Univariate Report: Differential Abundance based on ‘cond’ column

demo chkv

Contrast and number of results per methods:

Acute vs Conv : edgeR = 8, VOOM = 10, GLMM = 0, In common = 0

Author

Affiliation

Eugénie Lohmann

CRCM (CiBi Group)

Published

April 29, 2024

Report Parameters

Input parameters
	Sélection
adcode	FALSE
analyse_type	DA
annotation_column	CellSubset
batch	acqdate
cluster_id	cluster_id
col_block	patient_id
conditions	cond
conditions_reference	Acute
fc	1
log_bar	FALSE
min_cells	10
min_samples	22
path	/root/analycyte-projects/demo_chkv/0823ECE7
p_value	0.05
project	demo chkv
rds	dataFE.rds
show_parameters	TRUE
show_summary	TRUE
show_heatmap_on_off	TRUE
sorted	TRUE
transform	FALSE
plot_legends	Gender
contrast	Conv

This document has been formated using knitr¹ and quarto².

1 Features summary

Sample table with information on conditions (cond), batch (acqdate), ncells (Number of Cells)

percent.cluster : Percentage of clusters with cells (> 10 cells) in this fcs. see 1

1.0.1 Number of fcs per conditions

Condition-based distribution
Acute	Conv
43	43

Information on metaclusters is grouped in this table with the number of cells and the corresponding description.

n_cells : Number of cells in the cluster.
percent.n_cells : Percentage of total cells in the cluster.
percent.fcs : Percentage of samples with cells (> 10 cells) in this cluster. see 1

A total of 37 markers are included in this analysis report.

Type markers (7): CD19, CD38, CD4, CD45RA, CD54, CD8, CXCR5.

State markers (30): CCR4, CCR5, CCR6, CCR7, CD11b, CD11c, CD123, CD127, CD14, CD141, CD16, CD161, CD1c, CD20, CD209, CD25, CD27, CD3, CD40, CD45, CD56, CD57, CD66b, CD80, CD86, CHIKV, CX3CR1, CXCR3, HLADR, PD1.

2 Presence / Absence data

With a minimum of 10 per cluster.

3 Biological Question

The report is based on the analysis question: cond (parameters: Acute, Conv)

The reference is : Acute

The contrast tested are : Acute vs Conv

cond = Acute (43 fcs ):

1 1758 Acute, 3 1760 Acute, 5 1773 Acute, 7 1785 Acute, 9 1790 Acute, 11 1793 Acute, 13 1794 Acute, 15 1800 Acute, 17 1802 Acute, 19 1822 Acute, 21 1823 Acute, 23 1824 Acute, 25 1857 Acute, 27 1828 Acute, 29 1829 Acute, 31 1838 Acute, 33 1839 Acute, 35 1842 Acute, 37 1844 Acute, 39 1847 Acute, 41 1862 Acute, 43 1863 Acute, 45 1864 Acute, 47 1878 Acute, 49 1879 Acute, 51 1880 Acute, 53 1882 Acute, 55 1885 Acute, 57 1886 Acute, 59 1889 Acute, 61 1890 Acute, 63 1891 Acute, 65 1897 Acute, 67 1910 Acute, 69 1912 Acute, 71 1914 Acute, 73 1918 Acute, 75 1920 Acute, 77 1924 Acute, 79 1937 Acute, 81 1925 Acute, 83 1938 Acute, 85 1948 Acute

cond = Conv (43 fcs ):

2 1758 Conv, 4 1760 Conv, 6 1773 Conv, 8 1785 Conv, 10 1790 Conv, 12 1793 Conv, 14 1794 Conv, 16 1800 Conv, 18 1802 Conv, 20 1822 Conv, 22 1823 Conv, 24 1824 Conv, 26 1857 Conv, 28 1828 Conv, 30 1829 Conv, 32 1838 Conv, 34 1839 Conv, 36 1842 Conv, 38 1844 Conv, 40 1847 Conv, 42 1862 Conv, 44 1863 Conv, 46 1864 Conv, 48 1878 Conv, 50 1879 Conv, 52 1880 Conv, 54 1882 Conv, 56 1885 Conv, 58 1886 Conv, 60 1889 Conv, 62 1890 Conv, 64 1891 Conv, 66 1897 Conv, 68 1910 Conv, 70 1912 Conv, 72 1914 Conv, 74 1918 Conv, 76 1920 Conv, 78 1924 Conv, 80 1937 Conv, 82 1925 Conv, 84 1938 Conv, 86 1948 Conv

4 Materials and methods

Differentially Abundant Clusters (DAC) are especially interesting when you want to identify clusters having different cell abundances between two biological conditions.

The following report provides a turnkey analysis using diffcyt³ of three DAC softwares packages, differential abundance tests for cell populations will be calculated using edgeR⁴ (testDA_edgeR) , VOOM⁵ (testDA_voom) and GLMM⁶ (testDA_GLMM) algorithms.

Design and parameters between analysis
	edgeR 5	VOOM 6	GLMM 7
Design	cond, acqdate	cond, acqdate
Block id		patient_id
Random
Fixed			cond
p-adjust	0.05	0.05	0.05
log2FC	1	1	none
version

See Result table 8 for a comparision matrix.

See Union Graphics 9 for associated vizualisations.

5 edgeR

Sorted by ajusted p-adjust.
Clusters without enough cell counts per patients are excluded_edger.

Warning

Are excluded_edger the combinaison : T Cell_unassigned, Debris without enough cell counts. (6.8965517% of the queried).

List of the sorted significant edger clusters

Conv: Dendritic Cell (Type 2, CD1c+), Monocyte (CD14+ CD16+), Plasmacytoid Dendritic Cell, B Cell (Memory), CD4+ T Cell (Effector Memory), CM- HLADR+_unassigned, Dendritic Cell (Type 1, CD141+), Basophil

log2(Fold Change) in x-axis.
-log10(adjusted p-adjust) in y-axis.
The number of cells associated with the cluster is indicated by the size of the dot.
“significant_edger” DAC are highlighted in red color; associated cuttofs are a combinaison of :
- absolute log2(FC) > 1.
- adjusted pvalue cutoff : 0.05.

Number of cells per cluster associated with low adjusted p-adjusts.

mean % of total cells in x-axis.
-log10(adjusted p-adjust) in y-axis.
The number of cells associated with the cluster is indicated by the size of the dot.
“significant_edger” DAC are highlighted in red color; associated cuttofs are a combinaison of :
- mean % of total cells > 1.
- adjusted pvalue cutoff : 0.05.

5.1 graphics

Annotation Guide:

****: 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)

Acute vs Conv

5.1.1 volcano and abundance plot

Figure 2: Volcano and Abundance Plot DA edgeR Acute vs Conv

5.1.2 violin plot

Figure 3: Violin Plot DA edgeR Acute vs Conv

6 VOOM

Sorted by ajusted p-adjust.
Clusters without enough cell counts per patients are excluded_voom.

Warning

Are excluded_voom the combinaison : T Cell_unassigned, Debris without enough cell counts. (6.8965517% of the queried).

List of the sorted significant VOOM clusters

Conv: Dendritic Cell (Type 2, CD1c+), CD8+ T Cell (Naive), B Cell (Memory), CD4+ T Cell (Effector Memory), Plasmacytoid Dendritic Cell, Monocyte (CD14+ CD16+), CD4+ CD8+ T Cell, Basophil, CM- HLADR+_unassigned, Dendritic Cell (Type 1, CD141+)

log2(Fold Change) in x-axis.
-log10(adjusted p-adjust) in y-axis.
The number of cells associated with the cluster is indicated by the size of the dot.
“significant_VOOM” DAC are highlighted in red color; associated cuttofs are a combinaison of :
- absolute log2(FC) > 1.
- adjusted pvalue cutoff : 0.05.

Number of cells per cluster associated with low adjusted p-adjusts.

mean % of total cells in x-axis.
-log10(adjusted p-adjust) in y-axis.
The number of cells associated with the cluster is indicated by the size of the dot.
“significant_VOOM” DAC are highlighted in red color; associated cuttofs are a combinaison of :
- mean % of total cells > 1.
- adjusted pvalue cutoff : 0.05.

Annotation Guide:

****: 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)

6.1 graphics

Acute vs Conv

6.1.1 volcano and abundance plot

Figure 4: Volcano and Abundance Plot DA VOOM Acute vs Conv

6.1.2 violin plot

Figure 5: Violin Plot DA VOOM Acute vs Conv

7 GLMM

Sorted by ajusted p-adjust.
Clusters without enough cell counts per patients are excluded_glmm.

Warning

Are excluded_glmm the combinaison : B Cell (CD27-), B Cell (Memory), B Cell (Plasmablast), B Cell_unassigned, Basophil, CD4+ CD8+ T Cell, CD4+ T Cell (Central Memory), CD4+ T Cell (Effector Memory), CD4+ T Cell (EMRA), CD4+ T Cell (Naive), CD4+ T Cell (Treg), CD4- CD8- T Cell, CD8+ T Cell (Central Memory), CD8+ T Cell (Effector Memory), CD8+ T Cell (EMRA), CD8+ T Cell (Naive), CM- HLADR+_unassigned, CM-_unassigned, Dendritic Cell (Type 1, CD141+), Dendritic Cell (Type 2, CD1c+), Monocyte (CD14+ CD16+), Monocyte (CD14+ CD16-), Monocyte (CD14- CD16+), NK Cell (CD56+ CD16+), NK Cell (CD56+ CD16-), Plasmacytoid Dendritic Cell, Root_unassigned, T Cell_unassigned, Debris without enough cell counts. (100% of the queried).

List of the sorted significant glmm clusters

Conv:

Number of cells per cluster associated with low adjusted p-adjusts.

mean % of total cells in x-axis.
-log10(adjusted p-adjust) in y-axis.
The number of cells associated with the cluster is indicated by the size of the dot.
“significant_glmm” DAC are highlighted in red color; associated cuttofs are a combinaison of :
- mean % of total cells > 1.
- adjusted pvalue cutoff : 0.05.

Annotation Guide:

****: 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)

7.1 graphics

Acute vs Conv

7.1.1 volcano and abundance plot

Figure 6: Volcano and Abundance Plot DA glmm Acute vs Conv

7.1.2 violin plot

NULL

8 Comparison of results per method

Acute vs Conv

8.0.1 Result by methods on Acute vs Conv

Union list cluster result

Conv: Dendritic Cell (Type 2, CD1c+), Monocyte (CD14+ CD16+), Plasmacytoid Dendritic Cell, B Cell (Memory), CD4+ T Cell (Effector Memory), CM- HLADR+_unassigned, Basophil, Dendritic Cell (Type 1, CD141+)

9 Union Graphics

9.1 Heatmaps on union clusters

Exploration of the variability of accross resulting DACs.

Visualize samples organized according to hierarchical grouping or by condition on DAC.

Percentage heatmap with samples in columns and clusters in rows.

Clusters and samples are grouped using hierarchical clustering.

Group abundance is shown in the right-hand barplot with log values.

Acute vs Conv

9.1.1 Arcsinh Transformed Frequencies

Figure 7: Consensus cluster arcsinh transformed frequencies Acute vs Conv

Figure 8: Consensus clusters abundance with associated mfi Acute vs Conv

Glossary

counts: Raw count abundances.
perCellCounts: Frequencies abundances.
perCellCountsNorm: Argsinh frequencies / 0.03 then centered by the mean per clusters.

R session info

R version 4.3.3 (2024-02-29)

R version 4.3.3 (2024-02-29)

Platform: x86_64-pc-linux-gnu (64-bit)

locale: LC_CTYPE=en_US.UTF-8, LC_NUMERIC=C, LC_TIME=en_US.UTF-8, LC_COLLATE=en_US.UTF-8, LC_MONETARY=en_US.UTF-8, LC_MESSAGES=en_US.UTF-8, LC_PAPER=en_US.UTF-8, LC_NAME=C, LC_ADDRESS=C, LC_TELEPHONE=C, LC_MEASUREMENT=en_US.UTF-8 and LC_IDENTIFICATION=C

attached base packages: grid, stats4, stats, graphics, grDevices, utils, datasets, methods and base

other attached packages: knitr(v.1.46), patchwork(v.1.2.0), htmlwidgets(v.1.6.4), diffcyt(v.1.22.1), checkmate(v.2.3.1), ggiraph(v.0.8.9), reactable(v.0.4.4), plotly(v.4.10.4), tibble(v.3.2.1), kableExtra(v.1.4.0), ggrepel(v.0.9.5), ggplot2(v.3.5.1), tidyr(v.1.3.1), data.table(v.1.15.4), colorRamp2(v.0.1.0), randomcoloR(v.1.1.0.1), RColorBrewer(v.1.1-3), ComplexHeatmap(v.2.18.0), SummarizedExperiment(v.1.32.0), Biobase(v.2.62.0), GenomicRanges(v.1.54.1), GenomeInfoDb(v.1.38.8), IRanges(v.2.36.0), S4Vectors(v.0.40.2), BiocGenerics(v.0.48.1), MatrixGenerics(v.1.14.0), matrixStats(v.1.3.0), dplyr(v.1.1.4), readr(v.2.1.5) and analycyte.utils(v.3.0.11.2)

loaded via a namespace (and not attached): rstudioapi(v.0.16.0), jsonlite(v.1.8.8), shape(v.1.4.6.1), magrittr(v.2.0.3), TH.data(v.1.1-2), estimability(v.1.5), nloptr(v.2.0.3), farver(v.2.1.1), rmarkdown(v.2.26), GlobalOptions(v.0.1.2), zlibbioc(v.1.48.2), vctrs(v.0.6.5), minqa(v.1.2.6), Cairo(v.1.6-2), RCurl(v.1.98-1.14), rstatix(v.0.7.2), htmltools(v.0.5.8), S4Arrays(v.1.2.1), curl(v.5.2.1), broom(v.1.0.5), SparseArray(v.1.2.4), plyr(v.1.8.9), sandwich(v.3.1-0), emmeans(v.1.10.1), zoo(v.1.8-12), lubridate(v.1.9.3), uuid(v.1.2-0), igraph(v.2.0.3), lifecycle(v.1.0.4), iterators(v.1.0.14), pkgconfig(v.2.0.3), Matrix(v.1.6-5), R6(v.2.5.1), fastmap(v.1.1.1), GenomeInfoDbData(v.1.2.11), clue(v.0.3-65), digest(v.0.6.35), ggnewscale(v.0.4.10), colorspace(v.2.1-0), crosstalk(v.1.2.1), ggpubr(v.0.6.0), labeling(v.0.4.3), cytolib(v.2.14.1), fansi(v.1.0.6), colorRamps(v.2.3.4), timechange(v.0.3.0), polyclip(v.1.10-6), httr(v.1.4.7), abind(v.1.4-5), compiler(v.4.3.3), withr(v.3.0.0), doParallel(v.1.0.17), pander(v.0.6.5), ConsensusClusterPlus(v.1.66.0), backports(v.1.4.1), carData(v.3.0-5), highr(v.0.10), ggforce(v.0.4.2), ggsignif(v.0.6.4), MASS(v.7.3-60.0.1), DelayedArray(v.0.28.0), rjson(v.0.2.21), scatterplot3d(v.0.3-44), FlowSOM(v.2.10.0), flashClust(v.1.01-2), tools(v.4.3.3), FactoMineR(v.2.11), glue(v.1.7.0), nlme(v.3.1-164), Rtsne(v.0.17), reshape2(v.1.4.4), cluster(v.2.1.6), generics(v.0.1.3), gtable(v.0.3.4), tzdb(v.0.4.0), hms(v.1.1.3), car(v.3.1-2), xml2(v.1.3.6), utf8(v.1.2.4), XVector(v.0.42.0), foreach(v.1.5.2), pillar(v.1.9.0), stringr(v.1.5.1), limma(v.3.58.1), ggprism(v.1.0.5), circlize(v.0.4.16), splines(v.4.3.3), flowCore(v.2.14.2), tweenr(v.2.0.3), lattice(v.0.22-5), survival(v.3.5-8), RProtoBufLib(v.2.14.1), tidyselect(v.1.2.1), locfit(v.1.5-9.9), V8(v.4.4.2), edgeR(v.4.0.16), svglite(v.2.1.3), xfun(v.0.43), statmod(v.1.5.0), DT(v.0.33), stringi(v.1.8.3), boot(v.1.3-29), lazyeval(v.0.2.2), yaml(v.2.3.8), evaluate(v.0.23), codetools(v.0.2-19), multcompView(v.0.1-10), cli(v.3.6.2), xtable(v.1.8-4), systemfonts(v.1.0.6), munsell(v.0.5.0), jquerylib(v.0.1.4), Rcpp(v.1.0.12), png(v.0.1-8), XML(v.3.99-0.16.1), parallel(v.4.3.3), leaps(v.3.1), bitops(v.1.0-7), lme4(v.1.1-35.3), viridisLite(v.0.4.2), mvtnorm(v.1.2-4), scales(v.1.3.0), purrr(v.1.0.2), crayon(v.1.5.2), GetoptLong(v.1.0.5), rlang(v.1.1.3) and multcomp(v.1.4-25)

References

Xie, Y. Dynamic documents with R and knitr. (Chapman; Hall/CRC, 2015).

Allaire, J. Quarto: R interface to ’quarto’ markdown publishing system. (2022).

Weber, L. M. Diffcyt: Differential discovery in high-dimensional cytometry via high-resolution clustering. (2023).

Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: A bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47 (2015).

Nowicka, M. et al. CyTOF workflow: Differential discovery in high-throughput high-dimensional cytometry datasets. F1000Research 6, 748 (2017).

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