Description of the dataset

The dataset was published¹ by Kang et al in Plos Computational Biology.

In brief, total mRNA was prepared from Namalwa (Burkitt’s lymphoma), Hs343T (fibroblast line derived from a mammary gland adenocarcinoma), hTERT-HME1 (normal mammary epithelial cells immortalized with hTERT), and MCF7 (estrogen receptor positive breast cancer cell line). The RNA samples was profiled by RNA-sequencing in duplicates.

Omics_type = transcriptome

Cancer_type = brca

Cohort_size = 30

Patient_metadata = No

Sample_type = In silico mixture of cell lines

Preparation of the data

Expression data from array were collected, normalized together using fRMA and transformed using log2.

Normalisation = edgeR

Transformation = none, linear scale

Aggregation = median

Composition of the test dataset

test_data = readRDS("test_data.rds")
length(test_data)

## [1] 5

dim(test_data[[1]])

## [1] 56646    30

colnames(test_data[[1]]) = paste0("sample_",1:dim(test_data[[1]])[2])
knitr::kable(head(test_data[[1]][,1:5], 10))

print(readRDS("input_k_value.rds"))

## [1] 4

print(readRDS("cancer_type.rds"))

## [1] "brca"

Composition of the solution dataset (ground truth)

Source = in silico simulations

Number of expected cell types = 4

5 independant proportion matrices and corresponding complex expression matrices have been generated to score the algorithm performances.

test_solution = readRDS("test_solution.rds")
length(test_solution)

## [1] 5

dim(test_solution[[1]])

## [1]  4 30