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توضیحات

Pseudo-Bulking Single-Cell RNA-seq

ویژگی	مقدار
سیستم عامل	-
نام فایل	adpbulk-0.1.3
نام	adpbulk
نسخه کتابخانه	0.1.3
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	-
ایمیل نویسنده	Noam Teyssier <noam.teyssier@ucsf.edu>
آدرس صفحه اصلی	-
آدرس اینترنتی	https://pypi.org/project/adpbulk/
مجوز	-

# adpbulk # Summary Performs pseudobulking of an `AnnData` object based on columns available in the `.obs` dataframe. This was originally intended to be used to pseudo-bulk single-cell RNA-seq data to higher order combinations of the data as to use existing RNA-seq differential expression tools such as `edgeR` and `DESeq2`. An example usage of this would be pseudobulking cells based on their cluster, sample of origin, or CRISPRi guide identity. This is intended to work on both individual categories (i.e. one of the examples) or combinations of categories (two of the three, etc.) # Installation ## From PyPI ```bash pip install adpbulk ``` ## From Github ```bash git clone https://github.com/noamteyssier/adpbulk cd adpbulk pip install . pytest -v ``` # Usage This package is intended to be used as a python module. ## Single Category Pseudo-Bulk The simplest use case is to aggregate on a single category. This will aggregate all the observations belonging to the same class within the category and return a pseudo-bulked matrix with dimensions equal to the number of values within the category. ```python3 from adpbulk import ADPBulk # initialize the object adpb = ADPBulk(adat, "category_name") # perform the pseudobulking pseudobulk_matrix = adpb.fit_transform() # retrieve the sample meta data (useful for easy incorporation with edgeR) sample_meta = adpb.get_meta() ``` ## Multiple Category Pseudo-Bulk A common use case is to aggregate on multiple categories. This will aggregate all observations beloging to the combination of classes within two categories and return a pseudo-bulked matrix with dimensions equal to the number of values of nonzero intersections between categories. ```python3 from adpbulk import ADPBulk # initialize the object adpb = ADPBulk(adat, ["category_a", "category_b"]) # perform the pseudobulking pseudobulk_matrix = adpb.fit_transform() # retrieve the sample meta data (useful for easy incorporation with edgeR) sample_meta = adpb.get_meta() ``` ## Pseudo-Bulk using raw counts Some differential expression software expects the counts to be untransformed counts. SCANPY uses the `.raw` attribute in its `AnnData` objects to store the initial `AnnData` object before transformation. If you'd like to perform the pseudo-bulk aggregation using these raw counts you can provide the `use_raw=True` flag. ```python3 from adpbulk import ADPBulk # initialize the object w. aggregation on the `.raw` attribute adpb = ADPBulk(adat, ["category_a", "category_b"], use_raw=True) # perform the pseudobulking pseudobulk_matrix = adpb.fit_transform() # retrieve the sample meta data (useful for easy incorporation with edgeR) sample_meta = adpb.get_meta() ``` ## Alternative Aggregation Options It may also be useful to aggregate using an alternative function besides the sum - this option will allow you to choose between sum, mean, and median as an aggregation function. ```python3 from adpbulk import ADPBulk # initialize the object w. an alternative aggregation option # aggregation options are: sum, mean, and median # default aggregation is sum adpb = ADPBulk(adat, "category", method="mean") # perform the pseudobulking pseudobulk_matrix = adpb.fit_transform() # retrieve the sample meta data (useful for easy incorporation with edgeR) sample_meta = adpb.get_meta() ``` ## Alternative Formatting Options ```python3 from adpbulk import ADPBulk # initialize the object w. alternative name formatting options adpb = ADPBulk(adat, ["category_a", "category_b"], name_delim=".", group_delim="::") # perform the pseudobulking pseudobulk_matrix = adpb.fit_transform() # retrieve the sample meta data (useful for easy incorporation with edgeR) sample_meta = adpb.get_meta() ``` ## Example `AnnData` Function Here is a function to generate an `AnnData` object to test the module or to play with the object if unfamiliar. ```python3 import numpy as np import pandas as pd import anndata as ad def build_adat(SIZE_N=100, SIZE_M=100): """ creates an anndata for testing """ # generates random values (mock transformed data) mat = np.random.random((SIZE_N, SIZE_M)) # generates random values (mock raw count data) raw = np.random.randint(0, 1000, (SIZE_N, SIZE_M)) # creates the observations and categories obs = pd.DataFrame({ "cell": [f"b{idx}" for idx in np.arange(SIZE_N)], "cA": np.random.choice(np.random.choice(5)+1, SIZE_N), "cB": np.random.choice(np.random.choice(5)+1, SIZE_N), "cC": np.random.choice(np.random.choice(5)+1, SIZE_N), "cD": np.random.choice(np.random.choice(5)+1, SIZE_N), }).set_index("cell") # creates the variables (genes) and categories var = pd.DataFrame({ "symbol": [f"g{idx}" for idx in np.arange(SIZE_M)], "cA": np.random.choice(np.random.choice(5)+1, SIZE_M), "cB": np.random.choice(np.random.choice(5)+1, SIZE_M), "cC": np.random.choice(np.random.choice(5)+1, SIZE_M), "cD": np.random.choice(np.random.choice(5)+1, SIZE_M), }).set_index("symbol") # Creates the `AnnData` object adat = ad.AnnData( X=mat, obs=obs, var=var) # Creates an `AnnData` object to simulate the `.raw` attribute adat_raw = ad.AnnData( X=raw, obs=obs, var=var) # Sets the `.raw` attribute adat.raw = adat_raw return adat adat = build_adat() ```

نیازمندی

مقدار	نام
-	anndata>=0.7.4
-	numpy>=1.17.0
-	pandas>=0.21
-	tqdm
-	pytest

نحوه نصب

نصب پکیج whl adpbulk-0.1.3:

pip install adpbulk-0.1.3.whl

نصب پکیج tar.gz adpbulk-0.1.3:

pip install adpbulk-0.1.3.tar.gz