Processed data (CRISPR vs. RNAi)

The referenced software pipeline creates and stores this set of intermediate files which are then used to generate figures and supplemental tables for Krill-Burger et al., “Partial inhibition improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal”. While many of these files are primarily deposited to speedup code runtime by allowing users to load precomputed intermediary files, there are a few useful result tables. Most notably, these results include pan-dependency scores and probability of dependency (Bayesian inference of gene effects used for binary hit calling) for each genetic perturbation dataset as well as predictive models (ensemble of random forests) for each gene effect profile using multi-omics datasets as predictive features. File types are comma separated text files (csv) or R data objects (rds). See the ReadMe.pdf for detailed file descriptions. <br>本所引用的软件流水线生成并存储了一系列中间文件，后续用于生成Krill-Burger等人发表的"Partial inhibition improves identification of cancer vulnerabilities when CRISPR-Cas9 knockout is pan-lethal"一文的配图与补充表格。尽管其中多数文件的主要用途是允许用户加载预计算的中间文件以加速代码运行效率，但仍存在部分具备实用价值的结果表格。尤为值得注意的是，此类结果涵盖了针对每一项遗传扰动数据集的泛依赖性评分（pan-dependency scores）与依赖性概率（probability of dependency，即用于二元命中调用的基因效应贝叶斯推断），同时还包含了以多组学数据集（multi-omics datasets）作为预测特征、针对每一类基因效应谱的预测模型——随机森林集成模型（ensemble of random forests）。文件格式为逗号分隔文本文件（CSV）或R数据对象（rds）。详见ReadMe.pdf以获取详细的文件说明。