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Table 1 Summary of RNA-seq experimental scenarios

From: On the utility of RNA sample pooling to optimize cost and statistical power in RNA sequencing experiments

ScenarioNumber ofNumber ofTotal readsTotal cost≈ depth perNumber ofPool sizeRNA
 RNAlibrariescounts library ×106libraries pooling
 samples ×106 (min – max)per group  
a Scenarios based on the Zhang neuroblastoma samples
A0 (reference)80801600€ 21,800.0020 (11.2–30.0)40-No
A14040800€ 10,800.0020 (11.2–29.7)20-No
A24040400€ 7,800.0010 (4.9–13.1)20-No
A38080800€ 15,600.0010 (5.0–13.5)40-No
A48080400€ 12,600.005 (2.5–6.7)40-No
B18040800€ 11,600.0020 (11.8–28.3)202Yes
B24020400€ 5,800.0020 (13.4–28.7)102Yes
B38040400€ 8,600.0010 (5.3–12.7)202Yes
B44020200€ 4,300.0010 (6.0–12.9)102Yes
C18020400€ 6,600.0020 (15.00–27.8)104Yes
C24010200€ 3,300.0020 (14.7–26.0)54Yes
C38020200€ 5,100.0010 (6.7–12.5)104Yes
C44010100€ 2,550.0010 (6.6–11.7)54Yes
b Scenarios based on the NGP neuroblastoma cell lines
A0 (reference)1818270€ 4,185.0015 (14.3–19.3)9-No
A6690€ 1,395.0015 (15.0–17.7)3-No
B12690€ 1,515.0015 (14.9–17.6)32Yes
C18690€ 1,635.0015 (15.3–17.9)33Yes
  1. The total data generation cost of a particular scenario is given by (S×20)+(L×100)+(R×7.5), where S is the number of RNA samples (with RNA preparation cost €20.00 per sample), L is the number of libraries (with library preparation cost €100.00 per library), R is the total sequencing depth (with cost €7.50 per 1 million sequencing reads)