L2-5 VIP SERPINF1', 'Inh L2-5 VIP TYR'),
"n39": ('n40', 'Inh L1-2 VIP PCDH20'),
"n40": ('Inh L2-6 VIP QPCT', 'Inh L3-6 VIP HS3ST3A1'),
"n41": ('n43', 'n42'),
"n42": ('Inh L1-3 VIP ADAMTSL1', 'Inh L1-4 VIP PENK'),
"n43": ('n44', 'n46'),
"n44": ('n45', 'Inh L1-2 SST BAGE2'),
"n45": ('Inh L1 SST CHRNA4', 'Inh L1−2 GAD1 MC4R'),
"n46": ('Inh L1-3 PAX6 SYT6', 'n47'),
"n47": ('Inh L1-2 VIP TSPAN12', 'Inh L1-4 VIP CHRNA6'),
"n48": ('n49', 'n50'),
"n49": ('Inh L1-2 PAX6 CDH12', 'Inh L1-2 PAX6 TNFAIP8L3'),
"n50": ('Inh L1 SST NMBR', 'n51'),
"n51": ('n52', 'Inh L2-6 LAMP5 CA1'),
"n52": ('Inh L1-4 LAMP5 LCP2', 'Inh L1-2 LAMP5 DBP'),
"n53": ('n54', 'Inh L2-5 PVALB SCUBE3'),
"n54": ('Inh L3-6 SST NPY', 'n55'),
"n55": ('n61', 'n56'),
"n56": ('Inh L5-6 GAD1 GLP1R', 'n57'),
"n57": ('Inh L5-6 PVALB LGR5', 'n58'),
"n58": ('n59', 'Inh L5-6 SST MIR548F2'),
"n59": ('Inh L4-5 PVALB MEPE', 'n60'),
"n60": ('Inh L2-4 PVALB WFDC2', 'Inh L4-6 PVALB SULF1'),
"n61": ('n62', 'Inh L5-6 SST TH'),
"n62": ('n65', 'n63'),
"n63": ('n64', 'Inh L2-4 SST FRZB'),
"n64": ('Inh L1-3 SST CALB1', 'Inh L3-5 SST ADGRG6'),
"n65": ('Inh L3-6 SST HPGD', 'n66'),
"n66": ('n67', 'Inh L4-5 SST STK32A'),
"n67": ('n69', 'n68'),
"n68": ('Inh L5-6 SST NPM1P10', 'Inh L4-6 SST GXYLT2'),
"n69": ('Inh L4-6 SST B3GAT2', 'Inh L5-6 SST KLHDC8A'),
"n70": ('n71', 'Micro L1-3 TYROBP'),
"n71": ('n72', 'Endo L2-6 NOSTRIN'),
"n72": ('n73', 'Oligo L1-6 OPALIN'),
"n73": ('OPC L1-6 PDGFRA', 'n74'),
"n74": ('Astro L1-6 FGFR3 SLC14A1', 'Astro L1-2 FGFR3 GFAP')
}
Sample = sctreeshap(tree_arr)
The keys of the dict represent the name of each tree node, while the values represent the children of each node (the tree can be either binary or multi-children). Note that clusters do not need to be assigned as a key, since they are the leaf nodes in the tree and do not have children.
You can read in data by:
data = Sample.readData(branch_name='n70', data_directory='./nonneuron_full.pkl')
where branch_name is your target branch, data_directory is the directory of the input file. It can be either a csv file or pkl file.
The sample data "nonneuron_full.pkl" can be downloaded from:
After reading in data, you can filter low-expressed genes, housekeeping genes and general genes by:
prefix = ["MT", "RPS", "RPL", "HSP", "HLA"]
housekeeping = pd.read_csv("./Housekeeping_TranscriptsHuman.csv")
housekeeping = list(housekeeping["Gene_symbol"])
data = Sample.geneFiltering(data, min_partial=0.3, gene_set=housekeeping, gene_prefix=prefix)
print(data)
Then genes expressed in <30% cells will be filtered. Genes in gene_set or with prefix in gene_prefix will also be filtered.
The housekeeping genes of human can be downloaded from:
http://www.housekeeping.unicamp.br/Housekeeping_TranscriptsHuman.xlsx
You can merge clusters under a branch if needed:
data = Sample.mergeBranch(data, 'n73')
This relabels cells with cluster ['OPC L1-6 PDGFRA', 'Astro L1-6 FGFR3 SLC14A1', 'Astro L1-2 FGFR3 GFAP'] as 'n73'.
Displaying Shap Figures
Note: this part is recommended to run in jupyter notebook.
After reading in the data and filtering, you can build multi-classification model and generate shap figures:
Sample.explainMulti(
data,
use_SMOTE=False,
nthread=48, # multi-thread
shap_params={
"max_display": 10,
"bar_plot": True,
"beeswarm": False,
"decision_plot": False
}
)
or build binary-classification model and generate shap figures:
Sample.explainBinary(
data,
cluster_name='Micro L1-3 TYROBP',
use_SMOTE=False,
nthread=48, # multi-thread
shap_params={
"max_display": 10,
"bar_plot": True,
"beeswarm": True,
"force_plot": False,
"heat_map": False,
"decision_plot": False
}
)
Get Shap Values and Marker Genes
After running explainBinary() or explainMulti(), you can run:
shap_values = Sample.getShapValues()
marker_genes = Sample.getTopGenes()
to get shapley values and marker genes (with top absolute mean shap values).
API References
For more functions, you can refer to the documentations by printing the help function out:
print(Sample.help('documentations'))
and query the details of a function by:
function_name = 'readData' # Can be whatever the function in the class
print(Sample.help(function_name))
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