Intermediate data for TE calculation

This dataset includes intermediate data from RiboBase that generates translation efficiency (TE). The code to generate the files can be found at https://github.com/CenikLab/TE_model. We uploaded demo HeLa .ribo files, but due to the large storage requirements of the full dataset, I recommend contacting Dr. Can Cenik directly to request access to the complete version of RiboBase if you need the original data. The detailed explanation for each file: human_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in human. human_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in human. human_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in human. human_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in human. human_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in human. human_TE_rho.rda: TE proportional similarity data as genes by genes matrix in human. mouse_flatten_ribo_clr.rda: ribosome profiling clr normalized data with GEO GSM ids in columns and genes in rows in mouse. mouse_flatten_rna_clr.rda: matched RNA-seq clr normalized data with GEO GSM ids in columns and genes in rows in mouse. mouse_flatten_te_clr.rda: TE clr data with GEO GSM ids in columns and genes in rows in mouse. mouse_TE_cellline_all_plain.csv: TE clr data with genes in rows and cell lines in rows in mouse. mouse_RNA_rho_new.rda: matched RNA-seq proportional similarity data as genes by genes matrix in mouse. mouse_TE_rho.rda: TE proportional similarity data as genes by genes matrix in mouse. All the data was passed quality control. There are 1054 mouse samples and 835 mouse samples: * coverage > 0.1 X * CDS percentage > 70% * R2 between RNA and RIBO >= 0.188 (remove outliers) All ribosome profiling data here is non-dedup winsorizing data paired with RNA-seq dedup data without winsorizing (even though it names as flatten, it just the same format of the naming) ####codeIf you need to read rda data please use load("rdaname.rda") with R If you need to calculate proportional similarity from clr data:library(propr)human_TE_homo_rho <- propr:::lr2rho(as.matrix(clr_data))rownames(human_TE_homo_rho) <- colnames(human_TE_homo_rho) <- rownames(clr_data)本数据集包含源自RiboBase的、用于生成翻译效率（translation efficiency, TE）的中间数据。生成该数据集文件的代码可于https://github.com/CenikLab/TE_model处获取。 我们上传了HeLa细胞的示例.ribo格式文件，但由于完整数据集存储需求较高，若您需要获取原始完整数据，建议直接联系Can Cenik博士申请RiboBase完整版本的使用权限。 各文件详细说明如下： 1. human_flatten_ribo_clr.rda：人类样本的核糖体谱中心化对数比（centered log-ratio, clr）归一化数据，数据以基因为行、基因表达综合数据库（Gene Expression Omnibus, GEO）的GSM编号为列。 2. human_flatten_rna_clr.rda：人类样本的匹配RNA测序clr归一化数据，数据以基因为行、GEO GSM编号为列。 3. human_flatten_te_clr.rda：人类样本的TE clr归一化数据，数据以基因为行、GEO GSM编号为列。 4. human_TE_cellline_all_plain.csv：人类样本的TE clr归一化数据，数据以基因为行、细胞系为列。 5. human_RNA_rho_new.rda：人类样本的匹配RNA测序比例相似度数据，为基因×基因矩阵格式。 6. human_TE_rho.rda：人类样本的TE比例相似度数据，为基因×基因矩阵格式。 7. mouse_flatten_ribo_clr.rda：小鼠样本的核糖体谱clr归一化数据，数据以基因为行、GEO GSM编号为列。 8. mouse_flatten_rna_clr.rda：小鼠样本的匹配RNA测序clr归一化数据，数据以基因为行、GEO GSM编号为列。 9. mouse_flatten_te_clr.rda：小鼠样本的TE clr归一化数据，数据以基因为行、GEO GSM编号为列。 10. mouse_TE_cellline_all_plain.csv：小鼠样本的TE clr归一化数据，数据以基因为行、细胞系为列。 11. mouse_RNA_rho_new.rda：小鼠样本的匹配RNA测序比例相似度数据，为基因×基因矩阵格式。 12. mouse_TE_rho.rda：小鼠样本的TE比例相似度数据，为基因×基因矩阵格式。 所有数据均经过质量控制。本数据集包含1054个人类样本与835个小鼠样本，质控标准如下： * 测序覆盖度>0.1X * 编码区（coding sequence, CDS）占比>70% * RNA测序与核糖体谱数据的决定系数（coefficient of determination, R²）≥0.188（已剔除异常值） 本数据集所有核糖体谱数据均为未去重的缩尾（winsorizing）处理数据，配套的RNA测序数据为经过去重但未缩尾的处理数据（尽管部分文件命名带有“flatten”字样，仅为统一命名格式，无特殊含义）。 ####代码 若需读取.rda格式数据，请在R语言环境中执行命令load("rdaname.rda")。 若需从clr归一化数据计算比例相似度，请执行以下代码： library(propr) human_TE_homo_rho <- propr:::lr2rho(as.matrix(clr_data)) rownames(human_TE_homo_rho) <- colnames(human_TE_homo_rho) <- rownames(clr_data)