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DigCNV-0.3.3
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مشاهده بیشترتوضیحات
DigCNV: Discriminating True CNVs from artifacts from genotyping without further visualisation
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | DigCNV-0.3.3 |
| نام | DigCNV |
| نسخه کتابخانه | 0.3.3 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | - |
| ایمیل نویسنده | Thomas Renne <thomas.renne@umontreal.ca> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/DigCNV/ |
| مجوز | - |
# DigCNV
[](https://badge.fury.io/py/digcnv)
## A machine learning model to quality control genotyped CNVs
False CNVs bring noise to analysis and could distort a diagnosis.
CNV calling produce false negative and positive identifications.
To remove false negative it's recommended to use multiple CNV caller at a time.
So we present here, a statistical approach to clean CNV results coming from two calling algorithms,
*PennCNV* or *QuantiSNP*
This machine learning can be used in two different ways:
- a first one-line script to annotate any new CNVs on our pre-trained model. Trained on 38,000 CNVs coming from 7 different genotyping technologies.
This model have AUC > 90% for most technologies (already trained-on or new technologies)
- a second approach line by line to train and test our machine learning model on your own visualized dataset and performe some statistics.
## DigCNV models
- Model presented in IAMDRS congress available at: https://murena.io/s/xEsyae6gxfMEnWJ
- Most up-to-date model available at https://murena.io/s/xEsyae6gxfMEnWJ
## Classify CNVs with one line
``` sh
# To run only once to install package
pip install digcnv
python3 -m digcnv [-v] <Path to config file>
```
#### Config file
Example of config file needed for the one line execution. Example can be download with function `getConfigFileExample(output_path)`
```
[Inputs]
pc_output_path = Path to the PennCNV output file
pc_qc_path = Path to the PennCNV microarray quality file
qs_output_path = Path to the QuantiSNP output file
[Output]
Save_to_file = True
Output_path = /home/thomas/Documents/scripts/DigCNV/temp_data/DigCNV_pred.tsv
[DigCNV]
model_path": Path of the downloaded model. Available at :
```
## Run DigCNV with more options
#### Prepare data
```python
from digcnv import CNVision, dataPreparation
# Import and merge CNV coming from two CNV calling algorthims (only PennCNV and QuantiSNP in this version)
cnvs = CNVision.mergeMultipleCNVCallingOutputs("<list of PennCNV and QuantiSNP output pathways>", ["PennCNV", "QuantiSNP"])
# Add microarray quality data to the list of CNVs
cnvs = dataPreparation.addMicroArrayQualityData(cnvs, "<path to the PennCNV microarray quality file>")
# Compute derived features and add it to the CNV list
cnvs = dataPreparation.addDerivedFeatures(cnvs)
# Add CallRate data to CNV list
cnvs = dataPreparation.addCallRateToDataset(cnvs, call_rate_path="<Pathway to the callrate file>", callrate_colname="<CallRate column name>", individual_colname="<Individual column name>")
# Add Chromosomic information such as centromere and Segmental Duplications overlap
cnvs = dataPreparation.addChromosomicAnnotation(cnvs)
# Add number of probes used with the technology (Useful for model trained on multiple datasets and multiple technologies)
cnvs = dataPreparation.addNbProbeByTech(cnvs, pfb_file_path="<Pathway to the PFB file>")
```
#### Run DigCNV from the pre-trained model
More information at *"article"*
```python
from digcnv import digCnvModel, dataVerif
# Create an empty DigCNV model
model = digCnvModel.DigCnvModel()
# Open pre-trained model and update object
model_path = join(split(__file__)[0], 'data', 'DigCNV_model_multiple_technos.pkl')
model.openPreTrainedDigCnvModel(model_path)
# Check if mandatory columns for the DigCNV model exist and have right formats
dataVerif.checkIfMandatoryColumnsExist(cnvs, post_data_preparation=True)
dataVerif.checkColumnsformats(cnvs, post_data_preparation=True)
# Optional plot a correlation heatmap between different predictors used in model
dataVerif.plotCorrelationHeatMap(cnvs, list_dim=model._dimensions, output_path="<Pathway where output plot (.pdf or .png)>")
# Check NaN data within mandatory columns and split data into two dataframes: first for CNVs with all information available
# and a second one with all CNVs with at least one missing data (can't be used for prediction)
cnvs, cnvs_with_na = dataVerif.computeNaPercentage(cnvs, dimensions=model._dimensions, remove_na_data=True)
# Discriminate true from false CNVs from CNVs with all data, then produce a list of classes
predicted_cnvs = model.predictCnvClasses(cnvs)
cnvs["DigCNVpred"] = predicted_cnvs
```
#### Train your own DigCNV model
```python
from digcnv import digCNVModel
# Uses CNVs created in Prepare data section :
# Add your own annotation to your CNVs (0 for false CNVs and 1 for true)
cnvs["visualized_class"] = ["<classes of each CNV visualized>"]
# Dimensions used to classify CNVs (Recommended dimensions you can use your own)
predictors = ["",""]
# Remove CNVs with at least one missing values in used predictors or in visualized column
cnvs, removed = DigCnvPreProcessing.removeLinesWithNA(cnvs, dimensions=predictors + ["visualized_class"])
# Split dataset into two groups a training dataset and a testing dataset (70% - 30%)
X_train, y_train, X_test, y_test = DigCnvPreProcessing.createTrainingTestingDatasets(cnvs, X_dimension="visualized_class")
# If ratio between the two classes is too unbalanced uniformize classes by split majoritary class and adding new pseudo CNVs to minority class
X_train, y_train = DigCnvPreProcessing.uniformizeClassesSizes(X_train, y_train, 17, 0.4, 0.5)
# Create a DigCNV model
model = digCnvModel.DigCnvModel()
model.createDigCnvClassifier()
# Train the DigCNV model with the given training dataset
model.trainDigCnvModel(training_data=X_train, training_cat=y_train)
# Analyse classification accuracy for the trained model
predicted_cnvs = model.evaluateCnvClassification(testing_df=X_test, expected_values=y_test, images_dir_path="<Path to the image output directory>")
```
#### Tune DigCNV hyperparameters
```python
from digcnv import DigCNVTunning
```
## Input files format example
#### PennCNV output
```
chr20:44356194-44378577 numsnp=7 length=22,384 state2,cn=1 /path/to/finalreport/10001 startsnp=rs232258 endsnp=rs380421 conf=16.163
chr9:17583310-17622213 numsnp=21 length=38,904 state5,cn=3 /path/to/finalreport/10001 startsnp=rs1028594 endsnp=rs3808750 conf=101.052
chr10:47543322-47703613 numsnp=47 length=160,292 state5,cn=3 /path/to/finalreport/10001 startsnp=rs11259779 endsnp=rs4128664 conf=156.227
chr6:4263349-4472587 numsnp=69 length=209,239 state2,cn=1 /path/to/finalreport/10002 startsnp=rs6937085 endsnp=rs7746329 conf=120.225
chr6:80608294-80611616 numsnp=6 length=3,323 state2,cn=1 /path/to/finalreport/10002 startsnp=rs17833835 endsnp=rs1887571 conf=20.441
...
```
#### PennCNV quality output
```
SampleID LRR_mean LRR_median LRR_SD BAF_mean BAF_median BAF_SD BAF_DRIFT WF GCWF
10001 -0.0045 0.0000 0.1474 0.5028 0.5000 0.0268 0.000036 -0.0141 -0.0002
10002 0.0056 0.0000 0.1588 0.5027 0.5000 0.0259 0.000000 0.0194 0.0018
10003 -0.0090 0.0000 0.1564 0.5031 0.5000 0.0308 0.000000 0.0132 0.0053
10004 0.0014 0.0000 0.1494 0.5032 0.5000 0.0280 0.000000 0.0184 0.0018
10005 0.0031 0.0000 0.1412 0.5025 0.5000 0.0272 0.000000 0.0170 0.0001
...
```
#### QuantiSNP output
```
Sample Name Chromosome Start Position (bp) End Position (bp) Start Probe ID End Probe ID Length (bp) No. Probes Copy Number Max. Log BF Log BF: State 0 Log BF: State 1 Log BF: State 2 Log BF: State 3 Log BF: State 4 Log BF: State 5 Log BF: State 6
10001 1 31943355 31943355 rs7545865 rs7545865 1 1 1 1.2152 -24.0258 -2.5507 0 1.2152 -16.1517 -19.4123 -25.013
10001 1 111930916 111934304 rs12077338 rs4839132 3389 4 1 4.57286 -42.9409 4.57286 0 2.93527 -29.2398 -35.802 -44.9483
10001 2 44082362 44096010 rs6718187 rs6752551 13649 4 3 0.954579 -62.543 -41.1676 0 -14.003 0.954579 0.576184 -60.5049
10001 2 92308395 92308395 rs4509760 rs4509760 1 1 1 0.0218224 -26.3579 -4.38718 0 0.0218224 -19.0042 -21.9322 -27.2609
10001 3 59820539 59821071 rs1905866 rs17362486 533 2 4 0.33986 -9.54639 -22.8223 0 -3.94108 -6.15468 0.33986 -8.45724
...
```
نیازمندی
| مقدار | نام |
|---|---|
| - | pandas |
| - | configparser |
| - | imbalanced-learn |
| - | scikit-learn |
| - | matplotlib |
| - | seaborn |
| >=0.11 | joblib |
| - | pytest |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.10.4 | Python |
نحوه نصب
نصب پکیج whl DigCNV-0.3.3:
pip install DigCNV-0.3.3.whl
نصب پکیج tar.gz DigCNV-0.3.3:
pip install DigCNV-0.3.3.tar.gz