CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity
- Department of Applied Mathematics and Statistics, Johns Hopkins University, United States
- Departments of Neurology and Neuroscience, Johns Hopkins University, United States
- Department of Natural Sciences, University of Maryland Eastern Shore, United States
- Center for Imaging Science, Johns Hopkins University, United States
- Institute for Genome Sciences, University of Maryland School of Medicine, United States
Figures
Figure 1 with 4 supplements
Benchmarking performance of CellCover and competing methods on the CBMC dataset and blood cell type mapping.
(A) Balanced accuracy versus the size of the marker panel for all methods, with SD across random seeds shown as shaded areas. CellCover implemented with weights calculated from log normalized or binary data are both shown. (B) Proportion of intersection between each method’s global marker panel and the CellCover (log-normalized) reference panel. (C) Proportion of redundant marker genes selected for multiple cell classes versus panel size for CellCover and DE, also including SD across random seeds. (D) Sankey diagram of the mapping from source blood cell types (Hao et al., 2021) to target blood cell types (Stephenson et al., 2021). Original authors’ cell type labels are used: HSC = hematopoietic stem cells, DC = dendritic cells, B=B cells, T=T cells, NK = natural killer cells, eryth +RBC = red blood cells, mono = monocytes, ilc = innate lymphoid cells, prof +prolif = proliferating.
Figure 1—figure supplement 1
Number of redundant marker genes versus panel size for CellCover and DE.
Figure 1—figure supplement 2
Sankey diagram depicting cell type mapping from the Hao PBMC CITE-seq dataset to the Tabula Sapiens blood dataset (Smart-seq protocol).
Figure 1—figure supplement 3
Sankey diagram depicting cell type mapping from the Hao PBMC CITE-seq dataset to the Tabula Sapiens blood dataset (10x5’ protocol).
Figure 1—figure supplement 4
Sankey diagram depicting cell type mapping from the Hao PBMC CITE-seq dataset to the Tabula Sapiens blood dataset (10x3’ protocol).
Figure 2 with 4 supplements
CellCover marker gene panels in the developing mammalian neocortex.
(A) Dot plot of empirical conditional expression probabilities of CellCover marker panels of each cell age. The marker genes of each cell age are grouped along the horizontal axis and sorted by expression frequency in the cell class of interest. The color of the dots represents the expression probability of markers conditioned on time since a cell’s terminal division, i.e., the proportion of cells within a class expressing the marker gene. For this analysis, cells of each time point were pooled across embryonic ages (E12–15). The panel is obtained using the CellCover with and . RG = radial glia, NB = neuroblast, NR = neuron. (B) Transfer of marker gene panels from the Telley data (Telley et al., 2019) to a second mouse neocortex dataset shows consistent identification of the primary cell types in mouse neurogenesis. Left: UMAP representations of cells from the dorsal forebrain excitatory lineage in the La Manno atlas of mouse brain development (La Manno et al., 2021), colored by cell labels assigned by the original authors. This is followed by the same UMAPs, now showing the proportion of gene panels derived from the Telley dataset, labeled 1 H, 24 H, and 96 H, expressed at non-zero levels in each individual cell. These last three plots illustrate the transfer of marker gene panels derived from the Telley data to the La Manno data. (C) Box plots of these same proportions broken down by cell-type labels provided by the original authors. (D) Transfer of marker gene panels from the Telley data in mouse to data in the developing human neocortex from Polioudakis et al., 2019, shows the identification of conserved cell types in neurogenesis across mammalian species. Left: tSNE map of cells from the dorsal forebrain excitatory lineage in the Polioudakis data. This is followed by the same tSNE maps, now showing the transfer of marker gene panels derived from progenitors labeled 1, 24, and 96 hr after terminal cell division in the Telley data. The map is colored by the proportion of each gene panel that is expressed at non-zero levels in each individual cell of the human Polioudakis dataset. Original author cell labels are used: RG = radial glia, v=ventricular, o=outer, Pg = cycling progenitor, S=in S phase, G2M=in G2M phase, IP = intermediate progenitor, Ex = excitatory neuron, N=new migrating, M=maturing, Dp = deep layer, U=upper layer. (E) Box plots of these same proportions broken down by cell type labels and microdissection information provided by the original authors. The final two boxes indicate expression in neuronal subtypes segregated by physical location: germinal zone (GZ) or cortical plate (CP) microdissection.
Figure 2—figure supplement 1
Dot plot of CellCover marker gene panels for three cell ages (1, 24, and 96 hr) at embryonic day E12 in mouse neocortical development.
The marker genes of each cell age at its corresponding embryonic day are grouped along the horizontal axis and sorted by expression frequency in the cell class of interest. The color of the dots represents the expression probability of covering markers conditioned on time since a cell’s terminal division. The panel is obtained using the CellCover with and .
Figure 2—figure supplement 2
Dot plot of CellCover marker gene panels for three cell ages (1, 24, and 96 hr) at embryonic day E13 in mouse neocortical development.
The marker genes of each cell age at its corresponding embryonic day are grouped along the horizontal axis and sorted by expression frequency in the cell class of interest. The color of the dots represents the expression probability of covering markers conditioned on time since a cell’s terminal division. The panel is obtained using the CellCover with and .
Figure 2—figure supplement 3
Dot plot of CellCover marker gene panels for three cell ages (1, 24, and 96 hr) at embryonic day E14 in mouse neocortical development.
The marker genes of each cell age at its corresponding embryonic day are grouped along the horizontal axis and sorted by expression frequency in the cell class of interest. The color of the dots represents the expression probability of covering markers conditioned on time since a cell’s terminal division. The panel is obtained using the CellCover with and .
Figure 2—figure supplement 4
Dot plot of CellCover marker gene panels for three cell ages (1, 24, and 96 hr) at embryonic day E15 in mouse neocortical development.
The marker genes of each cell age at its corresponding embryonic day are grouped along the horizontal axis and sorted by expression frequency in the cell class of interest. The color of the dots represents the expression probability of covering markers conditioned on time since a cell’s terminal division. The panel is obtained using the CellCover with and .
Figure 3
Expression of 1 H, 24 H, and 96 H CellCover marker gene panels across development in bulk human and microdissected macaque neocortical tissue.
(A) Transfer of CellCover marker gene panels from the Telley data in mouse (Telley et al., 2019) into bulk RNA-seq from human fetal cortical tissue (Jaffe et al., 2018). The panel is obtained using the CellCover with and. The transferred values of gene panels were assessed as the sum of panel gene expression levels in each individual sample divided by the maximum sum observed in the samples. (B) Transfer of marker gene panels (Appendix 4—table 1d: , nested expanded from ) from the Telley data in mouse into microarray data from laser microdissected regions of the developing macaque neocortex (Bakken et al., 2016). Nonlinear fits in 1 H, 24 H, and 96 H panels are to VZ, iSVZ, and Ctx data, respectively. X-axis ages are expressed as embryonic (E) days after conception and months (mo) after birth. Transferred marker gene panel levels were calculated as in panel A. VZ = ventricular zone, iSVZ = inner subventricular zone, oSVZ = outer subventricular zone, subP = subplate, CP = cortical plate, Ctx = cortex.
Figure 4
Expression of the 12 CellCover gene panels from the Telley data across development in the fetal neocortex of the mouse and human.
(A) Transfer of Telley gene panels ( and ) into the radial glia (left panel), neuroblasts (center panel), and neurons (right panel) from the developing mouse brain atlas (La Manno et al., 2021). In all cases, covering rates of transferred panels from 1 H cells are depicted in blue, 24 H in green, and 96 H in red. Transferred levels were calculated as the proportion of cells of each type expressing more than three marker genes in the gene panel. (B) Transfer of the 12 gene panels ( and nested expanded from ) into bulk RNA-seq data from the human fetal cortex (Jaffe et al., 2018). Transferred values of gene panels were assessed as the sum of gene panel expression levels in each individual sample divided by the maximum sum observed in the samples (as in Figure 3A).
Figure 5 with 1 supplement
Expression of the 12 CellCover gene panels from the Telley data across development in the fetal and early postnatal neocortex of the macaque and human.
The CellCover marker panels are obtained at and nested expanded from . (A) Transfer of the 12 CellCover panels into microarray data from microdissected regions of the developing macaque neocortex (Bakken et al., 2016). X-axis ages are expressed as embryonic (E) days after conception and months (mo) after birth. Transferred gene panel levels were calculated as in Figure 3. VZ = ventricular zone, iSVZ = inner subventricular zone, oSVZ = outer subventricular zone, subP = subplate, CP = cortical plate, Ctx = cortex. (B) Repeated transfer of the 96 H gene panels into the microdissected macaque data, using additional labeling of dissections by cortical layer. (C) Repeated transfer of the 96 H gene panels into the human cortex data (Jaffe et al., 2018), showing additional late fetal and early postnatal samples in postnatal development.
Figure 5—figure supplement 1
Transfer of Telley within 1 H markers to radial glia, neuroblast, and glioblast cell populations in La Manno Dataset.
We use the box plot to show the distribution of the proportion of marker genes expressed in each of the three cell populations. For example, the first (blue) box plot in the top left panel shows the distribution of the proportion of E12.1H marker genes expressed in the radial glia cells in the La Manno dataset. These data clearly show the shift of mouse neocortical progenitors from neurogenic to gliogenic potential from E12-E15.
Figure 6 with 3 supplements
Expression of gliogenic and oRG marker gene panels across developmental time in human, macaque, and mouse neural progenitor cells.
CellCover () was used to define marker gene panels from scRNA-seq of sorted cell types of the developing human telencephalon (Liu et al., 2023). Here, the expression of marker gene panels from gliogenic precursor cells and outer radial glial (oRG) cells is examined in additional scRNA-seq data from progenitor cells of the developing (A) human (Trevino et al., 2021), (B) macaque (Micali et al., 2023), and (C) mouse (Di Bella et al., 2021) neocortex. Each panel depicts the number of progenitor cells (color intensity) expressing differing proportions of oRG (X-axis) and gliogenic precursor (Y-axis) marker panel genes at one developmental time in each species. Changes in the distribution of progenitor cells expressing different proportions of the marker genes as development progresses can be seen across panels from left to right. Ages are shown in individual panel titles. pcw = post-conceptional weeks.
Figure 6—figure supplement 1
Transfer of oRG and gliogenic marker panel, derived from the sorted cell types of human fetal cortex, to human progenitor cell subtypes.
The intensity of the dot at coordinate (x,y) indicates the number of human progenitor cells with percent of oRG markers expressed and percent of gliogenic markers expressed.
Figure 6—figure supplement 2
Transfer of oRG and gliogenic marker panel, derived from the sorted cell types of human fetal cortex, to macaque progenitor cell subtypes.
The intensity of the dot at coordinate (x,y) indicates the number of macaque progenitor cells with percent of oRG markers expressed and percent of gliogenic markers expressed.
Figure 6—figure supplement 3
Transfer of oRG and gliogenic marker panel, derived from the sorted cell types of human fetal cortex, to mouse progenitor cells.
The intensity of the dot at coordinate (x,y) indicates the number of mouse progenitor cells with percent of oRG markers expressed and percent of gliogenic markers expressed. (A) Transfer to the four embryonic dates of 1 H cells in Telley dataset. (B) Transfer to the La Manno radial glia cells across time.
Appendix 1—figure 1
Comparison of raw count and binarized scRNA-seq data in standard unsupervised dimension reduction using the Telley (Telley et al., 2019) dataset where ventricular neural progenitor cells were labeled specifically during their final cell division and collected cells for sequencing.
To focus on precise cell types, only cells of the excitatory glutamatergic neuronal lineage were included in this analysis.
Appendix 2—figure 1
UMAP visualizations of cell type annotations for the Hao PBMC dataset, the Tabula Sapiens blood datasets, and the Stephenson COVID-19 PBMC dataset.
(A) Hao PBMC Dataset where the UMAP visualization is available at https://atlas.fredhutch.org/nygc/multimodal-pbmc/. (B) Tabula Sapiens blood dataset sequenced using the Smart-seq2 protocol. (C) Tabula Sapiens blood dataset sequenced using the 10 x Genomics 5’ protocol. (D) Tabula Sapiens blood dataset sequenced using the 10 x Genomics 3’ protocol. (E) Stephenson COVID-19 PBMC dataset.
Appendix 2—figure 2
Cross-dataset marker gene transfer results.
Heatmaps show the proportion of target cells (y-axis) expressing at least five markers from source cell-type-specific marker panels (x-axis) generated by CellCover. Subplots represent transfer results to (A) Tabula Sapiens blood datasets sequenced with Smart-seq2, (B) 10x5’, (C) 10x3’, and (D) the Stephenson COVID-19 PBMC dataset with only the healthy controls.
Appendix 2—figure 3
Transfer of CellCover marker panels from PBMC CITE-seq Dataset to Tabula Sapiens blood dataset sequenced by Smart-seq2 protocol.
For each of the 32 cell types in the PBMC datasets, a CellCover marker panel of depth 5 and covering rate of 98% was derived. Each panel was transferred to the target dataset, with the plots showing the proportion of marker genes expressed in cells of the target dataset, as indicated by the titles of each subplot.
Appendix 2—figure 4
Transfer of CellCover marker panels from PBMC CITE-seq dataset to Tabula Sapiens blood dataset sequenced by 10 x Genomics 5’ protocol.
For each of the 32 cell types in the PBMC datasets, a CellCover marker panel of depth 5 and covering rate of 98% was derived. Each panel was transferred to the target dataset, with the plots showing the proportion of marker genes expressed in cells of the target dataset, as indicated by the titles of each subplot.
Appendix 2—figure 5
Transfer of CellCover marker panels from PBMC CITE-seq dataset to Tabula Sapiens blood dataset sequenced by 10 x Genomics 3’ protocol.
For each of the 32 cell types in the PBMC datasets, a CellCover marker panel of depth 5 and covering rate of 98% was derived. Each panel was transferred to the target dataset, with the plots showing the proportion of marker genes expressed in cells of the target dataset, as indicated by the titles of each subplot.
Appendix 2—figure 6
Transfer of CellCover marker panels from PBMC CITE-seq dataset to Stephenson COVID-19 PBMC dataset.
For each of the 32 cell types in the PBMC datasets, a CellCover marker panel of depth 5 and covering rate of 98% was derived. Each panel was transferred to the target dataset, with the plots showing the proportion of marker genes expressed in cells of the target dataset, as indicated by the titles of each subplot.
Appendix 3—figure 1
Telley CellCover marker genes with highest relative frequency.
This bar plot shows the top cell age covering markers ordered by the relative frequency of each gene being selected by the covering algorithm with , on the Telley dataset.
Appendix 3—figure 2
Geschwind CellCover marker genes with highest relative frequency.
The bar plot shows the top cell age covering markers ordered by three relative frequency of each gene being selected by the covering algorithm with , on the Geschwind dataset.
Appendix 4—figure 1
Over-representation analysis in gene marker panels of differing size and methodology, using the GO database.
Over-representation analysis was performed using the Fisher’s exact test using gene sets from the GO database. Over-represented gene sets were marked as significant by a cutoff using the Benjamini-Hochberg method. The background gene list was defined as all the genes present in the Telley dataset. (A) The number of GO gene sets found to be significantly enriched in the gene marker panels derived from CellCover and DE methods across different matched panel sizes. The X-axis indicates the depth parameter d that was set for the CellCover run (preceding the numbers on the X-axis, ‘d’ indicates the depth in de novo CellCover runs, ‘e’ indicates expanded runs that use marker panels from the preceding CellCover run as a starting point). Corresponding numbers of DE markers were selected for each depth so that marker panels of equal size were compared across CellCover and DE methods. Numbers in parentheses indicate the number of genes in each marker panel. (B) The proportion of gene sets that were found to be significant by one method and not the other.
Appendix 5—figure 1
Overlap and discordance between marker genes selected by CellCover and DE methods.
For each of the 12 CellCover gene panels defined in the Telley mouse data, the number of overlapping genes found by DE methods is quantified. In each panel, the orange line indicates the number of genes in the CellCover gene panel for that cell class. The blue line indicates the cumulative number of CellCover genes also found in the top ranked DE gene marker list as increasingly large DE gene lists are considered. The X-axis value where the orange and blue lines meet indicates the number of DE markers that would need to be selected to include all the CellCover markers.
Appendix 5—figure 2
Sensitivity and specificity of individual marker genes selected by CellCover.
Visualization of expression of individual marker genes selected by CellCover. Among all the covering markers for E12.1H, Hmga2 and Crabp2 have the lowest p-values in DE (and are therefore selected as DE marker genes) and show high sensitivity with lower specificity. In contrast, Nptx2, Pctp, and Cldn9 have the highest p-values in DE (and are therefore not selected as DE marker genes) and show low sensitivity individually, but very high specificity.
Appendix 5—figure 3
Telley 1 H CellCover markers sensitivity vs rank from DE.
We ran Seurat DE on the Telley dataset to discriminate 1 H cells and rank the entire gene portfolio by the p-values. Each CellCover gene (Figure 2A) has its sensitivity of the class , , on the horizontal axis and its rank from DE on the vertical axis. The size of the dot represents the weight of the marker defined in the method section (download the interactive plots at DE Rank vs Sensitivity). The ability of CellCover to borrow power across marker genes of low sensitivity and high specificity is one of the primary factors that sets it apart from DE methods.
Appendix 5—figure 4
Telley 24 H CellCover markers sensitivity vs rank from DE.
We ran Seurat DE on the Telley dataset to discriminate 24 H cells and rank the entire gene portfolio by the p-values. Each CellCover gene (Figure 2A) has its sensitivity of the class , , on the horizontal axis and its rank from DE on the vertical axis. The size of the dot represents the weight of the marker defined in the method section (download the interactive plots at DE Rank vs Sensitivity). The ability of CellCover to borrow power across marker genes of low sensitivity and high specificity is one of the primary factors that sets it apart from DE methods.
Appendix 5—figure 5
Telley 96 H CellCover markers sensitivity vs rank from DE.
We ran Seurat DE on the Telley dataset to discriminate 96 H cells and rank the entire gene portfolio by the p-values. Each CellCover gene (Figure 2A) has its sensitivity of the class , , on the horizontal axis and its rank from DE on the vertical axis. The size of the dot represents the weight of the marker defined in the method section (download the interactive plots at DE Rank vs Sensitivity). The ability of CellCover to borrow power across marker genes of low sensitivity and high specificity is one of the primary factors that sets it apart from DE methods.
Appendix 6—figure 1
Distribution of marker gene expression rate of CellCover and PhenotypeCover.
The blue bars form the histogram of the probabilities of expression of the CellCover (global version) marker genes obtained at and . The probability of expression is computed by . The yellow bars depict the distribution of probabilities of expression of the Phenotype marker gene obtained by letting to match the size of the CellCover marker panel.
Appendix 7—figure 1
CellCover run time of multiple cell populations from the Polioudakis dataset.
The axis is the covering depth , and the axis is the run time. We set a time limit of 1800 s for the program. If the branch and cut algorithm does not converge in 1800 s, MIP gap will be reported. The smaller the gap, the closer the dual and primal objectives are, indicating better convergence.
Tables
Appendix 1—table 1
Average accuracies of different data types and supervised classifiers in recovering the 12 time-based cell labels in the Telley data.
Subscript ‘b’ indicates binary data; all other trials used normalized log2 count level data. XGB = XGBOOST (Chen and Guestrin, 2016). ‘“PCA’ indicates using only the 1st 100 principal components. refers to a neural network with hidden layers and 64 total nodes in the network. The size of the training set is 2342, and the size of the test set is 414.
| XGB | XGBb | NN1 | NN1b | NN2 | NN2b | NN3 | NN3b | ||
|---|---|---|---|---|---|---|---|---|---|
| 95.0 | 93.6 | 86.5 | 91.2 | 93.8 | 97.1 | 93.3 | 96.4 | 87.9 | 94.9 |
Appendix 1—table 2
Comparison of binary and count data in predicative capacity in Poliousdakis dataset.
Subscript ‘b’ indicates binary data; other trials used normalized log count level data. XGB = XGBOOST. ‘PCA’ indicates where classification was based on the 1st 100 principal components. = Neural net with layers, where indicates the number of hidden layers with 64 nodes in the network.
| Donor | XGB | XGBb | NN1 | NN1b | NN2 | NN2b | NN3 | NN3b | ||
|---|---|---|---|---|---|---|---|---|---|---|
| 368 | 91.7 | 90.4 | 91.1 | 92.7 | 91.0 | 93.1 | 90.7 | 92.6 | 90.6 | 92.5 |
| 370 | 87.2 | 84.7 | 86.7 | 87.8 | 85.9 | 87.1 | 86.6 | 87.5 | 86.2 | 87.3 |
| 371 | 91.8 | 90.8 | 90.9 | 91.8 | 89.7 | 91.9 | 90.0 | 91.8 | 90.1 | 91.6 |
| 372 | 92.0 | 91.1 | 91.7 | 92.4 | 92.1 | 92.8 | 91.9 | 92.6 | 91.7 | 92.4 |
Appendix 1—table 3
Adjusted Rand Indices (ARI) for different inputs.
Each entry of the table shows the ARI between Seurat clustering results and true time clusters for each version of input data (count or binary expression profile of the indicated number of genes).
| Count | Binary | |
|---|---|---|
| Random 2000 genes | 0.32 | 0.37 |
| Random 4000 genes | 0.34 | 0.39 |
| Random 6000 genes | 0.36 | 0.41 |
| Random 8000 genes | 0.35 | 0.44 |
| All genes | 0.37 | 0.45 |
Appendix 4—table 1
Cell Cover marker genes extension at various depths.
| 1 H | Rbm20, Gm12481, Sox3, Tcf7l1, Rps18-ps3, Gm8129, Rps10-ps1, Rps13-ps2, Gm3362 |
| 24 H | Rnd2, Kit, Dusp14, Pramel7, Slc30a10, Sema6d, Sh3bgrl2, Neurod1, Nhlh1, Gm15232, Gm13789, Gm5619 |
| 96 H | Gabra1, Rasgrf2, Sla, Ntsr1, Clstn2, N28178, Fam135b, Fam19a2, Ccbe1, Adap1, Camk2b, mt-Tf, mt-Tv, mt-Nd4l, Gm15344, Nwd2, Gm26871 |
| (a) Covering markers of Telley dataset with d=3 and α=0.02 | |
| 1H | Ajuba, Etl4, Rbm20, Gm12481, Sox3, Gas1, Rps18-ps3, Gm8129, Rps10-ps1, Rpl15-ps3, Rps13-ps2, Gm6654, Gm3362, Hspa1a |
| 24 H | Rnd2, Kit, Dusp14, Serping1, Ifitm3, Pramel7, Slc30a10, Sema6d, Rasgef1b, Tmem176b, Crabp1, Neurod1, Mfap4, Sstr2, Nhlh1, 9630028B13Rik, Scrt2, mt-Ti, Adamtsl3, Gm12005, Gm15232, Gm13789, Gm5619 |
| 96 H | Mef2c, Gabra1, Tnr, Usp43, Rasgrf2, Sla, Dync1i1, Ptpro, Clstn2, N28178, Fam135b, Kcnj2, Fam19a2, Ccbe1, Adap1, Camk2b, Unc5c, mt-Tf, mt-Tv, mt-Nd4l, Gm15344, Nwd2, Gm26871, RP23-14P23.9 |
| (b) Covering markers of Telley dataset with d=5 and α=0.02 | |
| 1 H | Ajuba, Notch2, Rbm20, Gm12481, Sox3, Cenpe, Gas1, Tcf7l1, Hmga2, Rps18-ps3, Gm8129, Rps10-ps1, Rpl15-ps3, Sox21, Rps13-ps2, Gm6654, Gm3362, Hspa1a, WI1-1003N17.1 |
| 24 H | Rnd2, Kit, Dusp14, Gadd45g, Wnt7b, Serping1, Ifitm3, Pramel7, Tuba4a, Slc30a10, Sema6d, Rasgef1b, Cabp1, Tmem176b, Sh3bgrl2, Crabp1, Neurod1, Inf2, Ttc39b, Sstr2, Plcb1, Nhlh1, 9630028B13Rik, Scrt2, Adamtsl3, Gm12005, Rpl38-ps1, Gm15232, Gm13789, Pcp4, Gm5619 |
| 96 H | Mef2c, Gabra1, Tnr, Usp43, Rasgrf2, Sla, Dync1i1, Grip2, Ptpro, Clstn2, Arpp21, N28178, Fam135b, Dnm3, Rims1, Fam19a2, Ccbe1, Lingo1, Adap1, Camk2b, Unc5c, mt-Tf, mt-Tv, mt-Nd4l, Gm15344, Nwd2, Uba52, Gm26871, RP23-14P23.8 |
| (c) Covering markers of Telley dataset with d=7 and α=0.02 | |
| 1 H | Nr2e1, Zfp36l1, Spata13, Ajuba, Col2a1, Hes1, Flnb, Notch2, Cenpa, Gsta4, Aspm, Etl4, Rbm20, Gm12481, Sox3, Cenpe, Gas1, Cdca7, Dach1, Tcf7l1, Hmga2, Rps18-ps3, Gm8129, Rps26-ps1, Rps10-ps1, Rpl15-ps3, Sox21, Rpl34-ps1, Rps13-ps2, Sox2, Gm10224, Gm6654, Gm3362, Gm13267, Gm15795, Hspa1b, Hspa1a, Gm28625, WI1-1003N17.1 |
| 24 H | Rnd2, Kit, Dusp14, Nuak1, Gadd45g, Lrrc16b, Wnt7b, 5330426P16Rik, Serping1, Ifitm3, Pramel7, Tuba4a, Slc30a10, Sema6d, Necab3, Rasgef1b, Cabp1, Tmem176b, Sh3bgrl2, Crabp1, Eomes, Shf, Neurod1, Osbpl5, Inf2, Ttc39b, Clvs1, Sox5, Shb, Sstr2, Bcl11b, Nhlh2, Plcb1, Nhlh1, 9630028B13Rik, Rpl31-ps13, Cntn2, A930024E05Rik, Itpk1, Scrt2, Cxcl12, Unc5d, mt-Ti, Adamtsl3, Gm23935, Gm4294, Gm12005, Gm15481, Rpl38-ps1, Gm11989, Gm15232, Gm13789, Pcp4, Rps19-ps12, Gm5619 |
| 96 H | Ndrg1, Mef2c, Gabrb2, Crlf1, Met, Gabra1, Tnr, Glra2, Lama2, Gria1, Usp43, Rgs6, Mctp1, Rasgrf2, Cacna2d3, Sla, Cd200, Fbn2, Apba1, Prkar1b, Grin1, Dync1i1, Grip2, Grin2b, Ptpro, Slc6a11, Clstn2, Arpp21, Cck, Gucy1a3, N28178, Fam135b, Trim67, Dnm3, Cdh12, Necab1, Reps2, Rims1, Dlx1, 9330132A10Rik, Fam19a2, Ccbe1, Gpr85, Lingo1, Dlg2, Adap1, Fry, Ryr3, Camk2b, Unc5c, Kcnma1, mt-Tf, mt-Tv, mt-Nd4l, Nrxn3, A830039N20Rik, Scn2a1, Gm15344, Nwd2, Dlx6os1, Uba52, Gm26871, RP23-74F20.3, RP23-333L19.1, RP23-317L18.3, RP23-14P23.8, RP23-14P23.9 |
| (d) Covering markers of Telley dataset with d=15 and α=0.02 extended from markers in Table Appendix 4-table1c | |
| 1 H | Nr2e1, Zfp36l1, Gli3, Spata13, Ajuba, Col2a1, Hes1, Flnb, Notch2, Cenpa, Adamts9, Dusp16, Tead2, Sall1, Gsta4, Aspm, Etl4, Kif15, Ildr2, Rbm20, Gm12481, Sox3, Cenpe, Cldn12, Gas1, Creb5, Cdca7, Dach1, Tcf7l1, Hmga2, Gm7536, Rps18-ps3, Zfp516, Gm8129, Rps26-ps1, Rps10-ps1, Rpl15-ps3, Sox21, Phactr2, Rpl34-ps1, Rps13-ps2, Sox2, Gm10224, Gm6654, Gm3362, Gm13267, Gm15795, Hspa1b, Hspa1a, Gm26870, Gm28625, WI1-1003N17.1 |
| 24 H | Rnd2, Kcnn1, Kit, Syn2, Myt1, Dusp14, Nuak1, Gadd45g, Lrrc16b, Wnt7b, 5330426P16Rik, Serping1, Tmem178, Rcor2, Ifitm3, Nrp1, Pramel7, Tuba4a, Slc30a10, Sema6d, Necab3, Rasgef1b, Cabp1, Tmem176b, Nkd1, Sh3bgrl2, Crabp1, Eomes, Shf, Neurod1, Klhl35, Abca7, Ppp1r14a, Osbpl5, Inf2, Ttc39b, Clvs1, Sox5, Pxylp1, Shb, Rpsa-ps10, Sstr2, Bcl11b, Nhlh2, Plcb1, Nhlh1, 9630028B13Rik, Rpl31-ps13, Cntn2, A930024E05Rik, Itpk1, Scrt2, Cxcl12, Unc5d, mt-Ti, mt-Co3, Adamtsl3, Gm23935, Gm4294, Gm12005, Gm15481, Rpl38-ps1, Gm11989, Gm14094, Gm15232, Gm13789, Pcp4, Gm6467, Rps19-ps12, A730020E08Rik, Gm5619 |
| 96 H | Gabra2, Cacna1e, Chd5, Ndrg1, Mef2c, Gabrb2, Crlf1, Met, Gabra1, Tnr, Cacna1d, Glra2, Lama2, Gria1, Usp43, Rgs6, Mctp1, Rasgrf2, Cacna2d3, Sla, Cd200, Fbn2, Apba1, Prkar1b, Atp1b1, Grin1, Sparcl1, Dync1i1, Npy, Grip2, Grin2b, Ptpro, Slc6a11, Clstn2, Cspg5, Arpp21, Cck, Gucy1a3, Syt1, N28178, Fam135b, Trim67, Gabbr2, Dnm3, Cdh12, Necab1, Reps2, Inhba, 4930506M07Rik, Rims1, Dlx1, 9330132A10Rik, Fam19a2, Ccbe1, Gpr85, Lonrf2, Lingo1, Dlg2, Arhgap20, Adap1, Fry, Ryr3, Camk2b, Unc5c, Gm10115, Kcnma1, mt-Tf, mt-Tv, mt-Tl1, mt-Nd4l, Nrxn3, A830039N20Rik, 1810009A15Rik, Scn2a1, Gm15344, Gm15662, Nwd2, Dlx6os1, Uba52, Gm26871, RP23-74F20.3, RP23-333L19.1, RP23-317L18.3, RP23-14P23.8, RP23-14P23.9 |
| (e) Covering markers of Telley dataset with d=20 and α=0.02 extended from markers in Table Appendix 4-table1d | |
| 1 H | Otx1, Cdc20, Nr2e1, Plagl1, Grb10, Zfp36l1, Gli3, Samd4, Spata13, Ajuba, Col2a1, Hes1, Flnb, Notch1, Wwtr1, Notch2, Cenpa, Adamts9, Dusp16, Tead2, Sall1, Gsta4, Aspm, Etl4, Kif15, Mavs, Ildr2, Zic5, Dmrt3, Rbm20, Gm12481, Sox3, Cenpe, Cldn12, Gas1, Creb5, E330013P04Rik, Fgfr3, Cdca7, Dach1, Tcf7l1, Hmga2, Gm7536, Rps18-ps3, Zfp516, Gm8129, Rps26-ps1, Rps10-ps1, Rpl15-ps3, Sox21, Gm5453, Phactr2, Rpl34-ps1, Rps13-ps2, Sox2, Gm10224, Gm6654, Gm3362, Gm13267, Gm15795, Hspa1b, Hspa1a, Gm26870, 1190002F15Rik, Gm28625, WI1-1003N17.1 |
| 24 H | Sez6, Rnd2, Kcnn1, Kit, Syn2, Myt1, Dusp14, Nuak1, Chga, Gadd45g, Lrrc16b, Wnt7b, 5330426P16Rik, Serping1, Tmem178, Rcor2, Ifitm3, Nrp1, Pramel7, Tuba4a, Slc30a10, Sema6d, Necab3, Trp53inp1, Rasgef1b, Cabp1, Tmem176b, Nkd1, Sh3bgrl2, Crabp1, Eomes, Shf, Neurod1, Klhl35, Abca7, Ppp1r14a, Osbpl5, Inf2, Neurod6, Ttc39b, Neurod2, Nrn1, Ccser1, Clvs1, Sox5, Kif21b, Mfap4, Pxylp1, Shb, Rpsa-ps10, Sstr2, Bcl11b, Nhlh2, Plcb1, Nhlh1, 9630028B13Rik, Rpl31-ps13, Cntn2, A930024E05Rik, Itpk1, Rpl31-ps20, Scrt2, Cxcl12, Unc5d, mt-Ti, mt-Co3, Tecpr1, Hist2h2ac, Adamtsl3, Gm23935, Gm4294, Gm12005, Gm15481, Rpl38-ps1, Gm8606, Gm11989, Gm14094, Gm15232, Gm13789, Pcp4, Gm6467, Rps19-ps12, A730020E08Rik, Gm5619, 2600014E21Rik, Gm8885 |
| 96 H | Gabra2, Dlgap1, Cacna1e, Chd5, Ndrg1, Mef2c, Runx1t1, Gabrb2, Crlf1, Met, Gabra1, Prrt1, Tnr, Cacna1d, Glra2, Cacng2, Lama2, Gria1, Usp43, Rgs6, Mctp1, Rasgrf2, Cacna2d3, Sla, Cd200, Grm2, Nrxn1, Fbn2, Apba1, Prkar1b, Atp1b1, Grin1, Ntsr1, Ppp2r2c, Sparcl1, Dync1i1, Npy, Grip2, Grin2b, Ptpro, Tmtc1, Slc6a11, Clstn2, Cspg5, Arpp21, Cck, Gucy1a3, Lrfn5, Syt1, N28178, Fam135b, Trim67, March4, Gabbr2, Dnm3, Cdh12, Necab1, Reps2, Inhba, 4930506M07Rik, Rims1, Dlx1, Bend6, 9330132A10Rik, Fam19a2, Syt16, Amer3, Nxph1, Ccbe1, Gpr85, Lonrf2, Lingo1, Grm5, Dlg2, Arhgap20, Ppfia2, Cntn1, Maf, Adap1, Fry, Ryr3, Camk2b, Unc5c, Erbb4, Opcml, Gm10115, Kcnma1, mt-Tf, mt-Tv, mt-Tl1, mt-Nd4l, Nrxn3, Gad1, A830039N20Rik, 1810009A15Rik, Scn2a1, Gm15344, Gm15662, Nwd2, Dlx6os1, Uba52, A330076H08Rik, Gm26871, RP23-74F20.3, RP24-204F16.4, RP23-416H10.5, RP23-333L19.1, RP23-317L18.3, RP23-14P23.8, RP23-14P23.9 |
| (f) Covering markers of Telley dataset with d=25 and α=0.02 extended from markers in Table Appendix 4-table1e | |
Appendix 5—table 1
Sensitivity analysis of CellCover 1 H markers.
The first column of the table lists the average sensitivity of the marker gene in the cell population that is not 1 H, and the second column lists the sensitivity of the marker within class 1 H. The third column lists the p-value rank from DE. The CellCover marker with low sensitivity of 1 H also tends to have a low p-value rank in DE.
| Gene | Rank in DE | ||
|---|---|---|---|
| Rps10-ps1 | 0.07 | 0.82 | 3 |
| Gm3362 | 0.07 | 0.88 | 3 |
| Gm6654 | 0.17 | 0.92 | 3 |
| Rps18-ps3 | 0.10 | 0.88 | 3 |
| Rps13-ps2 | 0.17 | 0.91 | 3 |
| Gm8129 | 0.09 | 0.75 | 8 |
| Sox3 | 0.10 | 0.70 | 17 |
| Gas1 | 0.21 | 0.80 | 28 |
| Gm12481 | 0.00 | 0.39 | 42 |
| Etl4 | 0.15 | 0.67 | 57 |
| Ajuba | 0.08 | 0.49 | 108 |
| Rpl15-ps3 | 0.03 | 0.37 | 121 |
| Rbm20 | 0.05 | 0.40 | 149 |
| Hspa1a | 0.00 | 0.13 | 524 |
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CellCover defines marker gene panels capturing developmental progression in neocortical neural stem cell identity
eLife 14:RP107531.
https://doi.org/10.7554/eLife.107531.2
