---

name: sampleinfo description: The SampleInfo process is the pipeline entry point that reads sample metadata files, performs statistical analyses, and generates visualization reports.

SampleInfo Process Configuration

Purpose

The SampleInfo process is the pipeline entry point that reads sample metadata files, performs statistical analyses, and generates visualization reports.

When to Use

• Always required as first process unless using LoadingRNAFromSeurat
• When you have sample metadata in CSV/TSV format
• When you need to generate statistical summaries and visualizations
• When you want to add or transform metadata columns before downstream analysis

Note: Mutually exclusive with LoadingRNAFromSeurat.

Configuration Structure

[SampleInfo]
cache = true

[SampleInfo.in]
infile = "path/to/sample_info.txt"  # Required: yes (unless using LoadingRNAFromSeurat)

[SampleInfo.envs]
sep = "\t"                  # str - File separator
mutaters = {}                # dict - Column transformations using R expressions
save_mutated = false         # bool - Save mutated columns to output
exclude_cols = "TCRData,BCRData,RNAData"  # Columns hidden in report
defaults = { plot_type = "bar", more_formats = [], save_code = false }
stats = {}                   # dict - Statistical plot definitions

Required input columns: Sample (unique ID), RNAData (directory path) Optional columns: TCRData, BCRData, additional metadata Data format: CSV/TSV with header, RNA data must be Read10X()-compatible

Environment Variables

sep (string): Field separator - "\t", ",", ";", or any character

mutaters (dict): R expressions for dplyr::mutate(). Keys are column names, values are R expressions.

• Example: mutaters = { "AgeGroup" = "ifelse(Age > 60, 'Senior', 'Adult')" }
• Special function paired() identifies paired samples: paired(., 'PatientID', 'Timepoint', c('T1', 'T2'))

save_mutated (bool): Save mutated columns to output file. Factor columns lose level ordering when saved as text.

exclude_cols (str/list): Comma-separated string or list of columns to exclude from report table.

defaults (dict): Default plot parameters inherited by all plots:

[SampleInfo.envs.defaults]
plot_type = "bar"           # Plot type (see External References)
more_formats = []            # Additional formats: ["pdf", "svg"]
save_code = false            # Save R code and data
subset = null                # dplyr::filter expression
section = null               # Report section name
descr = null                 # Plot description
width = null, height = null, res = 100  # Plot dimensions

stats (dict): Plot definitions. Keys are case names (titles), values inherit from defaults.

External References

Plotthis Functions

plot_type	Function	Description
pie	PieChart()	Pie chart
bar	BarPlot()	Bar plot
box	BoxPlot()	Box plot
violin	ViolinPlot()	Violin plot
histogram	Histogram()	Histogram
density	DensityPlot()	Density plot
scatter	ScatterPlot()	Scatter plot
line	LinePlot()	Line plot
ridge	RidgePlot()	Ridge plot
heatmap	Heatmap()	Heatmap

Full reference: https://pwwang.github.io/plotthis/reference/

Common Plot Parameters

x = "column_name", y = "column_name"  # Axis columns
split_by = "column_name", facet_by = "column_name"  # Split/facet
palette = "Paired", alpha = 1.0  # Color and transparency
title = "Plot Title", nrow = 2, ncol = 3  # Layout
legend.position = "right"  # Legend placement

dplyr::filter() for subset

subset = "Sample == 'A'"
subset = "Age > 60"
subset = "Diagnosis %in% c('Colitis', 'Control')"
subset = "Sex == 'F' & Age > 50"

Configuration Examples

Minimal Configuration

[SampleInfo.in]
infile = "samples.txt"

Basic Statistics

[SampleInfo.in]
infile = "sample_info.txt"

[SampleInfo.envs.stats."Samples_per_Diagnosis"]
plot_type = "bar"
x = "Sample"
split_by = "Diagnosis"

Advanced Configuration

[SampleInfo.in]
infile = "metadata/samples.tsv"

[SampleInfo.envs]
save_mutated = true
mutaters = { "AgeGroup" = "ifelse(Age > 60, 'Senior', 'Adult')" }

[SampleInfo.envs.stats."N_Samples_per_Diagnosis"]
x = "Sample"
split_by = "Diagnosis"

[SampleInfo.envs.stats."Age_distribution"]
plot_type = "histogram"
x = "Age"

Common Patterns

Paired Sample Identification

[SampleInfo.envs]
mutaters = { "PairID" = "paired(., 'PatientID', 'Timepoint', c('T1', 'T2'))" }

[SampleInfo.envs.stats."Paired_Samples"]
x = "PairID"
subset = "!is.na(PairID)"

Subset Analysis

[SampleInfo.envs.stats."Controls_Only"]
x = "Sample"
split_by = "Diagnosis"
subset = "Diagnosis == 'Control'"

Dependencies

• Upstream: None (entry point process)
• Downstream: All pipeline processes depend on SampleInfo output
  • SeuratPreparing: Reads sample metadata
  • ScRepLoading: Uses TCRData/BCRData columns
  • All downstream: Use metadata columns for analysis

Validation Rules

Common Errors

1. Missing input file: Always specify [SampleInfo.in.infile]
2. Invalid separator: Match separator to file format (e.g., sep = "," for CSV)
3. Missing required columns: Ensure Sample and RNAData columns exist
4. Factor level ordering: Don't use save_mutated for factor columns - use SeuratPreparing.envs.mutaters instead

Value Constraints

• sep: Single character string
• mutaters: Valid R expressions
• stats keys: Must be unique case names
• devpars.res: Positive integer (default: 100)

Troubleshooting

• Issue: SampleInfo re-runs entire pipeline on parameter change Solution: Set cache = "force" at pipeline level and [SampleInfo] cache = false

• Issue: Factor levels appear in wrong order Solution: Use SeuratPreparing.envs.mutaters for factor columns

• Issue: Plots don't show expected data Solution: Check column names in x, y, split_by match input file exactly

• Issue: Paired sample function returns NA values Solution: Use uniq = false in paired() or adjust idents parameter

• Issue: Mutations not saved for downstream use Solution: Set save_mutated = true. For Seurat metadata, use SeuratPreparing.envs.mutaters

• Issue: Plot type not recognized Solution: Ensure plot_type is lowercase and maps to a plotthis function