DNA encoded library analysis. This is companion software to NGS-Barcode-Count for outputing analysis and graphs.
Anaconda python required for the instructions below
conda create -n del python=3.9
conda activate del
From pypl:
pip install delanalysis
From source:
git clone https://github.com/Roco-scientist/DEL-Analysis.git
cd DEL-Analysis
pip install --use-feature=in-tree-build .
Output files from NGS-Barcode-Count
conda activate del
python
All code below is within python
import delanalysis
# Import merged data output from NGS-Barcode-Count. This creates a DelDataMerged object
merged_data = delanalysis.read_merged("test_counts.all.csv")
# zscore, then quantile_normalize, then subtract background which is 'test_1'
merged_data_transformed = merged_data.binomial_zscore().subtract_background(background_name="test_1")
# Create a 2d comparison graph between 'test_2' and 'test_3' in the current directory and with a low end cutoff of 4
merged_data_transformed.comparison_graph(x_sample="test_2", y_sample="test_3", out_dir="./", min_score=4)
# Creates a DelDataSample object from a single sample from the merged object
test_2_data_transformed = merged_data_transformed.sample_data(sample_name="test_2")
# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
test_2_data_transformed.graph_3d(out_dir="./", min_score=4)
# Create a 2d graph within the current directory and a low end cutoff of 4
test_2_data_transformed.graph_2d(out_dir="./", min_score=4)
# Can all be done in one line
delanalysis.read_merged("test_counts.all.csv").binomial_zscore().subtract_background("test_1").sample_data("test_2").graph_3d("./", 4)
# Create a comparison graph for tri, di, and mono synthons
full = read_merged("../../test_del/test.all.csv")
double = read_merged("../../test_del/test.all.Double.csv")
single = read_merged("../../test_del/test.all.Single.csv")
full_double = full.concat(double)
full_double_single = full_double.concat(single)
full_double_single_zscore = full_double_single.binomial_zscore_sample_normalized()
full_double_single_zscore.subtract_background("test_1", inplace=True)
full_double_single_zscore.comparison_graph("test_2", "test_3", "../../test_del/", 0.002)
All code below is within python
import delanalysis
# Import sample data output from NGS-Barcode-Count. This creates a DelDataSample object
sample_data = delanalysis.read_sample("test_1.csv")
# zscore
sample_data_zscore = sample_data.binomial_zscore()
# Create a 3d graph with each axis being a barcode within the current directory and a low end cutoff of 4
sample_data_zscore.graph_3d(out_dir="./", min_score=4)
# Create a 2d graph within the current directory and a low end cutoff of 4
sample_data_zscore.graph_2d(out_dir="./", min_score=4)
The actual graphs will be interactive HTML graphs with hover data etc.
From comparison_graph()
From graph_2d()
From graph_3d()
| Method | Description |
|---|---|
| read_merged(file_path) | Creates a DelDataMerged object which can use the methods below |
| read_sample(file_path) | Creates a DelDataSample object which can use the methods below |
Used with either delanalysis.read_merged() or delanalysis.read_sample() objects
| Method | Description |
|---|---|
| building_block_columns() | returns all column names which contain building block info |
| data_columns() | returns all column names which contain data |
| data_descriptor() | Returns data_type with underscores for file output |
| data_type | The data type of the DelData |
| to_csv(out_file) | Writes the DelData object to the out_file in csv format |
| zscore(inplace=False) | z-scores the data |
| binomial_zscore(del_library_size, inplace=False) | z-scores the data using the binomial distribution standard deviation |
| binomial_zscore_sample_normalized(del_library_size, inplace=False) | z-scores the data using the binomial distribution standard deviation and normalizes by sqrt(n). See: Quantitative Comparison of Enrichment... |
| enrichment(del_library_size, inplace=False) | count * library_size/ total_counts |
| update_synthon_numbers(unique_synthons_per_barcode: List[int]) | The number of unique synthons is inferred by the total uniques found in the data. These numbers can be updated with this function |
Used with delanalysis.read_merged() which creates a DelDataMerged object
| Method | Description |
|---|---|
| quantile_normalize(inplace=False) | quantile normalizes the data |
| sample_enrichment(inplace=False) | (sample_count/total_sample_count)/(non_sample_count/total_non_sample_count). Still experimental as if the count only happens in one sample, a div 0 error occurs |
| subtract_background(background_name, inplace=False) | subtracts the background_name sample from all other samples |
| reduce(min_score, inplace=False) | Removes all rows from the data where no samples have a score above the min_score |
| merge(deldata, inplace=False) | Merges DelDataMerged data into the current DelDataMerged object |
| sample_data(sample_name) | Returns a DelDataSample object from the DelDataMerged object. This is needed for the 2d and 3d graph |
| select_samples(sample_names: List, inplace=False) | Reduces the data to the listed sample names |
| comparison_graph(x_sample, y_sample, out_dir, min_score=0) | Outputs a comparison graph of x_sample vs y_sample names. |
Used with delanalysis.read_sample() which creates a DelDataSample object
| Method | Description |
|---|---|
| reduce(min_score, inplace=False) | reduces the data to only data greater than the min_score |
| max_score() | Returns the maximum score within the data |
| data_column() | Returns the data column name |
| graph_2d(out_dir, min_score=0) | Produces two subplot 2d graphs for the different barcodes of a DelDataSample. |
| graph_3d(out_dir, min_score=0) | Produces 3d graphs for the different barcodes of a DelDataSample. |