Table 2.

Computational workflow and dimensionality for analyzed data streams

	Skeletal Muscle			Serum EVs
Transcript-Level Differential Expression Analysis	Long RNA	Small RNA	circRNA	Long RNA	Small RNA	circRNA
Raw counts (transcripts × samples)	94,946 × 120	85,542 × 120	39,577 × 120	94,946 × 148	85,542 × 154	197,605 × 148
Filtering Steps:
1. to remove participants missing pre-exercise sample	94,946 × 120	85,542 × 120	39,577 × 120	94,946 × 140	85,542 × 154	197,605 × 140
2. by biotype (based on length at transcript maturity)	86,636 × 120	5,398 × 120	39,577 × 120	86,636 × 140	5,398 × 154	197,605 × 140
3. for expression (cpm >0) in ≥25% of samples	32,295 × 120	2,673 × 120	1,812 × 120	62,772 × 140	1,313 × 154	1,120 × 140
Batch effect identification (adjusted with ComBat_seq if needed)	Adjusted
Outlier (±6 MAD from median) identification	32,295 × 120	2,673 × 120	1,812 × 120	62,772 × 133	1,313 × 146	1,120 × 140
Filtering by expression (long RNA only)	23,329 × 121			57,025 × 133
limma differential expression analysis with voom correction
Exploratory Transcriptomic Network Discovery
FDR < 0.2 at any time point	19,444	1,728	148	55,325	772	310
All biotype counts collapsed within tissue & filtered for shared samples	21,320 × 120			56,407 × 128
PLIER singular value decomposition:
n LVs generated (n showing main effect of exercise)	40 (23)			32 (4)

See text for complete workflow, including steps that do not affect data dimensionality. cpm, counts per million; EVs, extracellular vesicles; FDR, false discovery rate; LVs, latent variables; MAD, mean absolute deviation; PLIER, Pathway-Level information extractor.