Package 'scPharm'

Bulk RNA-seq Expression Data for Cancer Cell Lines

Description

TPM-normalized gene expression profiles for tumor cell lines from the Cell Model Passports database.

Usage

bulkdata

Format

A data frame with 37,004 genes (rows) and 1,387 cell lines (columns). Row names are gene symbols; column names are cell line identifiers.

Source

Cell Model Passports https://cellmodelpassports.sanger.ac.uk/downloads

References

van der Meer D, et al. (2019). Cell Model Passports - a curated and standardised dataset of pre-clinical cancer models. Nucleic Acids Research.

---

GDSC2 Drug Information

Description

Drug metadata including targets and signaling pathways from the GDSC2 project.

Usage

drug_info

Format

A data frame with 295 drugs and 4 variables:

DRUG_ID

GDSC drug identifier

DRUG_NAME

Drug name

PUTATIVE_TARGET

Known drug target(s)

PATHWAY_NAME

Target signaling pathway

Source

GDSC https://www.cancerrxgene.org/downloads/drug_data

---

GDSC2 Pharmacogenomics Data

Description

Drug sensitivity data (IC50 and AUC) for cancer cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) project.

Usage

gdscdata

Format

A data frame with 196,344 observations and 19 variables including:

DRUG_ID

GDSC drug identifier

DRUG_NAME

Drug name

CELL_LINE_NAME

Cell line identifier

COSMIC_ID

COSMIC cell line ID

SANGER_MODEL_ID

Sanger model ID

TCGA_DESC

TCGA cancer type classification

DATASET

Dataset source

COMPANY_ID

Company identifier

NLME_RESULT_ID

NLME result ID

NLME_CURVE_ID

NLME curve ID

LN_IC50

Natural log of IC50

AUC

Area under the dose-response curve

RMSE

Root mean square error

Z_SCORE

Z-score

MAX_CONC

Maximum concentration tested

MIN_CONC

Minimum concentration tested

PUTATIVE_TARGET

Known drug target

PATHWAY_NAME

Target pathway

WEBRELEASE

Web release version

Source

GDSC https://www.cancerrxgene.org/downloads/drug_data

References

Yang W, et al. (2013). Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Research 41, D955-D961.

---

Identify Potential Drug Combinations

Description

Identify potential drug combinations based on two strategies: (1) Compensation effects: drugs that target cells resistant to the primary drug (2) Booster effects: drugs that enhance sensitivity through different pathways

Usage

scPharmCombo(object, score, drug = NULL, topN = 1, drug_info = NULL)

Arguments

object	A Seurat object after running scPharmIdentify.
score	Output from scPharmDr.
drug	Name of the primary drug. If NULL, uses topN drugs.
topN	Number of top-ranked drugs to analyze. Default: 1.
drug_info	Drug information table. If NULL, uses built-in scPharm::drug_info.

Details

Compensation effects: Identifies drugs where cells resistant to the primary drug show sensitivity. This suggests the combination could overcome resistance.

Booster effects: Identifies drugs targeting different pathways that show high sensitivity in cells already sensitive to the primary drug. This suggests synergistic enhancement.

Value

A named list where each element corresponds to a primary drug and contains a data frame with:

DRUG_FIRST

Primary drug name

DRUG_ID

Combination drug ID

DRUG_NAME

Combination drug name

Effect

Combination effect score

Strategy

"compensation effects" or "booster effects"

See Also

scPharmIdentify, scPharmDr

Examples

## Not run: 
dr_scores <- scPharmDr(result)
combos <- scPharmCombo(result, dr_scores, topN = 3)

## End(Not run)

---

Compute Drug Prioritization Score (Dr)

Description

Calculate drug prioritization scores based on the ratio of sensitive and resistant tumor cell populations identified by scPharmIdentify.

Usage

scPharmDr(object)

Arguments

object

A Seurat object after running scPharmIdentify.

Details

The Dr score is calculated as:

$Dr = S \times (1 - R)$

where S is the proportion of sensitive cells and R is the proportion of resistant cells among tumor cells. Higher scores indicate better drug candidates for the patient.

Value

A data frame with the following columns:

DRUG_ID

GDSC drug identifier

DRUG_NAME

Drug name

SENSI_RATIO

Proportion of sensitive tumor cells

RESIS_RATIO

Proportion of resistant tumor cells

Dr

Drug prioritization score

Rank

Drug ranking (1 = best)

See Also

scPharmIdentify, scPharmCombo

Examples

## Not run: 
# After running scPharmIdentify
dr_scores <- scPharmDr(result)
head(dr_scores)

## End(Not run)

---

Predict Drug Side Effects (Dse)

Description

Calculate drug side effect scores based on the sensitivity of adjacent (normal) cells to drugs. Higher scores indicate greater potential for off-target toxicity.

Usage

scPharmDse(object)

Arguments

object

A Seurat object after running scPharmIdentify with type="tissue".

Details

The Dse score represents the proportion of adjacent (non-tumor) cells that are classified as sensitive to each drug. Drugs with high Dse scores may cause more side effects by affecting normal cells.

Value

A data frame with the following columns:

DRUG_ID

GDSC drug identifier

DRUG_NAME

Drug name

Dse

Side effect score (0-1, higher = more side effects)

See Also

scPharmIdentify, scPharmDr

Examples

## Not run: 
# After running scPharmIdentify with type="tissue"
dse_scores <- scPharmDse(result)
head(dse_scores)

## End(Not run)

---

Generate Null Distribution and Thresholds

Description

Generate a null distribution from healthy tissue cells and calculate thresholds for classifying sensitive and resistant cells.

Usage

scPharmGenNullDist(
  object,
  cancer,
  nmcs = 50,
  nfeatures = 200,
  cores = 1,
  features = NULL,
  slot = "data",
  layer = NULL,
  assay = "RNA",
  bulkdata = NULL,
  gdscdata = NULL
)

Arguments

object	A Seurat object containing cells from healthy/normal tissue.
cancer	TCGA cancer type(s) for context. A character string or vector. Use "pan" for pan-cancer analysis.
nmcs	Number of MCA components. Default: 50.
nfeatures	Number of genes for cell identity signature. Default: 200.
cores	Number of CPU cores. Default: 1.
features	Character vector of gene names to use. If NULL, uses all.
slot	Slot for Seurat V4. Default: "data".
layer	Layer for Seurat V5. If NULL, uses slot value.
assay	Assay to use. Default: "RNA".
bulkdata	Bulk RNA-seq data. If NULL, uses built-in data.
gdscdata	GDSC data. If NULL, uses built-in data.

Details

This function computes NES distributions from normal cells and uses a two-component Gaussian mixture model to determine thresholds. The thresholds are calculated as mean +/- 1 standard deviation of each component.

Value

A list containing:

NullDist

Numeric vector of NES values from normal cells

threshold_s

Threshold for sensitive cells (NES < threshold_s)

threshold_r

Threshold for resistant cells (NES > threshold_r)

See Also

scPharmIdentify

Examples

## Not run: 
# Using healthy tissue cells
thresholds <- scPharmGenNullDist(healthy_seurat, cancer = "BRCA")
print(thresholds$threshold_s)
print(thresholds$threshold_r)

## End(Not run)

---

Identify Pharmacological Cell Subpopulations

Description

Classify single cells into drug-sensitive, drug-resistant, or other subpopulations based on pharmacogenomics profiles from the GDSC2 database.

Usage

scPharmIdentify(
  object,
  type,
  cancer,
  drug = NULL,
  nmcs = 50,
  nfeatures = 200,
  cores = 1,
  features = NULL,
  slot = "data",
  layer = NULL,
  assay = "RNA",
  threshold.s = -1.751302,
  threshold.r = 1.518551,
  tumor.cells = NULL,
  normal.cells = NULL,
  bulkdata = NULL,
  gdscdata = NULL
)

Arguments

object	A Seurat object containing single-cell RNA-seq data.
type	Data source type. Either "tissue" for tumor tissue samples or "cellline" for cell line samples. When "tissue", the function identifies tumor vs adjacent normal cells using CNV analysis.
cancer	TCGA cancer type(s). A character string or vector specifying cancer type(s) (e.g., "BRCA", c("LUAD", "LUSC")). Use "pan" for pan-cancer analysis.
drug	Drug name to analyze. If NULL (default), all drugs from GDSC2 project will be analyzed.
nmcs	Number of MCA components to compute. Default: 50.
nfeatures	Number of genes for cell identity signature. Default: 200.
cores	Number of CPU cores for parallel processing. Default: 1.
features	Character vector of gene names to use. If NULL, all features are used.
slot	Slot name for Seurat V4 data access. Default: "data".
layer	Layer name for Seurat V5 data access. If NULL, uses slot value.
assay	Assay to use. Default: "RNA".
threshold.s	Threshold for labeling sensitive cells. Cells with NES below this value are classified as sensitive. Default: -1.751302.
threshold.r	Threshold for labeling resistant cells. Cells with NES above this value are classified as resistant. Default: 1.518551.
tumor.cells	Character vector of known tumor cell barcodes. If provided when type="tissue", CNV-based detection is skipped.
normal.cells	Character vector of known normal cell barcodes. Used as reference for CNV analysis when provided.
bulkdata	Bulk RNA-seq data for cell lines. If NULL, uses built-in scPharm::bulkdata.
gdscdata	GDSC pharmacogenomics data. If NULL, uses built-in scPharm::gdscdata.

Details

The function performs the following steps:

1. For tissue samples: identifies tumor cells via CNV analysis (or uses provided annotations)

2. Computes Multiple Correspondence Analysis (MCA) for dimensionality reduction

3. Generates cell identity gene signatures

4. Correlates gene expression with drug response (AUC) across cell lines

5. Performs GSEA to score each cell's drug response profile

6. Classifies cells based on NES thresholds

Value

A Seurat object with pharmacological annotations added to metadata:

cell.label

Cell type: "tumor" or "adjacent"

scPharm_label_DRUGID_DRUGNAME

Drug response: "sensitive", "resistant", or "other"

scPharm_nes_DRUGID_DRUGNAME

Normalized enrichment score for the drug

References

Tian P, Zheng J, et al. scPharm: identifying pharmacological subpopulations of single cells for precision medicine in cancers. 2023.

Examples

## Not run: 
# Basic usage
result <- scPharmIdentify(seurat_obj, type = "tissue", cancer = "LUAD")

# With known tumor cells
result <- scPharmIdentify(seurat_obj, type = "tissue", cancer = "LUAD",
                          tumor.cells = tumor_barcodes)

# Specific drug analysis
result <- scPharmIdentify(seurat_obj, type = "cellline", cancer = "BRCA",
                          drug = "Erlotinib")

## End(Not run)

---