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 and transformed using log2.

Normalisation = fRMA

Transformation = Log2

Composition of the public data

load("D_rna_immune.rds")
test_data = de.serieg
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	5.084187	5.352647	5.346210	5.430234	4.956454
A1BG-AS1	6.863607	6.819322	7.042982	7.240511	6.917173
A1CF	4.933038	5.190533	4.959159	4.860161	4.751924
A2M	6.778452	6.774715	7.446837	7.433180	6.912307
A2M-AS1	4.731402	5.027431	7.492709	6.547286	6.105185
A2ML1	3.727863	3.928135	3.958703	3.853147	3.899408
A2MP1	5.148930	5.022095	7.266519	7.074190	6.019521
A4GALT	7.297125	7.414685	6.966293	7.098775	6.758724
A4GNT	3.797872	3.924123	3.640855	3.914707	3.635332
AA06	5.368739	5.491767	5.217618	5.220630	4.942217

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