Yeast SNF2 RNA-seq de novo 24-sample standard

Assembly quality组装质量

Assembly metrics describe the transcriptome object that replaces a reference-derived transcript FASTA. N50, filtered transcript count, read support, and optional BUSCO completeness help judge whether this feature space is credible enough for downstream expression.组装指标描述替代参考转录本 FASTA 的转录组对象。N50、过滤后转录本数量、read support 和可选 BUSCO 完整性用于判断这个 feature 空间是否足以支撑后续表达分析。

BUSCO statusBUSCO 状态

Assembly QC reports组装 QC 报告

De novo expression semantics无参表达矩阵语义

The primary de novo expression matrices are transcript-level. Gene-level or pseudo-gene-level matrices are meaningful only when a tx2gene table or cluster map was explicitly supplied upstream.无参表达的主矩阵是转录本层面。只有上游显式提供 tx2gene 或聚类映射时，基因层面或伪基因层面矩阵才有相应语义。

De novo expression QC reports无参表达 QC 报告

Annotation and enrichment readiness注释与富集可用性

Functional annotation is homology-derived evidence controlled by the supplied protein database and optional GO map. Enrichment is available only when annotation-derived GMT/background files exist and match the DE feature ID space.功能注释是由同源搜索转移而来的证据，受用户提供的蛋白数据库和可选 GO map 控制。只有注释流程生成的 GMT/background 文件存在，并且与差异分析 feature ID 空间一致时，富集才可用。

Sample structure and distributions样本结构与数据分布

These plots are quality and design checks for the statistical model: PCA and correlation show sample relationships, while expression/count distributions show whether normalization and filtering produced comparable samples.这些图用于检查统计模型的前提：PCA 和相关性展示样本关系，表达/计数分布用于判断归一化与过滤后样本是否可比。

Reviews expression/count distribution before interpreting DE results.在解读差异表达前检查表达量或计数分布。

Reviews expression/count distribution before interpreting DE results.在解读差异表达前检查表达量或计数分布。

Shows whether biological groups separate and whether outlier samples need review.查看实验组是否分离、是否存在离群样本。

Checks replicate similarity and global sample relationships.检查重复样本一致性和整体样本关系。

Differential expression signal差异表达信号

These figures summarize the actual contrast: volcano and MA plots combine effect size with uncertainty, DEG counts show the threshold-level balance, and heatmap/top-gene plots make representative expression patterns inspectable.这些图展示真正的组间比较结果：火山图和 MA 图结合效应量与不确定性，差异基因数量图展示阈值下的方向平衡，热图和 Top 基因图帮助查看代表性表达模式。

Summarizes up- and down-regulated gene counts under the selected thresholds.汇总阈值下上调和下调基因数量。

Shows expression patterns of selected variable or DE genes across samples.展示代表性基因在样本间的表达模式。

Shows fold-change behavior across mean expression levels.查看不同表达水平上的倍数变化趋势。

Makes high-impact DE genes easier to inspect by sample group.便于按样本组查看重点差异基因表达。

Combines effect size and significance to highlight candidate DE genes.同时展示效应量和显著性，突出候选差异基因。

ORA visual summaryORA 可视化摘要

ORA starts from the significant-gene list and asks which gene sets contain more hits than expected under the selected background universe. The readable dotplot is the primary review figure, the classic dotplot preserves a familiar audit view, and the barplot emphasizes the strongest term-level signals.ORA 从显著差异基因列表出发，检验在选定背景基因集中哪些 gene set 命中数高于随机期望。可读气泡图是主要查看图，经典气泡图保留常见审计视图，柱状图突出最强的条目层面信号。

Summarizes enriched biological processes with wrapped readable labels.用换行后的可读标签展示富集生物过程。

Preserves the classic enrichment view with improved canvas sizing for audit.保留经典富集视图，并优化画布尺寸以便审计。

Ranks top over-represented gene sets by significance while showing term-level signal.按显著性展示 ORA Top 基因集合，便于快速查看条目强度。

GSEA directional summaryGSEA 方向性摘要

GSEA uses the ranked gene list rather than a hard significant-gene cutoff. NES plots compare positive and negative enrichment directions, while running curves show where representative gene-set members accumulate along the ranked evidence list.GSEA 使用排序基因列表，而不是只看显著基因阈值。NES 图比较正向和负向富集强度，running curve 展示代表性 gene set 成员在排序证据列表中的累积位置。

Displays running enrichment curves for representative GSEA gene sets.展示代表性 GSEA gene set 的 running enrichment 曲线。

Shows the strongest positive and negative normalized enrichment scores from preranked GSEA.展示 preranked GSEA 中正向和负向最强的标准化富集分数。