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Combine and Compare Population Activity Data

Compute Credits

This tool uses 1.0 compute credits per hour.

Overview

This tool combines population activity metrics (trace activity and optionally event rates) from multiple recordings, potentially across two experimental groups. It performs statistical comparisons to identify differences between defined behavioral states and, if applicable, between the two groups.

The tool employs a comprehensive multi-level statistical approach:

  1. Cell-Level Analysis: Uses Linear Mixed Models (LMM) to analyze individual cell activity or event rates, accounting for repeated measures within subjects. This approach compares activity across different behavioral states and assesses interactions between state and group effects.
  2. Subject-Level Modulation Analysis: Performs ANOVA (Repeated Measures or Mixed, depending on experimental design) on the number of significantly modulated cells per subject per state. This compares overall neural modulation patterns across states and groups.
  3. Subject-Level Activity Analysis: Calculates average activity or event rate per subject for each state, then performs ANOVA to compare overall neural activity levels between experimental conditions.

Key features include:

  • Support for single-group or two-group experimental designs
  • Paired (within-subject treatment) or unpaired (independent groups) experimental designs with flexible subject matching options
  • Separate analyses for average trace activity and event rate (event rate files are optional)
  • Multiple comparison correction methods and effect size calculations
  • Universal State Comparison Compatibility: Works with all state comparison types from the upstream Compare Neural Activity Across States tool, including baseline comparisons, state-vs-not-state, and pairwise comparisons

Parameters

Parameter Required? Default Description
Group 1 Population Activity Files True N/A Select population activity data from the first group to use for analysis
Group 1 Population Event Rate Files False N/A Select population event rate data from the first group to use for analysis
Group 1 Name False group1 Name of the first group
Group 1 Color False tab:red Color for the first group
Group 2 Population Activity Files False N/A Select population activity data from the second group to use for analysis
Group 2 Population Event Rate Files False N/A Select population event rate data from the second group to use for analysis
Group 2 Name False N/A Name of the second group
Group 2 Color False N/A Color for the second group
State Names True N/A Names of the state comparisons in the columns of the input files
State Colors False N/A Colors for the state comparisons
Modulation Colors False N/A Colors that represent up- and down-modulation respectively
Significance Threshold True 0.05 p-value threshold
Multiple Comparison Correction True N/A Method for correcting for multiple comparisons
Effect Size Method True N/A Method for calculating the effect size
Data Pairing False unpaired Indicates whether observations should be paired for statistical comparison.
Subject Matching Method False order Method for matching subjects between groups in paired analysis
Method to Compare States False auto Method for comparing states. 'Auto-detect' will automatically determine the method from the data. 'State vs Not State' and 'State vs Not Defined' both work with standard state comparison analysis. 'State vs Baseline' compares each state to a baseline state. 'Pairwise' compares states directly to each other.

Input Files

The inputs to this tool are population activity metric files generated by the Compare Neural Activity Across States tool. Event rate files are optional but must be provided for both groups if used in a two-group comparison.

Source Parameter File Type File Format
Group 1 Population Activity Files modulation_data csv
Group 1 Population Event Rate Files modulation_data csv
Group 2 Population Activity Files modulation_data csv
Group 2 Population Event Rate Files modulation_data csv

The input files have the following requirements:

  • Each group must contain at least two recording files (subjects) for meaningful statistical analysis
  • All input files must contain data for the same set of states with identical state names
  • State Comparison Compatibility: This tool is compatible with all state comparison methods from the upstream tool:
    • Baseline Comparisons: Each state compared to a specified baseline state
    • State vs Not-State: Each state compared to all time points not in that state
    • State vs Not-Defined: Each state compared to time points not belonging to any defined state
    • Pairwise Comparisons: Direct state-to-state comparisons (e.g., state1 vs state2) are supported and handled appropriately in the analysis pipeline
  • If Data Pairing is set to "paired" for two-group comparisons:
    • Both groups must have the same number of recording files
    • Files are matched between groups using the Subject Matching Method parameter
  • Event files, if provided, must correspond to the activity files in number and naming
  • Consistency Requirements: All input files must be generated with consistent parameters in the upstream tool:
    • Same state comparison method across all files
    • Same scaling method for trace activity and event rate data
    • Same state definitions and column naming
    • Same statistical parameters (recommended for comparable results)

Algorithm Description

The workflow combines data from multiple recordings, performs multi-level statistical analyses, and generates comprehensive outputs with visualizations.

graph TD A[Input Files Group 1] --> C[Combine Data Group 1]; B[Input Files Group 2] --> D[Combine Data Group 2]; C --> M[Merge Combined Data]; D --> M; M --> E[Cell Level Analysis
Linear Mixed Models]; M --> F[Subject Level Modulation Analysis
ANOVA on Cell Counts]; M --> G[Subject Level Activity Analysis
ANOVA on Mean Values]; E --> H[Statistical Comparisons
& Effect Sizes]; F --> H; G --> H; H --> I[Generate Outputs
& Visualizations];

The tool executes the following key steps:

  1. Data Integration: Combines files within each group, adds subject identifiers, and ensures consistent significance thresholds across recordings
  2. Multi-Level Statistical Analysis:
    • Cell-level: Linear Mixed Models account for hierarchical data structure (cells nested within subjects)
    • Subject-level: ANOVA on modulation counts and subject-averaged activity measures
  3. Post-hoc Testing: Pairwise comparisons with multiple comparison correction and effect size calculations

Statistical Methods

Linear Mixed Model (LMM)

Used for cell-level analysis of activity and event rates. LMMs handle hierarchical data (cells nested within subjects) and repeated measures (multiple states per subject). The models include fixed effects for experimental factors while accounting for random subject-to-subject variability.

Analysis of Variance (ANOVA)

Used for subject-level comparisons of modulation counts and averaged measures:

  • Repeated Measures ANOVA: For within-subject comparisons (single group) or paired group designs
  • Mixed ANOVA: For between-subject comparisons (unpaired group designs)

Post-hoc Pairwise Testing

Following significant omnibus tests, pairwise comparisons identify specific group or state differences:

  • Multiple comparison correction: User-selectable methods to control error rates
  • Effect size calculation: Standardized measures of practical significance

Modulation Analysis Table

The following table summarizes the statistical approaches used for subject-level modulation analysis based on experimental design:

Case Number of Groups Data Pairing Number of States Comparison Type Statistical Test Example Situation
Single Group 1 N/A multiple pairwise descriptive statistics only Single group of mice analyzed across locomotion states (mobile, immobile, grooming)
state vs baseline one-way ANOVA Single group comparing sleep states (REM, NREM) against wake baseline
state vs not state Single group comparing reward-seeking vs non-reward-seeking periods
Multiple Paired Groups 2 paired multiple pairwise paired t-tests Same animals recorded before/after drug treatment across multiple behavioral states
state vs baseline repeated measures ANOVA Within-subject drug vs vehicle comparison across states relative to baseline
state vs not state Same animals in different contexts comparing state-specific vs general activity
Multiple Unpaired Groups 2 unpaired multiple pairwise unpaired t-tests Separate cohorts of control vs knockout mice across behavioral states
state vs baseline mixed ANOVA Independent groups (e.g., male vs female) comparing states to baseline
state vs not state Different treatment groups comparing task-specific vs general activity
Special Cases 2 paired single group comparison paired t-tests Same animals comparing one specific state (e.g., freezing) before/after treatment
unpaired unpaired t-tests Independent groups comparing one behavioral state (e.g., control vs lesioned during exploration)

Outputs

Combined Group Data

Group Data Files

Combined data from all recordings within each group, saved as CSV files named by group.

Example Combined Data:

Name modulation scores in mobile p-values in mobile modulation in mobile mean activity in mobile modulation scores in immobile p-values in immobile modulation in immobile mean activity in immobile file Comparison subject_id total_cell_count
Cell_1 0.1847 0.0234 1 0.0923 -0.0623 0.3456 0 0.0756 mouse_01.csv trace_activity subject_1 156
Cell_2 -0.2156 0.0089 -1 0.0834 0.1789 0.0456 1 0.0891 mouse_01.csv trace_activity subject_1 156
Cell_3 0.0456 0.4567 0 0.0678 -0.0234 0.7890 0 0.0623 mouse_01.csv trace_activity subject_1 156
Cell_1 0.2134 0.0123 1 0.0945 -0.0891 0.2345 0 0.0712 mouse_02.csv trace_activity subject_2 142

Group Data Visualizations

  • Modulation Distribution: Proportion of cells showing increased, decreased, or unchanged activity for each state

    Modulation distribution showing proportion of cells with increased, decreased, or unchanged activity across behavioral states

  • Mean Activity/Event Rate: Distribution of average neural measures across experimental states

    Combined group data showing mean activity and event rate distributions across experimental states

Statistical Comparison Results

ANOVA/LMM Results

Comprehensive statistical test results from all analysis levels, saved as a CSV file.

Example Output:

Source p_value F_statistic df1 df2 stat_method Measure Comparison analysis_level coefficient std_error t_value significance_threshold multiple_correction data_pairing
group 0.738 0.147 1 2 mixed_anova activity trace_activity subject - - - 0.05 bonf unpaired
state 0.722 0.458 3 6 mixed_anova activity trace_activity subject - - - 0.05 bonf unpaired
Interaction 0.331 1.399 3 6 mixed_anova activity trace_activity subject - - - 0.05 bonf unpaired
Intercept - - 1 2 lmm activity trace_activity subject 0.258 - - 0.05 bonf unpaired
state[T.quad 1] 0.296 - 1 2 lmm activity trace_activity subject -0.621 0.594 -1.045 0.05 bonf unpaired
group[T.Vehicle] - - 1 2 lmm activity trace_activity subject 0.160 - - 0.05 bonf unpaired

Pairwise Comparison Results

Detailed results from post-hoc pairwise tests following significant omnibus tests, saved as a CSV file.

Example Output:

A B Contrast state T p-unc p-corr effect_size stat_method Measure Comparison analysis_level state_pairing group_pairing significance_threshold multiple_correction data_pairing
center quad 1 state - 0.009 0.994 1.0 0.004 paired_ttest activity trace_activity subject paired unpaired 0.05 bonf unpaired
quad 2 quad 4 state - 3.400 0.042 0.255 1.700 paired_ttest activity trace_activity subject paired unpaired 0.05 bonf unpaired
Drug Vehicle group across_states -0.384 0.738 - -0.192 unpaired_ttest activity trace_activity subject not_applicable unpaired 0.05 bonf unpaired
Drug Vehicle state * group center 0.659 0.578 1.0 0.329 unpaired_ttest activity trace_activity subject paired unpaired 0.05 bonf unpaired
center quad 1 center vs quad 1 - -1.414 0.293 - 1.414 unpaired_ttest up_modulation trace_activity subject not_applicable unpaired 0.05 bonf unpaired

Statistical Comparison Visualizations

  • Cell-Level State Analysis: Violin plots showing activity/event rate distributions across states, with statistical significance indicators

    Cell-level state comparison using Linear Mixed Models

  • Subject-Level Group Analysis: Scatter plots with subject-averaged measures, showing group differences across states with connecting lines for paired analyses

    Subject-level group comparison for activity measures

    Subject-level group comparison for event rate measures

  • Modulation Pattern Analysis: Proportion of modulated cells per subject across states and groups

    Modulation pattern comparison across groups and states