depmap

Name: depmap
Author: K-Dense-AI

31kstars3.1kforks

How to install depmap?

k-dense-ai/scientific-agent-skills/depmap

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill depmap

Installs into the current project.

Summary

Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.

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SKILL.md

1	# DepMap — Cancer Dependency Map
2
3	## Overview
4
5	The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:
6	- Identifying which genes are essential for specific cancer types
7	- Finding cancer-selective dependencies (therapeutic targets)
8	- Validating oncology drug targets
9	- Discovering synthetic lethal interactions
10
11	Key resources:
12	- DepMap Portal: https://depmap.org/portal/
13	- DepMap data downloads: https://depmap.org/portal/download/all/
14	- Python package: `depmap` (or access via API/downloads)
15	- API: https://depmap.org/portal/api/
16
17	## When to Use This Skill
18
19	Use DepMap when:
20
21	- Target validation: Is a gene essential for survival in cancer cell lines with a specific mutation (e.g., KRAS-mutant)?
22	- Biomarker discovery: What genomic features predict sensitivity to knockout of a gene?
23	- Synthetic lethality: Find genes that are selectively essential when another gene is mutated/deleted
24	- Drug sensitivity: What cell line features predict response to a compound?
25	- Pan-cancer essentiality: Is a gene broadly essential across all cancer types (bad target) or selectively essential?
26	- Correlation analysis: Which pairs of genes have correlated dependency profiles (co-essentiality)?
27
28	## Core Concepts
29
30	### Dependency Scores
31
32	\| Score \| Range \| Meaning \|
33	\|-------\|-------\|---------\|
34	\| Chronos (CRISPR) \| ~ -3 to 0+ \| More negative = more essential. Common essential threshold: −1. Pan-essential genes ~−1 to −2 \|
35	\| RNAi DEMETER2 \| ~ -3 to 0+ \| Similar scale to Chronos \|
36	\| Gene Effect \| normalized \| Normalized Chronos; −1 = median effect of common essential genes \|
37
38	Key thresholds:
39	- Chronos ≤ −0.5: likely dependent
40	- Chronos ≤ −1: strongly dependent (common essential range)
41
42	### Cell Line Annotations
43
44	Each cell line has:
45	- `DepMap_ID`: unique identifier (e.g., `ACH-000001`)
46	- `cell_line_name`: human-readable name
47	- `primary_disease`: cancer type
48	- `lineage`: broad tissue lineage
49	- `lineage_subtype`: specific subtype
50
51	## Core Capabilities
52
53	### 1. DepMap API
54
55	```python
56	import requests
57	import pandas as pd
58
59	BASE_URL = "https://depmap.org/portal/api"
60
61	def depmap_get(endpoint, params=None):
62	url = f"{BASE_URL}/{endpoint}"
63	response = requests.get(url, params=params)
64	response.raise_for_status()
65	return response.json()
66	```
67
68	### 2. Gene Dependency Scores
69
70	```python
71	def get_gene_dependency(gene_symbol, dataset="Chronos_Combined"):
72	"""Get CRISPR dependency scores for a gene across all cell lines."""
73	url = f"{BASE_URL}/gene"
74	params = {
75	"gene_id": gene_symbol,
76	"dataset": dataset
77	}
78	response = requests.get(url, params=params)
79	return response.json()
80
81	# Alternatively, use the /data endpoint:
82	def get_dependencies_slice(gene_symbol, dataset_name="CRISPRGeneEffect"):
83	"""Get a gene's dependency slice from a dataset."""
84	url = f"{BASE_URL}/data/gene_dependency"
85	params = {"gene_name": gene_symbol, "dataset_name": dataset_name}
86	response = requests.get(url, params=params)
87	data = response.json()
88	return data
89	```
90
91	### 3. Download-Based Analysis (Recommended for Large Queries)
92
93	For large-scale analysis, download DepMap data files and analyze locally:
94
95	```python
96	import pandas as pd
97	import requests, os
98
99	def download_depmap_data(url, output_path):
100	"""Download a DepMap data file."""
101	response = requests.get(url, stream=True)
102	with open(output_path, 'wb') as f:
103	for chunk in response.iter_content(chunk_size=8192):
104	f.write(chunk)
105
106	# DepMap 24Q4 data files (update version as needed)
107	FILES = {
108	"crispr_gene_effect": "https://figshare.com/ndownloader/files/...",
109	# OR download from: https://depmap.org/portal/download/all/
110	# Files available:
111	# CRISPRGeneEffect.csv - Chronos gene effect scores
112	# OmicsExpressionProteinCodingGenesTPMLogp1.csv - mRNA expression
113	# OmicsSomaticMutationsMatrixDamaging.csv - mutation binary matrix
114	# OmicsCNGene.csv - copy number
115	# sample_info.csv - cell line metadata
116	}
117
118	def load_depmap_gene_effect(filepath="CRISPRGeneEffect.csv"):
119	"""
120	Load DepMap CRISPR gene effect matrix.
121	Rows = cell lines (DepMap_ID), Columns = genes (Symbol (EntrezID))
122	"""
123	df = pd.read_csv(filepath, index_col=0)
124	# Rename columns to gene symbols only
125	df.columns = [col.split(" ")[0] for col in df.columns]
126	return df
127
128	def load_cell_line_i

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Type

depmap

31kstars3.1kforks

View on GitHub

How to install depmap?

k-dense-ai/scientific-agent-skills/depmap

$npx -y skills add K-Dense-AI/scientific-agent-skills --skill depmap

Installs into the current project.

Summary

Files · 1

View on GitHub

SKILL.md

1	# DepMap — Cancer Dependency Map
2
3	## Overview
4
5	The Cancer Dependency Map (DepMap) project, run by the Broad Institute, systematically characterizes genetic dependencies across hundreds of cancer cell lines using genome-wide CRISPR knockout screens (DepMap CRISPR), RNA interference (RNAi), and compound sensitivity assays (PRISM). DepMap data is essential for:
6	- Identifying which genes are essential for specific cancer types
7	- Finding cancer-selective dependencies (therapeutic targets)
8	- Validating oncology drug targets
9	- Discovering synthetic lethal interactions
10
11	Key resources:
12	- DepMap Portal: https://depmap.org/portal/
13	- DepMap data downloads: https://depmap.org/portal/download/all/
14	- Python package: `depmap` (or access via API/downloads)
15	- API: https://depmap.org/portal/api/
16
17	## When to Use This Skill
18
19	Use DepMap when:
20
21	- Target validation: Is a gene essential for survival in cancer cell lines with a specific mutation (e.g., KRAS-mutant)?
22	- Biomarker discovery: What genomic features predict sensitivity to knockout of a gene?
23	- Synthetic lethality: Find genes that are selectively essential when another gene is mutated/deleted
24	- Drug sensitivity: What cell line features predict response to a compound?
25	- Pan-cancer essentiality: Is a gene broadly essential across all cancer types (bad target) or selectively essential?
26	- Correlation analysis: Which pairs of genes have correlated dependency profiles (co-essentiality)?
27
28	## Core Concepts
29
30	### Dependency Scores
31
32	\| Score \| Range \| Meaning \|
33	\|-------\|-------\|---------\|
34	\| Chronos (CRISPR) \| ~ -3 to 0+ \| More negative = more essential. Common essential threshold: −1. Pan-essential genes ~−1 to −2 \|
35	\| RNAi DEMETER2 \| ~ -3 to 0+ \| Similar scale to Chronos \|
36	\| Gene Effect \| normalized \| Normalized Chronos; −1 = median effect of common essential genes \|
37
38	Key thresholds:
39	- Chronos ≤ −0.5: likely dependent
40	- Chronos ≤ −1: strongly dependent (common essential range)
41
42	### Cell Line Annotations
43
44	Each cell line has:
45	- `DepMap_ID`: unique identifier (e.g., `ACH-000001`)
46	- `cell_line_name`: human-readable name
47	- `primary_disease`: cancer type
48	- `lineage`: broad tissue lineage
49	- `lineage_subtype`: specific subtype
50
51	## Core Capabilities
52
53	### 1. DepMap API
54
55	```python
56	import requests
57	import pandas as pd
58
59	BASE_URL = "https://depmap.org/portal/api"
60
61	def depmap_get(endpoint, params=None):
62	url = f"{BASE_URL}/{endpoint}"
63	response = requests.get(url, params=params)
64	response.raise_for_status()
65	return response.json()
66	```
67
68	### 2. Gene Dependency Scores
69
70	```python
71	def get_gene_dependency(gene_symbol, dataset="Chronos_Combined"):
72	"""Get CRISPR dependency scores for a gene across all cell lines."""
73	url = f"{BASE_URL}/gene"
74	params = {
75	"gene_id": gene_symbol,
76	"dataset": dataset
77	}
78	response = requests.get(url, params=params)
79	return response.json()
80
81	# Alternatively, use the /data endpoint:
82	def get_dependencies_slice(gene_symbol, dataset_name="CRISPRGeneEffect"):
83	"""Get a gene's dependency slice from a dataset."""
84	url = f"{BASE_URL}/data/gene_dependency"
85	params = {"gene_name": gene_symbol, "dataset_name": dataset_name}
86	response = requests.get(url, params=params)
87	data = response.json()
88	return data
89	```
90
91	### 3. Download-Based Analysis (Recommended for Large Queries)
92
93	For large-scale analysis, download DepMap data files and analyze locally:
94
95	```python
96	import pandas as pd
97	import requests, os
98
99	def download_depmap_data(url, output_path):
100	"""Download a DepMap data file."""
101	response = requests.get(url, stream=True)
102	with open(output_path, 'wb') as f:
103	for chunk in response.iter_content(chunk_size=8192):
104	f.write(chunk)
105
106	# DepMap 24Q4 data files (update version as needed)
107	FILES = {
108	"crispr_gene_effect": "https://figshare.com/ndownloader/files/...",
109	# OR download from: https://depmap.org/portal/download/all/
110	# Files available:
111	# CRISPRGeneEffect.csv - Chronos gene effect scores
112	# OmicsExpressionProteinCodingGenesTPMLogp1.csv - mRNA expression
113	# OmicsSomaticMutationsMatrixDamaging.csv - mutation binary matrix
114	# OmicsCNGene.csv - copy number
115	# sample_info.csv - cell line metadata
116	}
117
118	def load_depmap_gene_effect(filepath="CRISPRGeneEffect.csv"):
119	"""
120	Load DepMap CRISPR gene effect matrix.
121	Rows = cell lines (DepMap_ID), Columns = genes (Symbol (EntrezID))
122	"""
123	df = pd.read_csv(filepath, index_col=0)
124	# Rename columns to gene symbols only
125	df.columns = [col.split(" ")[0] for col in df.columns]
126	return df
127
128	def load_cell_line_i

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