Fig. 2

PLATE-Seq performance. a Histogram of genes symbols detected per sample for a 96-well PLATE-Seq experiment in BT20 cells. b Histogram of uniquely mapped reads per sample for the experiment in a. c We pooled half of the sample from every six wells for conventional RNA-Seq with 30 M raw reads (Illumina TruSeq). Here we show a histogram of gene symbols detected per sample for each six-well TruSeq pool and for the sum of the corresponding six PLATE-Seq samples. d Same as c for uniquely mapped reads per sample. e Gene detection saturation curve for PLATE-Seq samples based on random subsampling. The points represent the average over all 96 wells and the error bars are deviations s.e.m. f Same as e but for each six-well TruSeq pool and for the sum of the corresponding six PLATE-Seq samples. g MDS clustering of PLATE-Seq and TruSeq samples based on differentially expressed genes identifying using the PLATE-Seq replicates for each drug compared to vehicle control samples. The PLATE-Seq replicates for each drug cluster together and also with the corresponding TruSeq samples. h Heat map showing the top 40 most differentially expressed genes based on PLATE-Seq of mitoxantrone- and idarubicin-treated BT20 cells measured with both PLATE-Seq and TruSeq. The two drugs are both topoisomerase II inhibitors and have similar gene expression signatures. i Same as h but with differentially active proteins as inferred using VIPER. Note that TOP2A, the gene that encodes the target of the two drugs, is strongly deactivated. j Gene expression of differentially active proteins inferred using VIPER. Most of these genes are not differentially expressed and some are difficult to detect with PLATE-Seq, yet VIPER can still infer their activities