14  DS - analyse intensity of clusters - univar

Supervised analysis aims at identifying the differences between experimental groups. Differential state (DS) identifies clusters-marker combinations where marker intensity changes.

14.0.1 DS Tables Columns

Diffcyt methods for differential state

The output tables are derived from methods within the diffcyt Weber et al. (2019) package.

limma outputs is generated by diffcyt-DS-limma Ritchie et al. (2015) LMM Nowicka et al. (2017)

Column names of Differential State tables output

cluster_id	Identifier for the cell cluster being analyzed. This, combined with marker_id, specifies the unique cell population and marker pair for which the MFI is compared.
marker_id	Identifier for the specific marker within the cluster. Each cluster_id and marker_id pair represents a unique data line in the analysis, indicating differences in median MFI between groups for a specific marker in a given cluster.
reference	The reference group or condition used as a baseline for comparison in the differential analysis.
contrast	The contrast group or condition that is compared against the reference to ascertain differences in MFI.
logFC	Log2 fold change of the median MFI from the contrast group relative to the reference group. Positive values indicate a higher median MFI in the contrast group, whereas negative values suggest a higher median MFI in the reference group.
explicit FC	The explicit fold change, showing the actual change in median MFI between the contrast and reference groups. This can be provided directly or calculated as the antilog of logFC.
p_val	The raw p-value from the statistical test, assessing the significance of the difference in median MFI between the contrast and reference groups. A lower p-value indicates a more statistically significant difference.
p_adj	The adjusted p-value which accounts for multiple testing corrections, offering a more stringent significance assessment compared to the raw p-value.
FDR	The False Discovery Rate, an adjusted p-value that corrects for multiple comparisons, representing the expected proportion of type I errors (false positives) among significant results. FDR assumptions Noting that while FDR is theoretically a complex correction, it is used here as a sorting tool under the assumption of similar study group sizes.
AveExpr	The average expression level of the marker across all samples, giving a baseline for the MFI that can be used for comparison.
t	The t-statistic value from the differential analysis, measuring the size of the difference in median MFI relative to the variability among samples. A larger absolute t-value indicates a more significant difference.
B	The log-odds that the difference in median MFI for the marker within the cluster is significant across the compared groups. A higher B value suggests stronger evidence for differential MFI.

14.0.2 Interactive volcano plot

Interactive volcano plot

This interactive volcano plot is designed to visualize differential state comparisons (DS) across clusters. It plots the log2(Fold Change) on the x-axis against the negative log10 of the adjusted p-value on the y-axis. The size of each dot on the plot represents the number of cells associated with each cluster*marker.

Significant differences are emphasized with red dashed lines, and the thresholds for these are determined by:

An absolute log2(Fold Change) greater than a specific cutoff (<cutoff FC>), An adjusted p-value below a certain threshold (<cutoff FDR>). Users can interact with the plot by hovering their mouse over points to overlay additional annotations.

14.0.3 Violin plot

violin plot

This violin plot is designed to illustrate the scaled mfi of marker*cluster across different experimental groups, with dynamic customization options that adjust to user-defined conditions (<condition>) and batch settings (<batch>). The plot is marked by a distinctive red dashed line at the zero abscissa.

Each violin’s color corresponds to a specific condition, facilitating quick visual comparisons among groups. The shape of the data points within each violin can vary, reflecting the specified batch conditions, which helps in assessing the impact of batching on the cell abundance.

Significance annotations are embedded directly within the plot, categorizing the statistical significance of differences observed between groups based on the adjusted p-values (FDR). The levels of significance are visually encoded as follows:

• ****: Highly significant (FDR ≤ 0.0001)
• ***: Very significant (0.0001 < FDR ≤ 0.001)
• **: Significant (0.001 < FDR ≤ 0.01)
• *: Moderately significant (0.01 < FDR ≤ 0.05)
• .: Suggestive (0.05 < FDR ≤ 0.1)

14.0.4 MFI Marker*Cluster Heatmap

MFI Marker*Cluster Heatmap

Heatmaps that show the significant markers*clusters with the lowest FDR values.

Nowicka, Malgorzata, Carsten Krieg, Helena L Crowell, Lukas M Weber, Felix J Hartmann, Silvia Guglietta, Burkhard Becher, Mitchell P Levesque, and Mark D Robinson. 2017. “CyTOF Workflow: Differential Discovery in High-Throughput High-Dimensional Cytometry Datasets.” F1000Research 6: 748. https://doi.org/10.12688/f1000research.11622.3.

Ritchie, Matthew E, Belinda Phipson, Di Wu, Yifang Hu, Charity W Law, Wei Shi, and Gordon K Smyth. 2015. “limma Powers Differential Expression Analyses for RNA-Sequencing and Microarray Studies.” Nucleic Acids Research 43 (7): e47. https://doi.org/10.1093/nar/gkv007.

Weber, Lukas M., Malgorzata Nowicka, Charlotte Soneson, and Mark D. Robinson. 2019. “Diffcyt: Differential Discovery in High-Dimensional Cytometry via High-Resolution Clustering.” Communications Biology 2 (1): 183. https://doi.org/10.1038/s42003-019-0415-5.