From 60b7c7099a818f2dc04cc5f4feee476ab99a3aba Mon Sep 17 00:00:00 2001 From: Alexandra Valeanu Date: Mon, 21 Oct 2024 21:37:43 +0300 Subject: [PATCH 1/4] Migrate from setup.py to pyproject.toml for up-to-date project configuration --- pyproject.toml | 32 ++++++++++++++++++++++++++++++++ setup.py | 36 ------------------------------------ 2 files changed, 32 insertions(+), 36 deletions(-) create mode 100644 pyproject.toml delete mode 100644 setup.py diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 0000000..1713fb3 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,32 @@ +[build-system] +requires = ["setuptools", "wheel"] +build-backend = "setuptools.build_meta" + +[project] +name = "kinex" +version = "1.0.2" +description = "A python package to compute kinase scoring and enrichment" +authors = [ + { name = "Alexandra Valeanu", email = "valeanualexandra17@gmail.com" } +] +requires-python = ">=3.8" +dependencies = [ + "scipy >= 1.10.0", + "numpy >= 1.19.5", + "nbformat >= 4.2.0", + "pandas", + "statsmodels", + "plotly", + "scikit-learn", + "umap-learn", + "importlib-resources" +] + +[project.optional-dependencies] +dev = [ + "sphinx", + "furo" +] + +[tool.setuptools] +package-dir = { "" = "src" } \ No newline at end of file diff --git a/setup.py b/setup.py deleted file mode 100644 index 71a49d7..0000000 --- a/setup.py +++ /dev/null @@ -1,36 +0,0 @@ -from setuptools import setup - -setup( - name="kinex", - version="1.0.2", - description="A python package to compute kinase scoring and enrichment", - packages=["kinex", "kinex.data", "kinex.tests", "kinex.tests.data"], - package_dir={"kinex": "src", - "kinex.data": "src/data", - "kinex.tests": "tests", - "kinex.tests.data": "tests/data"}, - package_data={'kinex.data': ['experiments.json', 'groups.json', 'pssm_table.csv'], - 'kinex.tests.data': ['test_scoring_matrix.csv', 'test_pssm_table.csv', 'test_input_sites.csv']}, - include_package_data=True, - url="https://github.com/bedapub/kinex", - author="Alexandra Valeanu", - author_email="valeanualexandra17@gmail.com", - python_requires='>=3.8', - install_requires=[ - "scipy >= 1.10.0", - "numpy >= 1.19.5", - "nbformat>=4.2.0", - "pandas", - "statsmodels", - "plotly", - "scikit-learn", - "umap-learn", - "importlib-resources" - ], - extras_require={ - "dev": [ - "sphinx", - "furo", - ], - }, -) From c6de0f3dcd4755f4793c9d702040c1836ace6f77 Mon Sep 17 00:00:00 2001 From: Alexandra Valeanu Date: Mon, 21 Oct 2024 21:48:56 +0300 Subject: [PATCH 2/4] Add updated sequence-related classes and functions --- src/sequence.py | 194 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 194 insertions(+) create mode 100644 src/sequence.py diff --git a/src/sequence.py b/src/sequence.py new file mode 100644 index 0000000..a7e6727 --- /dev/null +++ b/src/sequence.py @@ -0,0 +1,194 @@ +from enum import Enum +import pandas as pd + +allowed_characters = { + "P", "G", "A", "C", "S", "T", "V", "I", "L", "M", "F", "Y", "W", "H", + "K", "R", "Q", "N", "D", "E", "s", "t", "y", "X", "_", "*" +} + + +def is_central_sequence_valid(sequence_string: str) -> bool: + if sequence_string[len(sequence_string) // 2] not in ("S", "T", "Y", "s", "t", "y"): + return False + for aminoacid in sequence_string: + if aminoacid not in [x for x in allowed_characters if x != "*"]: + return False + return True + + +def is_separator_sequence_valid(sequence_string: str, separator: str) -> bool: + if separator * 2 in sequence_string: + return False + + valid_patterns = get_valid_patterns(separator) + if not any(pattern in sequence_string for pattern in valid_patterns): + return False + + for aminoacid in sequence_string: + if aminoacid not in allowed_characters: + return False + return True + + +def get_valid_patterns(separator): + return [ + f"S{separator}", + f"T{separator}", + f"Y{separator}", + f"s{separator}", + f"t{separator}", + f"y{separator}" + ] + + +class SequenceSeparator(Enum): + ASTERISK = "*" + PH = "(ph)" + + +class SequenceType(Enum): + SER_THR = ["S", "T"] + TYR = ["Y"] + + +class Sequence: + def __init__(self, sequence_string: str, sequence_type: SequenceType) -> None: + self.sequence_string = sequence_string + self.sequence_type = sequence_type + + def validate_sequence(self): + pass + + def preprocess_sequence(self): + pass + + def get_split_sequence(self) -> list: + pass + + def get_columns_list(self): + skip_characters = {"_", "X"} + columns = [] + + columns_range = (-5, 4) + if self.sequence_type == SequenceType.SER_THR: + columns_range = (-5, 4) + elif self.sequence_type == SequenceType.TYR: + columns_range = (-5, 5) + + part_id = 0 + parts = self.get_split_sequence() + for part in parts: + if part_id == 0: + part = part[::-1] + for position, aminoacid in enumerate(part): + if aminoacid in skip_characters: + continue + pos = (position + 1) * (-1) if part_id == 0 else position + 1 + if columns_range[0] <= pos <= columns_range[1]: + columns.append(f"{pos}{aminoacid}") + part_id += 1 + return columns + + def get_sequence_scores(self, pssm_table: pd.DataFrame, favorability: bool = False) -> list: + pass + + +class SeparatorSequence(Sequence): + def __init__(self, sequence_string: str, separator: SequenceSeparator, sequence_type: SequenceType) -> None: + super().__init__(sequence_string, sequence_type) + self.separator = separator + self.sequences = [] + + def preprocess_sequence(self): + sequences = self.sequence_string.split(self.separator.value) + for index, item in enumerate(sequences): + if index != len(sequences) - 1: + sequences[index] = item[:-1] + item[-1].lower() + for index in range(len(sequences) - 1): + seq = f"{sequences[:index + 1]}{self.separator.value}{sequences[index + 1:]}" + if not is_separator_sequence_valid(seq, self.separator.value): + continue + sequences.append(seq) + self.sequences = sequences + + def validate_sequence(self) -> None: + if len(self.sequences) == 0: + raise ValueError("Invalid sequence") + + def get_split_sequence(self) -> list: + parts = self.sequence_string.split(self.separator.value) + parts[0] = parts[0][:-1] + return parts + + def get_sequence_scores(self, pssm_table: pd.DataFrame, favorability: bool = False) -> list: + score_results = [] + for sequence in self.sequences: + columns_list = self.get_columns_list() + if favorability: + seq_upper = sequence.upper() + if f"S{self.separator.value}" in seq_upper: + columns_list.append("0S") + elif f"T{self.separator.value}" in seq_upper: + columns_list.append("0T") + score_results.append(get_score(columns_list, pssm_table)) + return score_results + + +class CentralSequence(Sequence): + def __init__(self, sequence_string: str, sequence_type: SequenceType) -> None: + super().__init__(sequence_string, sequence_type) + + def preprocess_sequence(self): + pass + + def validate_sequence(self): + if not is_central_sequence_valid(self.sequence_string): + raise ValueError("Invalid sequence") + + def get_split_sequence(self) -> list: + sequence_length = len(self.sequence_string) + return [ + self.sequence_string[:sequence_length // 2], + self.sequence_string[sequence_length // 2 + 1:] + ] + + def get_sequence_scores(self, pssm_table: pd.DataFrame, favorability: bool = False) -> list: + columns = self.get_columns_list() + if favorability: + seq_upper = self.sequence_string.upper() + if seq_upper[len(seq_upper) // 2] == "S": + columns.append("0S") + elif seq_upper[len(seq_upper) // 2] == "T": + columns.append("0T") + return [get_score(columns, pssm_table)] + + +def get_sequence_type(aminoacid: str) -> SequenceType: + aminoacid = aminoacid.upper() + for sequence_type in SequenceType: + if aminoacid in sequence_type.value: + return sequence_type + raise ValueError(f"Unsupported sequence type. Supported types: SER, THR, TYR") + + +def get_sequence_object(sequence: str) -> Sequence: + for separator in SequenceSeparator: + position = sequence.find(separator.value) + if position > 0: + sequence_type = get_sequence_type(sequence[position - 1]) + return SeparatorSequence(sequence, separator, sequence_type) + if len(sequence) % 2 == 1: + sequence_type = get_sequence_type(sequence[int(len(sequence) / 2)]) + return CentralSequence(sequence, sequence_type) + raise ValueError( + f"Unsupported sequence format. Supported formats: *, (ph) and central") + + +def get_score(columns: list, pssm: pd.DataFrame) -> pd.DataFrame: + pssm = pssm.reset_index() + columns.append("kinase") + score_df = pssm[columns] + score_df.insert(0, "score", score_df.prod(axis=1, numeric_only=True)) + score_df = score_df[["kinase", "score"]] + score_df = score_df.set_index("kinase") + return score_df \ No newline at end of file From e6b933e2dd546e49e3e5c02397d8d5ba101ff591 Mon Sep 17 00:00:00 2001 From: Alexandra Valeanu Date: Mon, 21 Oct 2024 22:53:13 +0300 Subject: [PATCH 3/4] Add tyrosine data and change functions --- src/data/__init__.py | 17 - src/data/groups.json | 1 - src/{ => kinex}/__init__.py | 0 src/{ => kinex}/comparison.py | 2 +- src/{ => kinex}/enrichment.py | 173 ++++------ src/{ => kinex}/functions.py | 0 src/{ => kinex}/kinex.py | 308 ++++++++---------- src/kinex/resources/__init__.py | 43 +++ .../resources}/experiments.json | 0 .../resources/pssm_table_ser_thr.csv} | 0 src/kinex/resources/pssm_table_tyr.csv | 94 ++++++ src/kinex/resources/ser_thr_family.json | 305 +++++++++++++++++ .../resources/ser_thr_family_colors.json | 13 + src/kinex/resources/tyr_family.json | 95 ++++++ src/kinex/resources/tyr_family_colors.json | 32 ++ src/kinex/score.py | 49 +++ src/{ => kinex}/sequence.py | 0 src/{ => kinex}/table2x2.py | 0 src/score.py | 52 --- 19 files changed, 826 insertions(+), 358 deletions(-) delete mode 100644 src/data/__init__.py delete mode 100644 src/data/groups.json rename src/{ => kinex}/__init__.py (100%) rename src/{ => kinex}/comparison.py (99%) rename src/{ => kinex}/enrichment.py (58%) rename src/{ => kinex}/functions.py (100%) rename src/{ => kinex}/kinex.py (53%) create mode 100644 src/kinex/resources/__init__.py rename src/{data => kinex/resources}/experiments.json (100%) rename src/{data/pssm_table.csv => kinex/resources/pssm_table_ser_thr.csv} (100%) create mode 100644 src/kinex/resources/pssm_table_tyr.csv create mode 100644 src/kinex/resources/ser_thr_family.json create mode 100644 src/kinex/resources/ser_thr_family_colors.json create mode 100644 src/kinex/resources/tyr_family.json create mode 100644 src/kinex/resources/tyr_family_colors.json create mode 100644 src/kinex/score.py rename src/{ => kinex}/sequence.py (100%) rename src/{ => kinex}/table2x2.py (100%) delete mode 100644 src/score.py diff --git a/src/data/__init__.py b/src/data/__init__.py deleted file mode 100644 index 2c48ddf..0000000 --- a/src/data/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from importlib import resources -import json -import pandas as pd - -def get_pssm() -> pd.DataFrame: - with resources.path("kinex.data", "pssm_table.csv") as df: - return pd.read_csv(df, index_col=0) - -def get_groups() -> dict: - with resources.path("kinex.data", "groups.json") as file_path: - with open(file_path) as json_file: - return json.load(json_file) - -def get_experiments() -> dict: - with resources.path("kinex.data", "experiments.json") as file_path: - with open(file_path) as json_file: - return json.load(json_file) \ No newline at end of file diff --git a/src/data/groups.json b/src/data/groups.json deleted file mode 100644 index f1107ea..0000000 --- a/src/data/groups.json +++ /dev/null @@ -1 +0,0 @@ -{"AAK1": "Other", "ACVR2A": "TKL", "ACVR2B": "TKL", "AKT1": "AGC", "AKT2": "AGC", "AKT3": "AGC", "ALK2": "TKL", "ALK4": "TKL", "ALPHAK3": "Alpha", "AMPKA1": "CAMK", "AMPKA2": "CAMK", "ANKRD3": "TKL", "ASK1": "STE", "ATM": "PIKK", "ATR": "PIKK", "AURA": "Other", "AURB": "Other", "AURC": "Other", "BCKDK": "PDHK", "BIKE": "Other", "BMPR1A": "TKL", "BMPR1B": "TKL", "BMPR2": "TKL", "BRAF": "TKL", "BRSK1": "CAMK", "BRSK2": "CAMK", "BUB1": "Other", "CAMK1A": "CAMK", "CAMK1B": "CAMK", "CAMK1D": "CAMK", "CAMK1G": "CAMK", "CAMK2A": "CAMK", "CAMK2B": "CAMK", "CAMK2D": "CAMK", "CAMK2G": "CAMK", "CAMK4": "CAMK", "CAMKK1": "Other", "CAMKK2": "Other", "CAMLCK": "CAMK", "CDC7": "Other", "CDK1": "CMGC", "CDK10": "CMGC", "CDK12": "CMGC", "CDK13": "CMGC", "CDK14": "CMGC", "CDK16": "CMGC", "CDK17": "CMGC", "CDK18": "CMGC", "CDK19": "CMGC", "CDK2": "CMGC", "CDK3": "CMGC", "CDK4": "CMGC", "CDK5": "CMGC", "CDK6": "CMGC", "CDK7": "CMGC", "CDK8": "CMGC", "CDK9": "CMGC", "CDKL1": "CMGC", "CDKL5": "CMGC", "CHAK1": "Alpha", "CHAK2": "Alpha", "CHK1": "CAMK", "CHK2": "CAMK", "CK1A": "CK1", "CK1A2": "CK1", "CK1D": "CK1", "CK1E": "CK1", "CK1G1": "CK1", "CK1G2": "CK1", "CK1G3": "CK1", "CK2A1": "Other", "CK2A2": "Other", "CLK1": "CMGC", "CLK2": "CMGC", "CLK3": "CMGC", "CLK4": "CMGC", "COT": "STE", "CRIK": "AGC", "DAPK1": "CAMK", "DAPK2": "CAMK", "DAPK3": "CAMK", "DCAMKL1": "CAMK", "DCAMKL2": "CAMK", "DLK": "TKL", "DMPK1": "AGC", "DNAPK": "PIKK", "DRAK1": "CAMK", "DSTYK": "TKL", "DYRK1A": "CMGC", "DYRK1B": "CMGC", "DYRK2": "CMGC", "DYRK3": "CMGC", "DYRK4": "CMGC", "EEF2K": "Alpha", "ERK1": "CMGC", "ERK2": "CMGC", "ERK5": "CMGC", "ERK7": "CMGC", "FAM20C": "FAM20C", "GAK": "Other", "GCK": "STE", "GCN2": "Other", "GRK1": "AGC", "GRK2": "AGC", "GRK3": "AGC", "GRK4": "AGC", "GRK5": "AGC", "GRK6": "AGC", "GRK7": "AGC", "GSK3A": "CMGC", "GSK3B": "CMGC", "HASPIN": "Other", "HGK": "STE", "HIPK1": "CMGC", "HIPK2": "CMGC", "HIPK3": "CMGC", "HIPK4": "CMGC", "HPK1": "STE", "HRI": "Other", "HUNK": "CMGC", "ICK": "CMGC", "IKKA": "Other", "IKKB": "Other", "IKKE": "Other", "IRAK1": "TKL", "IRAK4": "TKL", "IRE1": "Other", "IRE2": "Other", "JNK1": "CMGC", "JNK2": "CMGC", "JNK3": "CMGC", "KHS1": "STE", "KHS2": "STE", "KIS": "Other", "LATS1": "AGC", "LATS2": "AGC", "LKB1": "CAMK", "LOK": "STE", "LRRK2": "TKL", "MAK": "CMGC", "MAP3K15": "STE", "MAPKAPK2": "CAMK", "MAPKAPK3": "CAMK", "MAPKAPK5": "CAMK", "MARK1": "CAMK", "MARK2": "CAMK", "MARK3": "CAMK", "MARK4": "CAMK", "MASTL": "AGC", "MEK1": "STE", "MEK2": "STE", "MEK5": "STE", "MEKK1": "STE", "MEKK2": "STE", "MEKK3": "STE", "MEKK6": "STE", "MELK": "CAMK", "MINK": "STE", "MLK1": "TKL", "MLK2": "TKL", "MLK3": "TKL", "MLK4": "STE", "MNK1": "CAMK", "MNK2": "CAMK", "MOK": "CMGC", "MOS": "Other", "MPSK1": "Other", "MRCKA": "AGC", "MRCKB": "AGC", "MSK1": "AGC", "MSK2": "AGC", "MST1": "STE", "MST2": "STE", "MST3": "STE", "MST4": "STE", "MTOR": "PIKK", "MYLK4": "CAMK", "MYO3A": "STE", "MYO3B": "STE", "NDR1": "AGC", "NDR2": "AGC", "NEK1": "Other", "NEK11": "Other", "NEK2": "Other", "NEK3": "Other", "NEK4": "Other", "NEK5": "Other", "NEK6": "Other", "NEK7": "Other", "NEK8": "Other", "NEK9": "Other", "NIK": "STE", "NIM1": "CAMK", "NLK": "CMGC", "NUAK1": "CAMK", "NUAK2": "CAMK", "OSR1": "STE", "P38A": "CMGC", "P38B": "CMGC", "P38D": "CMGC", "P38G": "CMGC", "P70S6K": "AGC", "P70S6KB": "AGC", "P90RSK": "AGC", "PAK1": "STE", "PAK2": "STE", "PAK3": "STE", "PAK4": "STE", "PAK5": "STE", "PAK6": "STE", "PASK": "CAMK", "PBK": "Other", "PDHK1": "PDHK", "PDHK4": "PDHK", "PDK1": "AGC", "PERK": "Other", "PHKG1": "CAMK", "PHKG2": "CAMK", "PIM1": "CAMK", "PIM2": "CAMK", "PIM3": "CAMK", "PINK1": "Other", "PKACA": "AGC", "PKACB": "AGC", "PKACG": "AGC", "PKCA": "AGC", "PKCB": "AGC", "PKCD": "AGC", "PKCE": "AGC", "PKCG": "AGC", "PKCH": "AGC", "PKCI": "AGC", "PKCT": "AGC", "PKCZ": "AGC", "PKG1": "AGC", "PKG2": "AGC", "PKN1": "AGC", "PKN2": "AGC", "PKN3": "AGC", "PKR": "Other", "PLK1": "Other", "PLK2": "Other", "PLK3": "Other", "PLK4": "Other", "PRKD1": "CAMK", "PRKD2": "CAMK", "PRKD3": "CAMK", "PRKX": "AGC", "PRP4": "CMGC", "PRPK": "Other", "QIK": "CAMK", "QSK": "CAMK", "RAF1": "TKL", "RIPK1": "TKL", "RIPK2": "TKL", "RIPK3": "TKL", "ROCK1": "AGC", "ROCK2": "AGC", "RSK2": "AGC", "RSK3": "AGC", "RSK4": "AGC", "SBK": "Other", "SGK1": "AGC", "SGK3": "AGC", "SIK": "CAMK", "SKMLCK": "CAMK", "SLK": "STE", "SMG1": "PIKK", "SMMLCK": "CAMK", "SNRK": "CAMK", "SRPK1": "CMGC", "SRPK2": "CMGC", "SRPK3": "CMGC", "SSTK": "CAMK", "STK33": "CAMK", "STLK3": "STE", "TAK1": "TKL", "TAO1": "STE", "TAO2": "STE", "TAO3": "STE", "TBK1": "Other", "TGFBR1": "TKL", "TGFBR2": "TKL", "TLK1": "Other", "TLK2": "Other", "TNIK": "STE", "TSSK1": "CAMK", "TSSK2": "CAMK", "TTBK1": "CK1", "TTBK2": "CK1", "TTK": "Other", "ULK1": "Other", "ULK2": "Other", "VRK1": "CK1", "VRK2": "CK1", "WNK1": "Other", "WNK3": "Other", "WNK4": "Other", "YANK2": "AGC", "YANK3": "AGC", "YSK1": "STE", "YSK4": "STE", "ZAK": "STE"} \ No newline at end of file diff --git a/src/__init__.py b/src/kinex/__init__.py similarity index 100% rename from src/__init__.py rename to src/kinex/__init__.py diff --git a/src/comparison.py b/src/kinex/comparison.py similarity index 99% rename from src/comparison.py rename to src/kinex/comparison.py index 81a789b..029f82f 100644 --- a/src/comparison.py +++ b/src/kinex/comparison.py @@ -8,7 +8,7 @@ import plotly.express as px import plotly.graph_objects as go from kinex.functions import get_distances -from kinex.data import get_experiments +from kinex.resources import get_experiments class Comparison: """ diff --git a/src/enrichment.py b/src/kinex/enrichment.py similarity index 58% rename from src/enrichment.py rename to src/kinex/enrichment.py index a5de672..b03cae0 100644 --- a/src/enrichment.py +++ b/src/kinex/enrichment.py @@ -3,46 +3,28 @@ import plotly.express as px from statsmodels.stats.multitest import multipletests +from kinex.sequence import SequenceType from kinex.table2x2 import Table2x2 -from kinex.data import get_groups +from kinex.resources import get_ser_thr_family, get_ser_thr_family_colors, get_tyr_family, get_tyr_family_colors class Enrichment: - """ - Enrichment results including all the necessary information for the input sites - - Attributes - ---------- - enrichment_table: pandas.DataFrame - Table containing enrichment analysis results. - input_sites: pandas.DataFrame - A DataFrame containing the phosphosite sequences in the first column, logarithmised Fold Change in the second column, - regulation in the third column, and top15 kinases most likely to target each sequence in the fourth column. - failed_sites: list - A list of the sites labeled invalid by the check_sequence function. - total_upregulated: int - Total number of upregulated phosphosites - total_downregulated: int - Total number of downregulated phosphosites - total_unregulated: int - Total number of unregulated phosphosites - - Methods - ------- - plot(self, use_adjusted_pval: bool = False) -> None - Vulcano plot of the side displaying significant enrichment for each kinase vs the corresponding p-value. - - """ - - def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, failed_sites: list, total_upregulated: int, total_downregulated: int, total_unregulated: int, all_kinases: set) -> None: - - self.enrichment_table = enrichment_table - self.total_upregulated = total_upregulated - self.total_downregulated = total_downregulated - self.total_unregulated = total_unregulated - self.input_sites = input_sites - self.failed_sites = failed_sites - + def __init__(self, sequence_type: SequenceType, all_kinases: set): + self.sequence_type = sequence_type + self.total_upregulated = 0 + self.total_downregulated = 0 + self.total_unregulated = 0 + self.regulation_list = [] + self.top15_kinases_list = [] + self.enrichment_table = pd.DataFrame(columns=['kinase', 'upregulated', 'downregulated', 'unregulated']) + self.all_kinases = all_kinases + + def adjust_background_sites(self): + if self.total_unregulated == 0: + self.total_unregulated = np.min( + [self.total_upregulated, self.total_downregulated]) / 2 + + def fisher_statistics(self): new_columns = [ "upregulated_enrichment_value", "upregulated_enrichment_value_log2", @@ -85,9 +67,9 @@ def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, fa upregulated_enrichment_value = 0 upregulated_p_value = 1 self.enrichment_table.loc[i, - "upregulated_enrichment_value_log2"] = 0 + "upregulated_enrichment_value_log2"] = 0 self.enrichment_table.loc[i, - "upregulated_p_value_log10_abs"] = 0 + "upregulated_p_value_log10_abs"] = 0 else: upregulated_contingency_table = Table2x2([[upregulated_hit, self.total_upregulated - upregulated_hit], [unregulated_hit, self.total_unregulated - unregulated_hit]], @@ -106,14 +88,15 @@ def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, fa downregulated_enrichment_value = 0 downregulated_p_value = 1 self.enrichment_table.loc[i, - "downregulated_enrichment_value_log2"] = 0 + "downregulated_enrichment_value_log2"] = 0 self.enrichment_table.loc[i, - "downregulated_p_value_log10_abs"] = 0 + "downregulated_p_value_log10_abs"] = 0 else: - downregulated_contingency_table = Table2x2([[downregulated_hit, total_downregulated - downregulated_hit], - [unregulated_hit, total_unregulated - unregulated_hit]], - shift_zeros=True - ) + downregulated_contingency_table = Table2x2( + [[downregulated_hit, self.total_downregulated - downregulated_hit], + [unregulated_hit, self.total_unregulated - unregulated_hit]], + shift_zeros=True + ) downregulated_enrichment_value = downregulated_contingency_table.odds_ratio() downregulated_p_value = downregulated_contingency_table.p_val( mode="greater") @@ -125,30 +108,30 @@ def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, fa # Set the enrichment and p values self.enrichment_table.loc[i, - "upregulated_enrichment_value"] = upregulated_enrichment_value + "upregulated_enrichment_value"] = upregulated_enrichment_value self.enrichment_table.loc[i, - "upregulated_p_value"] = upregulated_p_value + "upregulated_p_value"] = upregulated_p_value self.enrichment_table.loc[i, - "downregulated_enrichment_value"] = downregulated_enrichment_value + "downregulated_enrichment_value"] = downregulated_enrichment_value self.enrichment_table.loc[i, - "downregulated_p_value"] = downregulated_p_value + "downregulated_p_value"] = downregulated_p_value # Determine the dominant direction, either upregulated or downregulated. if upregulated_enrichment_value > downregulated_enrichment_value: self.enrichment_table.loc[i, - "dominant_direction"] = "upregulated set" + "dominant_direction"] = "upregulated set" self.enrichment_table.loc[i, "dominant_enrichment_value_log2"] = self.enrichment_table.loc[i, - "upregulated_enrichment_value_log2"] + "upregulated_enrichment_value_log2"] self.enrichment_table.loc[i, "dominant_p_value_log10_abs"] = self.enrichment_table.loc[i, - "upregulated_p_value_log10_abs"] + "upregulated_p_value_log10_abs"] else: self.enrichment_table.loc[i, - "dominant_direction"] = "downregulated set" + "dominant_direction"] = "downregulated set" self.enrichment_table.loc[i, "dominant_enrichment_value_log2"] = self.enrichment_table.loc[i, - "downregulated_enrichment_value_log2"] + "downregulated_enrichment_value_log2"] self.enrichment_table.loc[i, "dominant_p_value_log10_abs"] = self.enrichment_table.loc[i, - "downregulated_p_value_log10_abs"] + "downregulated_p_value_log10_abs"] # Calculate adjusted p values upregulated_adjusted_p_value = multipletests( @@ -159,7 +142,6 @@ def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, fa self.enrichment_table["downregulated_adjusted_p_value"] = downregulated_adjusted_p_value[1] for i in range(len(self.enrichment_table)): - # adjusted p values log10 abs and dominant adjusted p values log10 abs self.enrichment_table.loc[i, "upregulated_adjusted_p_value_log10_abs"] = np.absolute( np.log10(self.enrichment_table.loc[i, "upregulated_adjusted_p_value"])) @@ -168,73 +150,51 @@ def __init__(self, enrichment_table: pd.DataFrame, input_sites: pd.DataFrame, fa if self.enrichment_table.loc[i, "dominant_direction"] == "downregulated set": self.enrichment_table.loc[i, "dominant_adjusted_p_value_log10_abs"] = self.enrichment_table.loc[i, - "downregulated_adjusted_p_value_log10_abs"] + "downregulated_adjusted_p_value_log10_abs"] elif self.enrichment_table.loc[i, "dominant_direction"] == "upregulated set": self.enrichment_table.loc[i, "dominant_adjusted_p_value_log10_abs"] = self.enrichment_table.loc[i, - "upregulated_adjusted_p_value_log10_abs"] + "upregulated_adjusted_p_value_log10_abs"] self.enrichment_table = self.enrichment_table.set_index("kinase") - missing_kinases = list(all_kinases - set(self.enrichment_table.index)) + + missing_kinases = list(self.all_kinases - set(self.enrichment_table.index)) self.enrichment_table = self.enrichment_table.reindex( self.enrichment_table.index.union(missing_kinases), fill_value=0) - def __repr__(self) -> str: - return f"Total number of upregulated phospho-sequences is: {self.total_upregulated}\nTotal number of downregulated phospho-sequences is: {self.total_downregulated}\nTotal number of unregulated phospho-sequences is: {self.total_unregulated}" - def plot(self, use_adjusted_pval: bool = False): - - kinase_family = get_groups() + match self.sequence_type: + case SequenceType.SER_THR: + kinase_family = get_ser_thr_family() + family_colors = get_ser_thr_family_colors() + case SequenceType.TYR: + kinase_family = get_tyr_family() + family_colors = get_tyr_family_colors() + case _: + raise ValueError(f"Invalid sequence type") family = [] - plotting_table = self.enrichment_table[self.enrichment_table.dominant_p_value_log10_abs > 0.3] - - for kinase in plotting_table.index: + for kinase in self.enrichment_table.index: family.append(kinase_family[kinase]) - # Colors of the kinase groups which correspond to the ones from Johnson et al. https://doi.org/10.1038/s41586-022-05575-3 - family_colors = { - "TKL": "#0400A9", - "STE": "#754391", - "CK1": "#499FD2", - "AGC": "#992B2C", - "CAMK": "#DE643A", - "Other": "#957C64", - "CMGC": "#2C643C", - "Alpha": "#C88AB5", - "PDHK": "#316D79", - "PIKK": "#020101", - "FAM20C": "#C22F7F" - } # Plot for 2 different cases: with and without adjusted p-val if use_adjusted_pval: - fig = px.scatter( - plotting_table, - x="dominant_enrichment_value_log2", - y="dominant_adjusted_p_value_log10_abs", - hover_name=plotting_table.index, - color=family, - text=plotting_table.index, - color_discrete_map=family_colors, # Use the defined color mapping - category_orders={"family": [ - 'TKL', 'STE', 'CK1', 'AGC', 'CAMK', 'Other', 'CMGC', 'Alpha', 'PDHK', 'PIKK', 'FAM20C']}, - template="none" - ) + y = "dominant_adjusted_p_value_log10_abs" y_axis_title = "-Log\u2081\u2080(adjusted p-value)" - else: - fig = px.scatter( - plotting_table, - x="dominant_enrichment_value_log2", - y="dominant_p_value_log10_abs", - hover_name=plotting_table.index, - color=family, - text=plotting_table.index, - color_discrete_map=family_colors, # Use the defined color mapping - category_orders={"family": [ - 'TKL', 'STE', 'CK1', 'AGC', 'CAMK', 'Other', 'CMGC', 'Alpha', 'PDHK', 'PIKK', 'FAM20C']}, - template="none" - ) + y = "dominant_p_value_log10_abs" y_axis_title = "-Log\u2081\u2080(p-value)" + fig = px.scatter( + self.enrichment_table, + x="dominant_enrichment_value_log2", + y=y, + hover_name=self.enrichment_table.index, + color=family, + text=self.enrichment_table.index, + color_discrete_map=family_colors, # Use the defined color mapping + # category_orders={"family": category_orders}, + template="none" + ) + # add horizontal line at y = 1.3 which is absolute val of log10(0.05) fig.add_hline( y=1.3, @@ -282,5 +242,4 @@ def plot(self, use_adjusted_pval: bool = False): width=600, height=600 ) -# fig.show() - return fig + return fig \ No newline at end of file diff --git a/src/functions.py b/src/kinex/functions.py similarity index 100% rename from src/functions.py rename to src/kinex/functions.py diff --git a/src/kinex.py b/src/kinex/kinex.py similarity index 53% rename from src/kinex.py rename to src/kinex/kinex.py index 582f01a..a141535 100644 --- a/src/kinex.py +++ b/src/kinex/kinex.py @@ -1,24 +1,17 @@ import pandas as pd import numpy as np -# import time import bisect +from functools import reduce -from kinex.functions import check_sequence, get_sequence_format, score - -from kinex.data import get_pssm +from kinex.resources import get_pssm_ser_thr, get_pssm_tyr from kinex.score import Score from kinex.enrichment import Enrichment -from kinex.comparison import Comparison +from kinex.sequence import get_sequence_object, SequenceType -# import logging -# logging.basicConfig( -# filename="kinex.log", -# level=logging.ERROR, -# format="%(asctime)s %(levelname)s %(message)s", -# datefmt="%Y-%m-%d %H:%M:%S", -# ) +from collections import namedtuple +EnrichmentResults = namedtuple("EnrichmentResults", ["ser_thr", "tyr", "failed_sites"]) class Kinex: """ @@ -26,24 +19,24 @@ class Kinex: Attributes ---------- - pssm : pandas.DataFrame + pssm_ser_thr : pandas.DataFrame Normalised and scaled densiometries from PSPA experiments. The table cotains on rows the kinases and on columns the positions for each aminoacid. - scoring_matrix : pandas.DataFrame + scoring_matrix_ser_thr : pandas.DataFrame Table containing 82,755 experimentally identified Ser/Thr phosphosites that have been scored by 303 Ser or Thr kinase PSSM. - The table allows the ranking of kinases, as well as the calculation of promiscuity index and median percentile for each input sequence. + The table allows the ranking of kinases, as well as the calculation of promiscuity index and median percentile for each input validation. Methods ------- - get_score(self, sequence: str, phospho_priming: bool = False, favorability: bool = False, method: str = 'avg') -> score.Score - Checks the sequence format and it's validity; computes the scores and ranks the kinases. + get_score(self, validation: str, phospho_priming: bool = False, favorability: bool = False, method: str = 'avg') -> score.Score + Checks the validation format and it's validity; computes the scores and ranks the kinases. get_enrichment(self, input_sites: pd.DataFrame, fc_threshold: float = 1.5) -> enrichment.Enrichment Checks regulation and plots the the enrichment vs p-value. Examples -------- PSSM table - >>> kinex.pssm + >>> kinex.pssm_ser_thr kinase -5P -5G -5A ... 4E 4s 4t 4y 0 AAK1 1.2242 0.4165 0.4825 ... 0.7777 0.4274 0.4274 0.4597 1 ACVR2A 0.7057 0.8178 0.9920 ... 1.0888 1.1958 1.1958 1.0015 @@ -53,7 +46,7 @@ class Kinex: [303 rows x 208 columns] Scoring matrix - >>> kinex.scoring_matrix + >>> kinex.scoring_matrix_ser_thr AAK1 ACVR2A ACVR2B ... YSK1 YSK4 ZAK VDDEKGDSNDDYDSA -7.652375 -0.556483 0.382342 ... -4.315913 -1.380515 -2.494291 YDSAGLLSDEDCMSV -3.490767 -0.142416 0.363835 ... -6.744147 -3.372538 -5.395429 @@ -66,66 +59,78 @@ class Kinex: """ - def __init__(self, scoring_matrix: pd.DataFrame, pssm: pd.DataFrame = get_pssm()) -> None: + def __init__(self, + scoring_matrix_ser_thr: pd.DataFrame, + scoring_matrix_tyr: pd.DataFrame, + pssm_ser_thr: pd.DataFrame = get_pssm_ser_thr(), + pssm_tyr: pd.DataFrame = get_pssm_tyr()) -> None: """ Initializes the instance of the Kinex class. Parameters ---------- - pssm : pandas.DataFrame + pssm_ser_thr : pandas.DataFrame Normalised and scaled densiometries from PSPA experiments. The table cotains on rows the kinases and on columns the positions for each aminoacid. - scoring_matrix : pandas.DataFrame + scoring_matrix_ser_thr : pandas.DataFrame Table containing 82,755 experimentally identified Ser/Thr phosphosites that have been scored by 303 Ser or Thr kinase PSSM. - The table allows the ranking of kinases, as well as the calculation of promiscuity index and median percentile for each input sequence. + The table allows the ranking of kinases, as well as the calculation of promiscuity index and median percentile for each input validation. """ - self.pssm = pssm - self.scoring_matrix = {} - for col in scoring_matrix: - self.scoring_matrix[col] = scoring_matrix[col].to_list() + self.pssm_ser_thr = pssm_ser_thr + self.pssm_tyr = pssm_tyr + + self.scoring_matrix_ser_thr = {col: scoring_matrix_ser_thr[col].to_list() for col in scoring_matrix_ser_thr} + self.scoring_matrix_ser_thr_length = len(scoring_matrix_ser_thr) + + self.scoring_matrix_tyr = {col: scoring_matrix_tyr[col].to_list() for col in scoring_matrix_tyr} + self.scoring_matrix_tyr_length = len(scoring_matrix_tyr) def __repr__(self): return "" - def get_score(self, sequence: str, phospho_priming: bool = False, favorability: bool = False, method: str = 'avg') -> Score: + def get_score(self, + sequence: str, + phospho_priming: bool = False, + favorability: bool = False, + method: str = 'avg') -> Score: """ - Checks the sequence format and it's validity. + Checks the validation format and it's validity. Computes the scores, the logarithmised scores, and ranks the kinases based on the 82,755 Ser/Thr phosphosites matrix. Parameters ---------- sequence: str - A string representing a peptide sequence of the 20 natural aminoacids, plus pT and pY at 9 positions surrounding a Ser/Thr phospho-acceptor. + A string representing a peptide validation of the 20 natural aminoacids, plus pT and pY at 9 positions surrounding a Ser/Thr phospho-acceptor. phospho_priming: bool, default False Enable/Disable the phospo-priming option. favorability: bool, default False Considers the favorability towards either Ser or Thr phospho-acceptor. method: str, default 'avg' - Accounts for multiple phosphorylation sites present in a sequence and considers either the max/min/average of the site scores. + Accounts for multiple phosphorylation sites present in a validation and considers either the max/min/average of the site scores. Returns ------- Score - Scoring result in form of an instance of the Score class containing sequence string, kinase ranking table, median percentile and methods + Scoring result in form of an instance of the Score class containing validation string, kinase ranking table, median percentile and methods like promiscuity_index(limit) (returns a promiscuity index) and top(number) (returns a top {number} in kinase ranking). Examples -------- Reading necessary tables - >>> pssm_table = pd.read_csv('./data/pssm_table.csv') - >>> scoring_matrix = pd.read_csv('./data/scoring_matrix.csv', index_col=0) + >>> pssm_table = pd.read_csv('resources/pssm_table_ser_thr.csv') + >>> scoring_matrix = pd.read_csv('kinex/resources/scoring_matrix.csv', index_col=0) Initializing Kinex class instance - >>> kinex = Kinex(pssm=pssm_table, scoring_matrix=scoring_matrix) + >>> kinex = Kinex(pssm_ser_thr=pssm_table, scoring_matrix_ser_thr=scoring_matrix) Calling get_score method and saving it in result variable - >>> result = kinex.get_score(sequence='GRNSLs*PVQA', phospho_priming=False, favorability=False) + >>> result = kinex.get_score(validation='GRNSLs*PVQA', phospho_priming=False, favorability=False) >>> result Scoring results for ['GRNSLs*PVQA'] - Getting the sequence from scoring results - >>> result.sequence + Getting the validation from scoring results + >>> result.validation ['GRNSLs*PVQA'] Kinase ranking @@ -175,112 +180,63 @@ def get_score(self, sequence: str, phospho_priming: bool = False, favorability: CDK7 9.678460 3.274777 86.048 """ - # start = time.perf_counter() + if len(sequence) < 3: + raise ValueError(f"Invalid sequence") - sequence_format = get_sequence_format(sequence) - - # Check method and format if not method in ['min', 'max', 'avg', 'all']: - raise ValueError( - f"Method {method} is not supported. Supported methods: 'min', 'max', 'avg', 'all'") - - if sequence_format == 'unsupported': - raise ValueError( - f"Sequence format is not supported. Supported formats: '*' and 'central'") - - # Empty list for possible sequences - sequences = [] - if sequence_format == '(ph)' or sequence_format == '*': - # Split the sequence to sub-sequences and count them - sequence = sequence.split(sequence_format) - num = len(sequence) - sequence_format = '*' - - # Make the last aminoacid lowercase - for id in range(num): - if not id == num - 1: - sequence[id] = sequence[id][:-1] + \ - sequence[id][-1:].lower() - - # Make possible sequences - for id in range(num - 1): - seq = '' - for i in range(num): - if i == id + 1: - seq += "*" - seq += sequence[i] - # Check the validity of subsequence. If invalid, go to next - if not check_sequence(seq, sequence_format): - continue - sequences.append(seq) - - # If all the subsequences invalid -> sequence invalid - if len(sequences) == 0: - raise ValueError("Invalid sequence") - sequence = sequences - - # Empty dataframe to store scores - df = pd.DataFrame() - df_all = [] - # Iterate through every sequence for asterisk format - if sequence_format == "*": - number_of_seq = len(sequence) - for id in range(number_of_seq): - if not phospho_priming: - sequence[id] = sequence[id].upper() - if method == 'all': - df_all.append(score( - sequence=sequence[id], sequence_format=sequence_format, pssm=self.pssm, favorability=favorability)) - else: - if id == 0: - df = score(sequence=sequence[id], sequence_format=sequence_format, pssm=self.pssm, favorability=favorability) / number_of_seq if method == 'avg' else score( - sequence=sequence[id], sequence_format=sequence_format, pssm=self.pssm, favorability=favorability) - else: - if method == 'avg': - df = df.add(score( - sequence=sequence[id], sequence_format=sequence_format, pssm=self.pssm, favorability=favorability) / number_of_seq) - else: - df = pd.concat([df, score( - sequence=sequence[id], sequence_format=sequence_format, pssm=self.pssm, favorability=favorability)]) - if method == 'min': - df = df.groupby(df.index).min() - elif method == 'max': - df = df.groupby(df.index).max() - - # Central format - elif sequence_format == 'central': - if not check_sequence(sequence, sequence_format): - raise ValueError("Invalid sequence") - if not phospho_priming: - sequence = sequence.upper() - df = score(sequence=sequence, sequence_format=sequence_format, - pssm=self.pssm, favorability=favorability) - sequence = [sequence] - - if not method == 'all': - df_all.append(df) - - for table in df_all: + raise ValueError(f"Method {method} is not supported. Supported methods: 'min', 'max', 'avg', 'all'") + + if not phospho_priming: + sequence = sequence.upper() + + sequence_object = get_sequence_object(sequence) + sequence_object.preprocess_sequence() + sequence_object.validate_sequence() + + match sequence_object.sequence_type: + case SequenceType.SER_THR: + pssm = self.pssm_ser_thr + scoring_matrix = self.scoring_matrix_ser_thr + scoring_matrix_len = self.scoring_matrix_ser_thr_length + case SequenceType.TYR: + pssm = self.pssm_tyr + scoring_matrix = self.scoring_matrix_tyr + scoring_matrix_len = self.scoring_matrix_tyr_length + case _: + raise ValueError(f"Invalid sequence type") + + scores_results = sequence_object.get_sequence_scores(pssm, favorability) + + match method: + case 'min': + scores_results = pd.concat(scores_results) + scores_results = [scores_results.groupby(scores_results.index).min()] + case 'max': + scores_results = pd.concat(scores_results) + scores_results = [scores_results.groupby(scores_results.index).max()] + case 'avg': + scores_results = [ + reduce(lambda df1, df2: df1.add(df2, fill_value=0), scores_results) / len(scores_results)] + case 'all': + pass + + for table in scores_results: table.insert(1, "log_score", np.log2(table["score"])) - # Compute percentiles percentiles = [] for kinase in table.index: - # Get the position of the kinase score within the 82755 scored reference sequences and divide it by 82755 to get the percentile score - percentile = (bisect.bisect_left( - self.scoring_matrix[kinase], table.log_score[kinase]) + 1) * 100 / 82755 + percentile = (bisect.bisect_left(scoring_matrix[kinase], table.log_score[kinase]) + 1) * ( + 100 / scoring_matrix_len) percentiles.append(percentile) table.insert(2, "percentile_score", percentiles) table.sort_values("percentile_score", ascending=False, inplace=True) - # Debugging information - # end = time.perf_counter() - # logging.debug(f'{sequence}, {end-start}') - return Score(sequence, df_all) + return Score(sequence_object, scores_results) - def get_enrichment(self, input_sites: pd.DataFrame, fc_threshold: float = 1.5, phospho_priming: bool = False, favorability: bool = False, method: str = 'avg'): + def get_enrichment(self, input_sites: pd.DataFrame, fc_threshold: float = 1.5, phospho_priming: bool = False, + favorability: bool = False, method: str = 'avg'): """ Counts the number of up/down/unregulated phosphosite sequences. Using one-sided Fisher exact test determines the kinase enrichment. @@ -302,12 +258,12 @@ def get_enrichment(self, input_sites: pd.DataFrame, fc_threshold: float = 1.5, p Examples -------- Reading necessary tables - >>> pssm_table = pd.read_csv('./data/pssm_table.csv') - >>> scoring_matrix = pd.read_csv('./data/scoring_matrix.csv', index_col=0) - >>> input_sites = pd.read_csv('data/fam20c_cut.csv', sep='\t') + >>> pssm_table = pd.read_csv('resources/pssm_table_ser_thr.csv') + >>> scoring_matrix = pd.read_csv('kinex/resources/scoring_matrix.csv', index_col=0) + >>> input_sites = pd.read_csv('kinex/resources/fam20c_cut.csv', sep='\t') Initializing Kinex class instance - >>> kinex = Kinex(pssm=pssm_table, scoring_matrix=scoring_matrix) + >>> kinex = Kinex(pssm_ser_thr=pssm_table, scoring_matrix_ser_thr=scoring_matrix) Calling get_score method and saving it inside of a variable >>> result = kinex.get_enrichment(input_sites=input_sites, fc_threshold=1.0) @@ -366,68 +322,60 @@ def get_enrichment(self, input_sites: pd.DataFrame, fc_threshold: float = 1.5, p """ if not method in ['min', 'max', 'avg']: - raise ValueError( - f"Method {method} is not supported. Supported methods: 'min', 'max', 'avg'") + raise ValueError(f"Method {method} is not supported. Supported methods: 'min', 'max', 'avg'") - # start = time.perf_counter() - df = input_sites.copy() - - df.iloc[:,0] = df.iloc[:,0].astype(str).str.replace('(ub)', '', regex=False).str.replace( + input_sites_copy = input_sites.copy() + input_sites_copy.iloc[:, 0] = input_sites_copy.iloc[:, 0].astype(str).str.replace('(ub)', '', + regex=False).str.replace( '(ox)', '', regex=False).str.replace('(ac)', '', regex=False).str.replace('(de)', '', regex=False) - # Empty DataFrame to store the output - enrichment_table = pd.DataFrame( - columns=['kinase', 'upregulated', 'downregulated', 'unregulated']) - -# logging.debug(enrichment_table) + ser_thr_enrichment = Enrichment(SequenceType.SER_THR, set(self.pssm_ser_thr.index)) + tyr_enrichment = Enrichment(SequenceType.TYR, set(self.pssm_tyr.index)) - total_upregulated = total_downregulated = total_unregulated = 0 - regulation_list = [] failed_sites = [] - top15_kinases_list = [] - for id in range(len(df)): - # Get top 15 kinases, check if site is valid - # logging.debug(f"Scoring {df.iloc[id, 0]} : {id}/{len(df) - 1}") + for id in range(len(input_sites_copy)): try: - top15_kinases = self.get_score(sequence=str( - df.iloc[id, 0]), phospho_priming=phospho_priming, favorability=favorability, method=method).top(15).index + score_result = self.get_score(sequence=str( + input_sites_copy.iloc[id, 0]), phospho_priming=phospho_priming, favorability=favorability, + method=method) except ValueError: - # logging.warning(f"Scoring of {df.iloc[id, 0]} failed") - failed_sites.append(df.iloc[id, 0]) - regulation_list.append('failed') - top15_kinases_list.append("") + failed_sites.append(input_sites_copy.iloc[id, 0]) continue + match score_result.sequence.sequence_type: + case SequenceType.SER_THR: + enrichment_object = ser_thr_enrichment + case SequenceType.TYR: + enrichment_object = tyr_enrichment + case _: + failed_sites.append(input_sites_copy.iloc[id, 0]) + continue + regulation = "" - if float(str(df.iloc[id, 1])) >= fc_threshold: + if float(str(input_sites_copy.iloc[id, 1])) >= fc_threshold: regulation = "upregulated" - total_upregulated += 1 - elif float(str(df.iloc[id, 1])) <= -fc_threshold: + enrichment_object.total_upregulated += 1 + elif float(str(input_sites_copy.iloc[id, 1])) <= -fc_threshold: regulation = "downregulated" - total_downregulated += 1 - elif float(str(df.iloc[id, 1])) < fc_threshold: + enrichment_object.total_downregulated += 1 + elif float(str(input_sites_copy.iloc[id, 1])) < fc_threshold: regulation = "unregulated" - total_unregulated += 1 - - regulation_list.append(regulation) - top15_kinases_list.append(",".join(top15_kinases)) + enrichment_object.total_unregulated += 1 - enrichment_table = pd.concat([enrichment_table, pd.DataFrame( - {"kinase": top15_kinases, regulation: np.ones(len(top15_kinases))})]).groupby('kinase').sum(numeric_only=False).reset_index() + top15_kinases = score_result.top(15)[0].index + enrichment_object.regulation_list.append(regulation) + enrichment_object.top15_kinases_list.append(",".join(top15_kinases)) - # Add regulation column to input_sites table - df.insert(2, 'regulation', regulation_list) - df.insert(3, 'top15_kinases', top15_kinases_list) + enrichment_object.enrichment_table = pd.concat([enrichment_object.enrichment_table, pd.DataFrame( + {"kinase": top15_kinases, regulation: np.ones(len(top15_kinases))})]).groupby('kinase').sum( + numeric_only=False).reset_index() - # TODO think about background adjustment # Background adjustment - if total_unregulated == 0: - total_unregulated = np.min( - [total_upregulated, total_downregulated])/2 + ser_thr_enrichment.adjust_background_sites() + tyr_enrichment.adjust_background_sites() - # end = time.perf_counter() - # logging.debug(f'{end-start}') - # logging.debug(enrichment_table) + ser_thr_enrichment.fisher_statistics() + tyr_enrichment.fisher_statistics() - return Enrichment(enrichment_table, df, failed_sites, total_upregulated, total_downregulated, total_unregulated, set(self.pssm.index)) + return EnrichmentResults(ser_thr_enrichment, tyr_enrichment, failed_sites) \ No newline at end of file diff --git a/src/kinex/resources/__init__.py b/src/kinex/resources/__init__.py new file mode 100644 index 0000000..1be06f2 --- /dev/null +++ b/src/kinex/resources/__init__.py @@ -0,0 +1,43 @@ +from importlib import resources +import json +import pandas as pd + + +def get_pssm_ser_thr() -> pd.DataFrame: + with resources.path("kinex.resources", "pssm_table_ser_thr.csv") as df: + return pd.read_csv(df, index_col=0) + + +def get_pssm_tyr() -> pd.DataFrame: + with resources.path("kinex.resources", "pssm_table_tyr.csv") as df: + return pd.read_csv(df, index_col=0) + + +def get_ser_thr_family() -> dict: + with resources.path("kinex.resources", "ser_thr_family.json") as file_path: + with open(file_path) as json_file: + return json.load(json_file) + + +def get_ser_thr_family_colors() -> dict: + with resources.path("kinex.resources", "ser_thr_family_colors.json") as file_path: + with open(file_path) as json_file: + return json.load(json_file) + + +def get_tyr_family() -> dict: + with resources.path("kinex.resources", "tyr_family.json") as file_path: + with open(file_path) as json_file: + return json.load(json_file) + + +def get_tyr_family_colors() -> dict: + with resources.path("kinex.resources", "tyr_family_colors.json") as file_path: + with open(file_path) as json_file: + return json.load(json_file) + + +def get_experiments() -> dict: + with resources.path("kinex.resources", "experiments.json") as file_path: + with open(file_path) as json_file: + return json.load(json_file) \ No newline at end of file diff --git a/src/data/experiments.json b/src/kinex/resources/experiments.json similarity index 100% rename from src/data/experiments.json rename to src/kinex/resources/experiments.json diff --git a/src/data/pssm_table.csv b/src/kinex/resources/pssm_table_ser_thr.csv similarity index 100% rename from src/data/pssm_table.csv rename to src/kinex/resources/pssm_table_ser_thr.csv diff --git a/src/kinex/resources/pssm_table_tyr.csv b/src/kinex/resources/pssm_table_tyr.csv new file mode 100644 index 0000000..d62d5d8 --- /dev/null +++ b/src/kinex/resources/pssm_table_tyr.csv @@ -0,0 +1,94 @@ 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diff --git a/src/kinex/resources/ser_thr_family.json b/src/kinex/resources/ser_thr_family.json new file mode 100644 index 0000000..ddcf568 --- /dev/null +++ b/src/kinex/resources/ser_thr_family.json @@ -0,0 +1,305 @@ +{ + "AAK1": "Other", + "ACVR2A": "TKL", + "ACVR2B": "TKL", + "AKT1": "AGC", + "AKT2": "AGC", + "AKT3": "AGC", + "ALK2": "TKL", + "ALK4": "TKL", + "ALPHAK3": "Alpha", + "AMPKA1": "CAMK", + "AMPKA2": "CAMK", + "ANKRD3": "TKL", + "ASK1": "STE", + "ATM": "PIKK", + "ATR": "PIKK", + "AURA": "Other", + "AURB": "Other", + "AURC": "Other", + "BCKDK": "PDHK", + "BIKE": "Other", + "BMPR1A": "TKL", + "BMPR1B": "TKL", + "BMPR2": "TKL", + "BRAF": "TKL", + "BRSK1": "CAMK", + "BRSK2": "CAMK", + "BUB1": "Other", + "CAMK1A": "CAMK", + "CAMK1B": "CAMK", + "CAMK1D": "CAMK", + "CAMK1G": "CAMK", + "CAMK2A": "CAMK", + "CAMK2B": "CAMK", + "CAMK2D": "CAMK", + "CAMK2G": "CAMK", + "CAMK4": "CAMK", + "CAMKK1": "Other", + "CAMKK2": "Other", + "CAMLCK": "CAMK", + "CDC7": "Other", + "CDK1": "CMGC", + "CDK10": "CMGC", + "CDK12": "CMGC", + "CDK13": "CMGC", + "CDK14": "CMGC", + "CDK16": "CMGC", + "CDK17": "CMGC", + "CDK18": "CMGC", + "CDK19": "CMGC", + "CDK2": "CMGC", + "CDK3": "CMGC", + "CDK4": "CMGC", + "CDK5": "CMGC", + "CDK6": "CMGC", + "CDK7": "CMGC", + "CDK8": "CMGC", + "CDK9": "CMGC", + "CDKL1": "CMGC", + "CDKL5": "CMGC", + "CHAK1": "Alpha", + "CHAK2": "Alpha", + "CHK1": "CAMK", + "CHK2": "CAMK", + "CK1A": "CK1", + "CK1A2": "CK1", + "CK1D": "CK1", + "CK1E": "CK1", + "CK1G1": "CK1", + "CK1G2": "CK1", + "CK1G3": "CK1", + "CK2A1": "Other", + "CK2A2": "Other", + "CLK1": "CMGC", + "CLK2": "CMGC", + "CLK3": "CMGC", + "CLK4": "CMGC", + "COT": "STE", + "CRIK": "AGC", + "DAPK1": "CAMK", + "DAPK2": "CAMK", + "DAPK3": "CAMK", + "DCAMKL1": "CAMK", + "DCAMKL2": "CAMK", + "DLK": "TKL", + "DMPK1": "AGC", + "DNAPK": "PIKK", + "DRAK1": "CAMK", + "DSTYK": "TKL", + "DYRK1A": "CMGC", + "DYRK1B": "CMGC", + "DYRK2": "CMGC", + "DYRK3": "CMGC", + "DYRK4": "CMGC", + "EEF2K": "Alpha", + "ERK1": "CMGC", + "ERK2": "CMGC", + "ERK5": "CMGC", + "ERK7": "CMGC", + "FAM20C": "FAM20C", + "GAK": "Other", + "GCK": "STE", + "GCN2": "Other", + "GRK1": "AGC", + "GRK2": "AGC", + "GRK3": "AGC", + "GRK4": "AGC", + "GRK5": "AGC", + "GRK6": "AGC", + "GRK7": "AGC", + "GSK3A": "CMGC", + "GSK3B": "CMGC", + "HASPIN": "Other", + "HGK": "STE", + "HIPK1": "CMGC", + "HIPK2": "CMGC", + "HIPK3": "CMGC", + "HIPK4": "CMGC", + "HPK1": "STE", + "HRI": "Other", + "HUNK": "CMGC", + "ICK": "CMGC", + "IKKA": "Other", + "IKKB": "Other", + "IKKE": "Other", + "IRAK1": "TKL", + "IRAK4": "TKL", + "IRE1": "Other", + "IRE2": "Other", + "JNK1": "CMGC", + "JNK2": "CMGC", + "JNK3": "CMGC", + "KHS1": "STE", + "KHS2": "STE", + "KIS": "Other", + "LATS1": "AGC", + "LATS2": "AGC", + "LKB1": "CAMK", + "LOK": "STE", + "LRRK2": "TKL", + "MAK": "CMGC", + "MAP3K15": "STE", + "MAPKAPK2": "CAMK", + "MAPKAPK3": "CAMK", + "MAPKAPK5": "CAMK", + "MARK1": "CAMK", + "MARK2": "CAMK", + "MARK3": "CAMK", + "MARK4": "CAMK", + "MASTL": "AGC", + "MEK1": "STE", + "MEK2": "STE", + "MEK5": "STE", + "MEKK1": "STE", + "MEKK2": "STE", + "MEKK3": "STE", + "MEKK6": "STE", + "MELK": "CAMK", + "MINK": "STE", + "MLK1": "TKL", + "MLK2": "TKL", + "MLK3": "TKL", + "MLK4": "STE", + "MNK1": "CAMK", + "MNK2": "CAMK", + "MOK": "CMGC", + "MOS": "Other", + "MPSK1": "Other", + "MRCKA": "AGC", + "MRCKB": "AGC", + "MSK1": "AGC", + "MSK2": "AGC", + "MST1": "STE", + "MST2": "STE", + "MST3": "STE", + "MST4": "STE", + "MTOR": "PIKK", + "MYLK4": "CAMK", + "MYO3A": "STE", + "MYO3B": "STE", + "NDR1": "AGC", + "NDR2": "AGC", + "NEK1": "Other", + "NEK11": "Other", + "NEK2": "Other", + "NEK3": "Other", + "NEK4": "Other", + "NEK5": "Other", + "NEK6": "Other", + "NEK7": "Other", + "NEK8": "Other", + "NEK9": "Other", + "NIK": "STE", + "NIM1": "CAMK", + "NLK": "CMGC", + "NUAK1": "CAMK", + "NUAK2": "CAMK", + "OSR1": "STE", + "P38A": "CMGC", + "P38B": "CMGC", + "P38D": "CMGC", + "P38G": "CMGC", + "P70S6K": "AGC", + "P70S6KB": "AGC", + "P90RSK": "AGC", + "PAK1": "STE", + "PAK2": "STE", + "PAK3": "STE", + "PAK4": "STE", + "PAK5": "STE", + "PAK6": "STE", + "PASK": "CAMK", + "PBK": "Other", + "PDHK1": "PDHK", + "PDHK4": "PDHK", + "PDK1": "AGC", + "PERK": "Other", + "PHKG1": "CAMK", + "PHKG2": "CAMK", + "PIM1": "CAMK", + "PIM2": "CAMK", + "PIM3": "CAMK", + "PINK1": "Other", + "PKACA": "AGC", + "PKACB": "AGC", + "PKACG": "AGC", + "PKCA": "AGC", + "PKCB": "AGC", + "PKCD": "AGC", + "PKCE": "AGC", + "PKCG": "AGC", + "PKCH": "AGC", + "PKCI": "AGC", + "PKCT": "AGC", + "PKCZ": "AGC", + "PKG1": "AGC", + "PKG2": "AGC", + "PKN1": "AGC", + "PKN2": "AGC", + "PKN3": "AGC", + "PKR": "Other", + "PLK1": "Other", + "PLK2": "Other", + "PLK3": "Other", + "PLK4": "Other", + "PRKD1": "CAMK", + "PRKD2": "CAMK", + "PRKD3": "CAMK", + "PRKX": "AGC", + "PRP4": "CMGC", + "PRPK": "Other", + "QIK": "CAMK", + "QSK": "CAMK", + "RAF1": "TKL", + "RIPK1": "TKL", + "RIPK2": "TKL", + "RIPK3": "TKL", + "ROCK1": "AGC", + "ROCK2": "AGC", + "RSK2": "AGC", + "RSK3": "AGC", + "RSK4": "AGC", + "SBK": "Other", + "SGK1": "AGC", + "SGK3": "AGC", + "SIK": "CAMK", + "SKMLCK": "CAMK", + "SLK": "STE", + "SMG1": "PIKK", + "SMMLCK": "CAMK", + "SNRK": "CAMK", + "SRPK1": "CMGC", + "SRPK2": "CMGC", + "SRPK3": "CMGC", + "SSTK": "CAMK", + "STK33": "CAMK", + "STLK3": "STE", + "TAK1": "TKL", + "TAO1": "STE", + "TAO2": "STE", + "TAO3": "STE", + "TBK1": "Other", + "TGFBR1": "TKL", + "TGFBR2": "TKL", + "TLK1": "Other", + "TLK2": "Other", + "TNIK": "STE", + "TSSK1": "CAMK", + "TSSK2": "CAMK", + "TTBK1": "CK1", + "TTBK2": "CK1", + "TTK": "Other", + "ULK1": "Other", + "ULK2": "Other", + "VRK1": "CK1", + "VRK2": "CK1", + "WNK1": "Other", + "WNK3": "Other", + "WNK4": "Other", + "YANK2": "AGC", + "YANK3": "AGC", + "YSK1": "STE", + "YSK4": "STE", + "ZAK": "STE" +} \ No newline at end of file diff --git a/src/kinex/resources/ser_thr_family_colors.json b/src/kinex/resources/ser_thr_family_colors.json new file mode 100644 index 0000000..c7a4349 --- /dev/null +++ b/src/kinex/resources/ser_thr_family_colors.json @@ -0,0 +1,13 @@ +{ + "TKL": "#0400A9", + "STE": "#754391", + "CK1": "#499FD2", + "AGC": "#992B2C", + "CAMK": "#DE643A", + "Other": "#957C64", + "CMGC": "#2C643C", + "Alpha": "#C88AB5", + "PDHK": "#316D79", + "PIKK": "#020101", + "FAM20C": "#C22F7F" +} \ No newline at end of file diff --git a/src/kinex/resources/tyr_family.json b/src/kinex/resources/tyr_family.json new file mode 100644 index 0000000..1b6200c --- /dev/null +++ b/src/kinex/resources/tyr_family.json @@ -0,0 +1,95 @@ +{ + "ABL": "ABL", + "ARG": "ABL", + "ALK": "LTKR", + "AXL": "TAMR", + "BLK": "SRC", + "BMPR2": "TKL", + "ETK": "TEC", + "BTK": "TEC", + "CSFR": "PDGFR", + "CSK": "CSK", + "DDR1": "DDR", + "DDR2": "DDR", + "EGFR": "ErbB", + "EPHA1": "EPHR", + "EPHA2": "EPHR", + "EPHA3": "EPHR", + "EPHA4": "EPHR", + "EPHA5": "EPHR", + "EPHA6": "EPHR", + "EPHA7": "EPHR", + "EPHA8": "EPHR", + "EPHB1": "EPHR", + "EPHB2": "EPHR", + "EPHB3": "EPHR", + "EPHB4": "EPHR", + "HER2": "ErbB", + "HER4": "ErbB", + "FER": "FES", + "FES": "FES", + "FGFR1": "FGFR", + "FGFR2": "FGFR", + "FGFR3": "FGFR", + "FGFR4": "FGFR", + "FGR": "SRC", + "VEGFR1": "VEGFR", + "FLT3": "PDGFR", + "VEGFR3": "VEGFR", + "FRK": "FRK", + "FYN": "SRC", + "HCK": "SRC", + "IGF1R": "INSR", + "INSR": "INSR", + "IRR": "INSR", + "ITK": "TEC", + "JAK1": "JAK", + "JAK2": "JAK", + "JAK3": "JAK", + "VEGFR2": "VEGFR", + "KIT": "PDGFR", + "LCK": "SRC", + "LIMK1": "TKL", + "LIMK2": "TKL", + "LTK": "LTKR", + "LYN": "SRC", + "CTK": "CSK", + "MER": "TAMR", + "MET": "HGFR", + "MKK4": "STE", + "MKK6": "STE", + "MKK7": "STE", + "MST1R": "HGFR", + "MUSK": "MUSK", + "NEK10": "OTHER", + "TRKA": "NGFR", + "TRKB": "NGFR", + "TRKC": "NGFR", + "PDGFRA": "PDGFR", + "PDGFRB": "PDGFR", + "PDHK1": "PDHK", + "PDHK3": "PDHK", + "PDHK4": "PDHK", + "PINK1": "OTHER", + "MYT1": "WEE", + "FAK": "FAK", + "PYK2": "FAK", + "BRK": "FRK", + "RET": "RETR", + "ROS": "ROSR", + "SRC": "SRC", + "SRMS": "FRK", + "SYK": "SYK", + "TEC": "TEC", + "TIE2": "TIER", + "TESK1": "TKL", + "TNK1": "ACK", + "ACK": "ACK", + "TNNI3K": "TKL", + "TXK": "TEC", + "TYK2": "JAK", + "TYRO3": "TAMR", + "WEE1": "WEE", + "YES": "SRC", + "ZAP70": "SYK" +} \ No newline at end of file diff --git a/src/kinex/resources/tyr_family_colors.json b/src/kinex/resources/tyr_family_colors.json new file mode 100644 index 0000000..4dcf9bd --- /dev/null +++ b/src/kinex/resources/tyr_family_colors.json @@ -0,0 +1,32 @@ +{ + "ErbB": "#ff6a6a", + "INSR": "#48aaa7", + "VEGFR": "#ffd82a", + "PDGFR": "#ffaa4f", + "FGFR": "#ecec47", + "NGFR": "#5cdf79", + "MUSK": "#54be8d", + "HGFR": "#aae179", + "TAMR": "#c3e0a3", + "TIER": "#d78937", + "EPHR": "#939aff", + "RETR": "#cbcb00", + "DDR": "#46aa75", + "SEV": "#28929c", + "LTKR": "#2fadb8", + "ABL": "#e2c0e3", + "FES": "#c9aac9", + "CSK": "#ffd7fe", + "FAK": "#8dc7f7", + "JAK": "#c77373", + "SRC": "#c891ff", + "SYK": "#bbd4ea", + "TEC": "#ff95ff", + "FRK": "#e276ff", + "ACK": "#ff8f5b", + "TKL": "#d9d9d9", + "OTHER": "#bfbfbf", + "STE": "#a6a6a6", + "WEE": "#808080", + "PDHK": "#000000" +} diff --git a/src/kinex/score.py b/src/kinex/score.py new file mode 100644 index 0000000..fb12bac --- /dev/null +++ b/src/kinex/score.py @@ -0,0 +1,49 @@ +from kinex.sequence import Sequence + + +class Score: + """ + Score of the site validation based on pssm + + Attributes + ---------- + sequence : str + A string representing a validation of aminoacids + ranking : pandas.DataFrame + containing scores, log2(scores) and percentiles for each kinase + median_percentile : int + Median value of all percentile scores for a validation + + Methods + ------- + promiscuity_index(self, limit: int = 90) -> int + The number of kinases scoring above the {limit}th percentile + + top(self, number: int = 15) -> + Top {number} ranked kinases according to percentile score. + + """ + + def __init__(self, sequence: Sequence, ranking: list) -> None: + self.sequence = sequence + self.ranking = ranking + + median_percentile = [] + for score in ranking: + median_percentile.append(score["percentile_score"].median()) + self.median_percentile = median_percentile + + def __repr__(self): + return f"Scoring results for {self.sequence.sequence_string}" + + def promiscuity_index(self, limit: int = 90) -> list: + promiscuity_index = [] + for score in self.ranking: + promiscuity_index.append(score["percentile_score"][score["percentile_score"] > limit].count()) + return promiscuity_index + + def top(self, number: int = 15) -> list: + top = [] + for score in self.ranking: + top.append(score.head(number)) + return top \ No newline at end of file diff --git a/src/sequence.py b/src/kinex/sequence.py similarity index 100% rename from src/sequence.py rename to src/kinex/sequence.py diff --git a/src/table2x2.py b/src/kinex/table2x2.py similarity index 100% rename from src/table2x2.py rename to src/kinex/table2x2.py diff --git a/src/score.py b/src/score.py deleted file mode 100644 index d3f22ee..0000000 --- a/src/score.py +++ /dev/null @@ -1,52 +0,0 @@ -import pandas as pd - -class Score: - """ - Score of the site sequence based on pssm - - Attributes - ---------- - sequence : str - A string representing a sequence of aminoacids - ranking : pandas.DataFrame - containing scores, log2(scores) and percentiles for each kinase - median_percentile : int - Median value of all percentile scores for a sequence - - Methods - ------- - promiscuity_index(self, limit: int = 90) -> int - The number of kinases scoring above the {limit}th percentile - - top(self, number: int = 15) -> - Top {number} ranked kinases according to percentile score. - - """ - - def __init__(self,sequence: list, ranking: list) -> None: - self.sequence = sequence - self.n_sequences = len(ranking) - median_percentile = [] - if self.n_sequences == 1: - self.ranking = ranking[0] - self.median_percentile = ranking[0]["percentile_score"].median() - else: - self.ranking = ranking - for score in ranking: - median_percentile.append(score["percentile_score"].median()) - self.median_percentile = median_percentile - - def __repr__ (self): - return f"Scoring results for {self.sequence}" - - def promiscuity_index(self, limit: int = 90, subsequence: int = 0) -> int: - if self.n_sequences == 1: - return self.ranking["percentile_score"][self.ranking["percentile_score"] > limit].count() - else: - return self.ranking[subsequence]["percentile_score"][self.ranking[subsequence]["percentile_score"] > limit].count() - - def top(self, number: int = 15, subsequence: int = 0): - if self.n_sequences == 1: - return self.ranking.head(number) - else: - return self.ranking[subsequence].head(number) \ No newline at end of file From 1efba412f35bcfbed9b1f8746bba1480478522e4 Mon Sep 17 00:00:00 2001 From: Alexandra Valeanu Date: Tue, 22 Oct 2024 23:01:18 +0300 Subject: [PATCH 4/4] Update documentation --- README.md | 65 ++++++++++++---------- docs/chapters/features/comparison.rst | 34 +++++------- docs/chapters/features/enrichment.rst | 77 ++++++++++----------------- docs/chapters/features/scoring.rst | 30 +++++------ docs/chapters/features/usage.rst | 46 ++++++++++++++++ docs/chapters/usage.rst | 56 ------------------- docs/conf.py | 5 +- docs/index.rst | 10 ++-- docs/requirements.txt | 2 + 9 files changed, 150 insertions(+), 175 deletions(-) create mode 100644 docs/chapters/features/usage.rst delete mode 100644 docs/chapters/usage.rst diff --git a/README.md b/README.md index fde1cbb..4008358 100644 --- a/README.md +++ b/README.md @@ -1,15 +1,8 @@ -# Kinex +# Kinex - Kinome Exploration Tool -Alexandra Valeanu and Jitao David Zhang with the input and help of many colleagues +**Kinex** is a Python package for infering causal kinases from phosphoproteomics data. -**kinex** is a workflow implemented in a Python package with the same name. Kinex infers causal kinases from phosphoproteomics data. - -## Table of Contents - -- [Main Features](#main-features) -- [Requirements](#requirements) -- [Installation](#installation) -- [Documentation](#documentation) +Paper: Kinex infers causal kinases from phosphoproteomics data. https://doi.org/10.1101/2023.11.23.568445 ## Main Features @@ -24,47 +17,61 @@ Alexandra Valeanu and Jitao David Zhang with the input and help of many colleagu ## Installation -Here are few ways to install **kinex** - ### From Conda -1. Create and activate your conda environment - ``` +# Create and activate your conda environment conda create --name kinex conda activate kinex -``` - -2. Install kinex package -``` +# Install kinex package conda install -c bioconda kinex ``` - ### From source -1. Create and activate a python 3.11 conda environment - ``` +# Create and activate a python 3.11 conda environment conda create --name kinex conda activate kinex conda install python=3.11 -``` - -2. Download the package: -``` +# Download the package: git clone git@github.com:bedapub/kinex.git cd kinex + +# Install the package +pip install . ``` -3. Install the package +## Quick start +1. Import package and create Kinex object ``` -pip install . +from kinex import Kinex +import pandas as pd + +# Read scoring matrices from zenodo +scoring_matrix_ser_thr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_ser_thr_82k_sorted.csv.gz?download=1", compression="gzip") +scoring_matrix_tyr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_tyr_7k_sorted.csv.gz?download=1", compression="gzip") + +# Create Kinex object +kinex = Kinex(scoring_matrix_ser_thr, scoring_matrix_tyr) +``` +2. Score a sequence +``` +sequence = "FVKQKAY*QSPQKQ" +res = kinex.get_score(sequence) +``` + +3. Enrichment analysis +``` +enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max") + +enrich.ser_thr.plot() +enrich.tyr.plot() ``` -## [Documentation](https://kinex.readthedocs.io/en/latest/) +## Documentation -You can find detailed [documentation](https://kinex.readthedocs.io/en/latest/) describing every feature of the package with examples and tutorials [here](https://kinex.readthedocs.io/en/latest/). +You can find detailed documentation describing every feature of the package with examples and tutorials [here](https://kinex.readthedocs.io/en/latest/). diff --git a/docs/chapters/features/comparison.rst b/docs/chapters/features/comparison.rst index 9fa64e7..c571c84 100644 --- a/docs/chapters/features/comparison.rst +++ b/docs/chapters/features/comparison.rst @@ -5,13 +5,13 @@ Drug comparison .. code:: python - >>> from kinex import Comparison + from kinex import Comparison 2. Initialize a Comparison object .. code:: python - >>> comp = Comparison() + comp = Comparison() Compare multiple experiments with each other @@ -21,7 +21,7 @@ Compare multiple experiments with each other .. code:: python - >>> data_path = "path/to/your/tables" + data_path = "path/to/your/tables" .. note:: @@ -40,7 +40,7 @@ Compare multiple experiments with each other .. code:: python - >>> fig = comp.get_comparison(data_path=data_path, method='mds') + fig = comp.get_comparison(data_path=data_path, method='mds') .. note:: @@ -50,7 +50,7 @@ Compare multiple experiments with each other .. code:: python - >>> fig.show() + fig.show() .. raw:: html :file: ../../figures/comparison_multiple_drugs.html @@ -72,7 +72,7 @@ Compare an experiment to the existing collection of drug profiles .. code:: python - >>> input_data = pd.read_csv('tables/table1.csv', index_col=0) + input_data = pd.read_csv('tables/table1.csv', index_col=0) .. note:: @@ -83,13 +83,7 @@ Compare an experiment to the existing collection of drug profiles dominant_enrichment_value_log2 dominant_p_value_log10_abs 0.868162 0.821932 -0.785398 0.707911 - -0.934463 0.901927 - -1.369094 0.000000 - -1.474303 0.000000 - ... ... - -2.914661 2.022525 - -2.490535 1.691968 - -2.920072 0.000000 + ... ... -1.551978 0.795959 -2.986266 1.521982 @@ -99,7 +93,7 @@ Compare an experiment to the existing collection of drug profiles .. code:: python - >>> fig = comp.get_comparison(input_data=input_data, method='tsne') + fig = comp.get_comparison(input_data=input_data, method='tsne') .. note:: @@ -118,7 +112,7 @@ Compare an experiment to the existing collection of drug profiles .. code:: python - >>> fig.show() + fig.show() .. raw:: html :file: ../../figures/comparison_input.html @@ -131,28 +125,28 @@ Save the plot in a desired format .. code:: python - >>> fig.write_html("path/to/file.html") + fig.write_html("path/to/file.html") - ``.svg`` .. code:: python - >>> fig.write_image("images/fig1.svg") + fig.write_image("images/fig1.svg") - ``.pdf`` .. code:: python - >>> fig.write_image("images/fig1.pdf") + fig.write_image("images/fig1.pdf") - ``.png`` .. code:: python - >>> fig.write_image("images/fig1.png") + fig.write_image("images/fig1.png") - ``.jpeg`` .. code:: python - >>> fig.write_image("images/fig1.jpeg") + fig.write_image("images/fig1.jpeg") diff --git a/docs/chapters/features/enrichment.rst b/docs/chapters/features/enrichment.rst index a5b1194..9e481d9 100644 --- a/docs/chapters/features/enrichment.rst +++ b/docs/chapters/features/enrichment.rst @@ -9,8 +9,11 @@ Kinases inference analysis .. code:: python - >>> input_sites = pd.read_csv('path/to/your/input_sites.csv') - >>> input_sites + input_sites = pd.read_csv('path/to/your/input_sites.csv') + input_sites + +.. code-block:: text + Sequence Fold Change: a/a' KO Clone A vs WT 0 KLEEKQKs*DAEEDGV -88.159789 1 EEDGVTGs*QDEEDSK -88.159789 @@ -34,55 +37,35 @@ Kinases inference analysis .. code:: python - >>> enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max") - >>> enrich - Total number of upregulated phospho-sequences is: 63 - Total number of downregulated phospho-sequences is: 86 - Total number of unregulated phospho-sequences is: 309 - enrichment.Enrichment + enrich = kinex.get_enrichment(input_sites, fc_threshold=1.5, phospho_priming=False, favorability=True, method="max") 3. Access the total number of up-regulated, down-regulated, and un-regulated phospho-sequences .. code:: python - >>> enrich.total_upregulated - 63 - int - >>> enrich.total_downregulated - 86 - int - >>> enrich.total_unregulated - 309 - int + print("Total upregulated Ser/Thr kinases:", enrich.ser_thr.total_upregulated) + print("Total downregulated Ser/Thr kinases:", enrich.ser_thr.total_downregulated) + print("Total unregulated Ser/Thr kinases:", enrich.ser_thr.total_unregulated) -4. Check the sites that were marked as failed +.. code-block:: text -.. code:: python + Total upregulated Ser/Thr kinases: 63 + Total downregulated Ser/Thr kinases: 86 + Total unregulated Ser/Thr kinases: 309 - >>> enrich.failed_sites - ['EKIGEGTyGVVYKGR', 'KPSIVTKyVESDDEK', 'LGQRIYQyIQSRFYR', 'INPGYDDyADSDEDQ', 'ADNDITPyLVSRFYR', 'RGEPNVSyICSRYYR'] - list - -5. Check the regulation of each phospho-sequence, and get the top 15 kinases most likely to target each phospho-sequence +4. Check the sites that were marked as failed .. code:: python - >>> enrich.input_sites - Sequence Fold Change: a/a' KO Clone A vs WT regulation top15_kinases - 0 KLEEKQKs*DAEEDGV -88.159789 downregulated GRK7,IKKA,CAMK2B,CK2A1,CK2A2,GRK6,LATS2,GRK1,C... - 1 EEDGVTGs*QDEEDSK -88.159789 downregulated DNAPK,CAMK2G,ATM,ATR,GRK5,GRK1,SMG1,CAMK2B,GRK... - .. ... ... ... - 462 AKEESEEs*DEDMGFG 19.421218 upregulated BMPR1A,TGFBR1,BMPR1B,ALK2,CK1G2,CK2A2,ACVR2A,G... - 463 RNGPRDAs*PPGSEPE 63.187703 upregulated SRPK2,SRPK1,SRPK3,HIPK4,CLK2,CLK3,HIPK2,KIS,GR... + enrich.failed_sites - [464 rows x 4 columns] - pandas.DataFrame - -6. Show enrichment table +5. Show enrichment table .. code:: python - >>> enrich.enrichment_table + enrich.ser_thr.enrichment_table + +.. code-block:: text upregulated downregulated ... dominant_enrichment_value_log2 dominant_p_value_log10_abs kinase @@ -95,7 +78,7 @@ Kinases inference analysis [303 rows x 19 columns] pandas.DataFrame -7. Vulcano plot of Enrichment Odds Ratio (EOR) and p-value +6. Vulcano plot of Enrichment Odds Ratio (EOR) and p-value .. note:: @@ -103,8 +86,8 @@ Kinases inference analysis .. code:: python - >>> fig = enrich.plot(use_adjusted_pval=False) - >>> fig.show() + fig = enrich.ser_thr.plot(use_adjusted_pval=False) + fig.show() .. raw:: html @@ -117,38 +100,34 @@ Kinases inference analysis `https://plotly.com/python/creating-and-updating-figures `_ - - - -8. Save the figure in a desired format - +7. Save the figure in a desired format - ``.html`` .. code:: python - >>> fig.write_html("path/to/file.html") + fig.write_html("path/to/file.html") - ``.svg`` .. code:: python - >>> fig.write_image("images/fig1.svg") + fig.write_image("images/fig1.svg") - ``.pdf`` .. code:: python - >>> fig.write_image("images/fig1.pdf") + fig.write_image("images/fig1.pdf") - ``.png`` .. code:: python - >>> fig.write_image("images/fig1.png", scale=10) + fig.write_image("images/fig1.png", scale=10) - ``.jpeg`` .. code:: python - >>> fig.write_image("images/fig1.jpeg", scale=10) + fig.write_image("images/fig1.jpeg", scale=10) diff --git a/docs/chapters/features/scoring.rst b/docs/chapters/features/scoring.rst index 8572c8e..c501f6d 100644 --- a/docs/chapters/features/scoring.rst +++ b/docs/chapters/features/scoring.rst @@ -5,33 +5,30 @@ Get scores for a phospho-sequence .. code:: python - >>> sequence = "FVKQKASQSPQKQ" - >>> res = kinex.get_score(sequence) - score.Score + sequence = "FVKQKASQSPQKQ" + res = kinex.get_score(sequence) .. note:: The supported format for a phospho-sequence\ - ``asterisk``: F_KQKAS*QSPQK - ``(ph)``: FKQKAS(ph)QSPQK + - ``central``: FVKQKASQSPQKQ 2. Rank the kinases for a given phospho-sequence based on their percentile score .. code:: python - >>> res.ranking + res.ranking + +.. code-block:: text score log_score percentile_score kinase PRKD2 30.071143 4.910308 98.923 ATR 6.798658 2.765250 98.531 MNK1 9.017093 3.172662 97.935 - BUB1 9.066003 3.180467 97.679 - MNK2 8.275760 3.048892 97.455 ... ... ... ... - GRK3 0.140495 -2.831414 11.024 - MTOR 0.203042 -2.300148 9.881 - CK1D 0.593436 -0.752837 6.265 JNK3 0.062386 -4.002623 2.043 JNK1 0.021452 -5.542765 0.453 @@ -42,23 +39,24 @@ Get scores for a phospho-sequence .. code:: python - >>> n = 5 - >>> res.top(n) + res.top(3) + +.. code-block:: text score log_score percentile_score kinase PRKD2 30.071143 4.910308 98.923 ATR 6.798658 2.765250 98.531 MNK1 9.017093 3.172662 97.935 - BUB1 9.066003 3.180467 97.679 - MNK2 8.275760 3.048892 97.455 pandas.DataFrame 4. Get median percentile (median score of kinases for a given phospho-sequence) .. code:: python - >>> res.median_percentile + res.median_percentile + +.. code-block:: text 63.493 numpy.float64 @@ -66,6 +64,8 @@ Get scores for a phospho-sequence .. code:: python - >>> res.promiscuity_index() + res.promiscuity_index() + +.. code-block:: text 30 numpy.int64 \ No newline at end of file diff --git a/docs/chapters/features/usage.rst b/docs/chapters/features/usage.rst new file mode 100644 index 0000000..0d90257 --- /dev/null +++ b/docs/chapters/features/usage.rst @@ -0,0 +1,46 @@ +Import package and initialise Kinex +=================================== + +1. Import kinex + +.. code:: python + + from kinex import Kinex + +2. Read the scoring matrix + +.. code:: python + + scoring_matrix_ser_thr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_ser_thr_82k_sorted.csv.gz?download=1", compression="gzip") + scoring_matrix_tyr = pd.read_csv("https://zenodo.org/records/13964893/files/scoring_matrix_tyr_7k_sorted.csv.gz?download=1", compression="gzip") + scoring_matrix_ser_thr + +.. code:: python + + AAK1 ACVR2A ... YSK4 ZAK + 0 -11.147481 -6.325340 ... -6.723077 -7.402360 + 1 -10.421859 -6.178601 ... -6.343452 -7.373478 + ... ... ... ... + 82753 8.074270 7.289390 ... 4.525527 4.837377 + 82754 8.623180 7.871226 ... 4.869195 5.062391 + + [82755 rows x 303 columns] + +.. note:: + + You can optionally save the scoring matrix locally for faster use in the future. + + .. code:: python + + scoring_matrix_ser_thr.to_csv("scoring_matrix_ser_thr.csv") + scoring_matrix_tyr.to_csv("scoring_matrix_tyr.csv") + + Or just download using the links: + `https://zenodo.org/records/13964893/files/scoring_matrix_ser_thr_82k_sorted.csv.gz?download=1 `_ + `https://zenodo.org/records/13964893/files/scoring_matrix_tyr_7k_sorted.csv.gz?download=1 `_ + +3. Create a kinex object + +.. code:: python + + kinex = Kinex(scoring_matrix_ser_thr, scoring_matrix_tyr) diff --git a/docs/chapters/usage.rst b/docs/chapters/usage.rst deleted file mode 100644 index 566f0a6..0000000 --- a/docs/chapters/usage.rst +++ /dev/null @@ -1,56 +0,0 @@ -Using kinex -=========== - -Import package and initialise data object ------------------------------------------ - -1. Import kinex - -.. code:: python - - >>> from kinex import Kinex - -2. Read the scoring matrix - -.. code:: python - - >>> scoring_matrix = pd.read_csv("https://zenodo.org/records/10201142/files/kinex_scoring_matrix_82k_sorted.csv.gzip?download=1", compression="gzip") - >>> scoring_matrix - - AAK1 ACVR2A ... YSK4 ZAK - 0 -11.147481 -6.325340 ... -6.723077 -7.402360 - 1 -10.421859 -6.178601 ... -6.343452 -7.373478 - ... ... ... ... - 82753 8.074270 7.289390 ... 4.525527 4.837377 - 82754 8.623180 7.871226 ... 4.869195 5.062391 - - [82755 rows x 303 columns] - -.. note:: - - You can optionally save the scoring matrix locally for future uses. - - .. code:: python - - >>> scoring_matrix.to_csv("path/to/scoring_matrix.csv") - - Or just download using the link: `https://zenodo.org/records/10201142/files/kinex_scoring_matrix_82k_sorted.csv.gzip?download=1 `_ - -3. Create a kinex object - -.. code:: python - - >>> kinex = Kinex(scoring_matrix) - >>> type(kinex) - kinex.Kinex - -Use one of three main features of kinex ------------------------------------------ - -.. toctree:: - :maxdepth: 3 - - features/scoring - features/enrichment - features/comparison - diff --git a/docs/conf.py b/docs/conf.py index ef03f70..7c143b4 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -9,12 +9,13 @@ project = 'Kinex' copyright = '2023, Alexandra Valeanu and Jitao David Zhang' author = 'Alexandra Valeanu and Jitao David Zhang' -release = '1.0.2' +release = '1.1.0' # -- General configuration --------------------------------------------------- # https://www.sphinx-doc.org/en/master/usage/configuration.html#general-configuration -extensions = ['sphinx.ext.autosectionlabel'] +extensions = ['sphinx.ext.autosectionlabel', + 'sphinx_copybutton'] templates_path = ['_templates'] exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store'] diff --git a/docs/index.rst b/docs/index.rst index b1565a4..1f4d019 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -12,10 +12,12 @@ If you use Kinex, please cite Valeanu et. al, 2023, bioRxiv, doi: `https://doi.o This documentation includes the information about installation and usage of the package. - .. toctree:: - :maxdepth: 3 - :caption: Contents: + :maxdepth: 2 + :caption: User Guide chapters/installation - chapters/usage + chapters/features/usage + chapters/features/scoring + chapters/features/enrichment + chapters/features/comparison \ No newline at end of file diff --git a/docs/requirements.txt b/docs/requirements.txt index 129296c..d1a29fe 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -62,5 +62,7 @@ sphinxcontrib-qthelp==1.0.6 # via sphinx sphinxcontrib-serializinghtml==1.1.9 # via sphinx +sphinx-copybutton==0.5.2 + # via sphinx urllib3==2.1.0 # via requests