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Table S1. The improvement in efficiency in RnaSeqSampleSize package. Table S2. Estimated sample size for RNA-Seq experiments in different cancer types by single parameter method. Table S3. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method. For each cancer type, we used the related TCGA dataset to estimate the read count and dispersion distribution. Table S4. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in interested KEGG pathway were considered. Table S5. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in KEGG pathway ID 05200 (Pathways in Cancer) were considered. Figure S1. A screen shot of user interface of RnaSeqSampleSize package. (DOCX 217 kb)

Table S1. The improvement in efficiency in RnaSeqSampleSize package. Table S2. Estimated sample size for RNA-Seq experiments in different cancer types by single parameter method. Table S3. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method. For each cancer type, we used the related TCGA dataset to estimate the read count and dispersion distribution. Table S4. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in interested KEGG pathway were considered. Table S5. Estimated sample size for RNA-Seq experiments in different cancer types by real data distribution based method, only the genes in KEGG pathway ID 05200 (Pathways in Cancer) were considered. Figure S1. A screen shot of user interface of RnaSeqSampleSize package. (DOCX 217Â kb)