Dataset Factsheet

Description of the dataset

6 different cell types including 5 immune cell types (“T cells”, “NK cells”, “B cells”, “monocytic lineage”, “neutrophils”) were used to simulate mRNA mixtures. This benchmark dataset has been used to evaluate MCP-counter method and published in Genome Biology

Omics_type = tanscriptome

Cancer_type = colorectal

Cohort_size = 12

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.

Normalisation = fRMA

Transformation = none, linear scale

Composition of the public data

test_data = readRDS("D_rna_immune_notlog.rds")
test_data = list(test_data)
dim(test_data[[1]])

## [1] 23520    12

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

	sample_1	sample_2	sample_3	sample_4	sample_5
A1BG	33.92289	40.86085	40.67894	43.11846	31.04856
A1BG-AS1	116.45327	112.93290	131.87088	151.22064	120.85833
A1CF	30.54869	36.51792	31.10683	29.04386	26.94460
A2M	109.77855	109.49456	174.47026	172.82642	120.45138
A2M-AS1	26.56403	32.61427	180.10677	93.52536	68.84047
A2ML1	13.24947	15.22251	15.54850	14.45150	14.92240
A2MP1	35.47989	32.49386	153.97141	134.75457	64.87187
A4GALT	157.27281	170.62494	125.04407	137.07057	108.28758
A4GNT	13.90828	15.18025	12.47403	15.08149	12.42636
AA06	41.31915	44.99733	37.21000	37.28777	30.74366

saveRDS(test_data, "test_data.rds")

Expected number of cell types

print(readRDS("input_k_value.rds"))

## [1] 6

Cancer type

print(readRDS("cancer_type.rds"))

## [1] "coad"

Composition of the ground truth

Source = in silico simulations

Number of expected cell types = 6

## [1]  6 12