Additional file 1 of INFLECT: an R-package for cytometry cluster evaluation using marker modality
Creators: name: Verhoeff, Jan
name: Abeln, Sanne
name: Garcia-Vallejo, Juan J.
Publisher: name: figshare
Year: 2022
Subjects: subject: Biophysics
subject: Space Science
subject: Medicine
subject: Cell Biology
subject: Biotechnology
subject: Immunology
subject: FOS: Clinical medicine
subject: 80699 Information Systems not elsewhere classified
subject: FOS: Computer and information sciences
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: Hematology
subject: 110309 Infectious Diseases
subject: FOS: Health sciences

description: Additional file 1: Supplementary Fig. S1: Marker performance for Levine32 INFLECT. Marker performance diagnostic plots for remaining markers of Levine32. On the x-axis the amount of FlowSOM-metaclusters, and on the y-axis the amount of metaclusters that pass the unimodality and interquartile range checks as a percentage of total metaclusters for the listed marker; Supplementary Fig. S2: All 15 repeats of 90% subsamples of Levine32. A L-function applied to the sigmoidal curve fitted to set $${U}_{i}$$ U i . B L-function applied to the set $${U}_{i}$$ U i itself. Resulting inflection points for both panels denoted with vertical dotted line; Supplementary Fig. S3: With fewer datapoints in Unimodality set $${U}_{i}$$ U i INFLECT still produces a stable fitted sigmoidal curve. INFLECT diagnostic plots with different sizes of set $${U}_{i}$$ U i , from 81 calculations down to 9 datapoints. Resulting inflection point is stable around 40 metaclusters. The values of the Unimodality set $${U}_{i}$$ U i are plotted on the y-axis versus the number of metaclusters assessed on the x-axis; Supplementary Fig. S4: Traditional (meta)clustering evaluations perform poorly on Levine32 dataset. A Davies Bouldin (DB) Index on the y-axis versus the amount of FlowSOM-metaclusters. A lower score indicates better clustering. In this case the DB index does not form a plateau, making interpretation difficult. B Calinski-Harabasz (CH) Index on the y-axis versus the amount of FlowSOM-metaclusters. A higher score indicates better clustering. CH index drops with increasing FlowSOM-metaclusters, making CH index less suitable for evaluation. C Diagnostic plot of ConsensusClusterPlus on Levine32 dataset. Relative change in area under cumulative distribution function (CDF) curve compared to $$k-1$$ k - 1 clusters. FlowSOM implementation of ConsensusClusterPlus indicates 14 metaclusters as optimal $$k$$ k . D tSNE embedding of Levine32, colored for the 14 ConsensusClusterPlus metaclusters. Multiple islands (CD4 T cells, CD8 T cells and monocytes) are grouped into 1 large metacluster; Supplementary Fig. S5: INFLECT reveals heterogeneity within labeled phenotypes of the replicate PBMC dataset from Bagwell et al. A tSNE embedding of 10,000 events from Bagwell dataset. Colored for the 26 manually gated phenotypes, plus light-pink for unlabeled cells and yellow for events with 2 or more labels. B Histograms of selected markers highlighting heterogeneity within the Early NK cells. X-axis denote Arcsinh(x/5) transformed expression values. C Same tSNE embedding of 10,000 events as in A, here colored for the 52 INFLECT metaclusters. D Histograms of selected markers highlighting the difference between metacluster 24 and 29, which correspond to the 2 different cell populations within the Early NK label. X-axis denote Arcsinh(x/5) transformed expression values. Metacluster 29 is a smaller CD56bright CD16dim population, whereas metacluster 24 has high levels of CD16 and intermediate CD56 expression; Supplementary Fig. S6: INFLECT applied to bone marrow dataset from Kimmey et al. reveals high degree of heterogeneity. A tSNE embedding of 10,000 events from KimmeyBM dataset. Colors denote the labels identified by the authors. Immature B cells are highlighted in rectangle. B Histograms of selected markers highlighting heterogeneity within Immature B cells. X-axis denote Arcsinh(x/5) transformed expression values. C Same tSNE embedding as in A, now colored for the 74 INFLECT metaclusters. Highlighted in the rectangle are the 4 different metaclusters of the Immature B cells, which were separated into metaclusters 38, 41, 49, and 71. D Histograms of selected markers highlighting heterogeneity between the 4 metaclusters corresponding to the Immature B cell label. X-axis denote Arcsinh(x/5) transformed expression values. Metacluster 71 has a CD38+ phenotype, metacluster 41 has a IgMdim phenotype and metaclusters 38 and 49 are differentiated in expression levels of CD24 and to a lesser extent CD45RA; Supplementary Fig. S7: INFLECT algorithm applied to PBMC dataset from Kimmey et al. captures underlying heterogeneity of labeled populations. A tSNE embedding of 10,000 events from KimmeyPBMC dataset. Colors denote the labels identified by the authors. Rectangle highlights the cDCs label. B Histograms of selected markers highlighting heterogeneity within the cDCs label. X-axis denote Arcsinh(x/5) transformed expression values. C Same tSNE embedding as in A, here colored for the 41 INFLECT metaclusters. In the rectangle the metaclusters 7 and 15 are highlighted, which correspond to the cDCs label. D Histograms of selected markers highlighting heterogeneity between metaclusters 7 and 15. Main difference between metaclusters 7 and 15 is the level of CD11c expression.