Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Description of the sequencing budget allocation problem. Consider estimating the underlying gene distribution (top) from the noisy read counts obtained via sequencing (bottom). With a fixed number of reads to be sequenced, deep sequencing of a few cells accurately estimates each individual cell but lacks coverage of the entire distribution (left), whereas a shallow sequencing of many cells covers the entire population but introduces a lot of noise (right). b Optimal tradeoff. The memory T-cell marker gene S100A4 has 41.7k reads in the pbmc_4k dataset. For estimating the underlying gamma distribution \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${X}_{g} \sim {\rm{Gamma}}({r}_{g},{\theta }_{g})$$\end{document} X g ~ Gamma ( r g , θ g ) , the relative error is plotted as a function of the sequencing depth, where the optimal error is obtained at a depth of one read per cell (orange star) and is two times smaller than that at the current depth of pbmc_4k (red triangle). c Experimental design. To determine the sequencing depth for an experiment, first the relative gene expression level can be obtained via pilot experiments or previous studies (top left). Then the researcher can select a set of genes of interest (i.e., some marker genes highlighted as black dots), of which the smallest relative expression level \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${p}^{* }$$\end{document} p * ( MS4A1 ) defines the reliable detection limit. Finally, the optimal sequencing depth is determined as \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${n}_{{\rm{reads}}}^{* }=1/{p}^{* }$$\end{document} n reads * = 1 ∕ p * (top right). The errors under different tradeoffs are visualized as a function of the genes ordered from the most expressed to the least (bottom). The optimal sequencing budget allocation (orange) minimizes the worst-case error over all the genes of interest (left of the red dashed line), whereas both the deeper sequencing (green) and the shallower sequencing (blue) yield worse results.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Marker, Expressing

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: for estimating the coefficient of variation (CV), the plug-in estimates become more inflated as the sequencing depth becomes shallower (from right to left along the x axis), whereas the EB estimates are consistent. 3-std confidence intervals are provided for this panel. Middle: both brain_1k and brain_1.3m are from the mouse brain, and hence each gene should have a similar CV value between the two datasets. This is indeed the case for the EB estimator (right), which is adaptive to different sequencing depths. However, as brain_1k is twice deeper than brain_1.3m, the plug-in estimates are biased that most points are above the 45-degree line (red). Bottom: distribution recovery for the gene GZMA from a dataset that is subsampled to be five times shallower (left). The EB estimator provides a reasonable estimation for both the zero proportion and the tail shape, resulting in a small total variation error (right). b Feature selection and PCA. The task is to first select features (genes) based on CV, and then perform PCA on the selected features. The results on the full data (pbmc_4k) and a subsampled (three times shallower) are compared. EB estimates are more consistent between the full data and the subsampled data for both the CV ranks (top) and the PCA plots (bottom).

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Sequencing, Selection

Journal: Nature Communications

Article Title: Determining sequencing depth in a single-cell RNA-seq experiment

doi: 10.1038/s41467-020-14482-y

Figure Lengend Snippet: a Top: the EB-estimated Pearson correlation for some marker genes in pbmc_4k are visualized, ordered by different cell populations (top). The clear block-diagonal structure implies that the EB estimator is capable of capturing the gene functional groups. As a comparison, the plug-in estimator also recovers those modules but with a weaker contrast (bottom left panel, plug-in with 100%). Bottom: a subsample experiment further shows that the EB estimator can recover the module with 5% of the data. For the plug-in estimator, the first block (T cells) is blurred with 25% of the data, and the entire structure vanishes with 10% of the data. b Gene network based on the EB-estimated Pearson correlation using the pbmc_4k dataset. Most gene modules correspond to important cell types or functions, including T cells, B cells, NK-cells, myeloid-derived cells, megakaryocytes/platelets, ribosomal protein genes, and mitochondrially encoded protein-coding genes. c Left: the estimated Pearson correlations between all genes and LCK (1st panel) and CD3D (2nd panel), two known T-cell markers. There are three modes for the EB-estimated values, where the positive mode, the zero mode, and the negative mode correspond to genes in the same module, different modules, and irrelevant genes, respectively. The plug-in estimated values are nonetheless much closer to zero even for the truly correlated ones, indicating an artificial shrinkage of the estimated values. Right: two instances where the EB estimates are significantly different from the plug-in estimates. The axes represent read counts, and the color codes the number of cells. Both gene pairs are biologically validated (see Gene network analysis in Methods). See also Supplementary Figs. – for more examples.

Article Snippet: They are publicly available and can be downloaded via the following links: pbmc_4k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc4k pbmc_8k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/pbmc8k brain_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_900 brain_2k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neurons_2000 brain_9k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/neuron_9k brain_1.3m: https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons 293T_1k, 3T3_1k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_1k 293T_6k, 3T3_6k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_6k 293T_12k, 3T3_12k: https://support.10xgenomics.com/single-cell-gene-expression/datasets/2.1.0/hgmm_12k We note that pbmc_4k and pbmc_8k are from the same donor; brain_1k and brain_9k are also from the same donor.

Techniques: Marker, Blocking Assay, Functional Assay, Derivative Assay

Journal: Cancers

Article Title: Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer

doi: 10.3390/cancers13194923

Figure Lengend Snippet: scCMS of individual cells. ( a ) Heatmap plot of normalized enrichment scores of the 27 marker gene-sets across all single tumor cells of 10 CRC samples from Lee et al. cohort. ( b ) UMAP plot of different tumor-tissue cell lineages. ( c ) UMAP plot of the four scCMS distributions and mixed transcriptomic states of the single tumor cells. ( d ) Proportions of scCMS groups in different cell lineages from Lee et al. scRNASeq dataset. ( e ) The UMAP plot of the epithelial-cell subtypes. ( f ) scCMS distributions within the epithelial-cell cluster. ( g ) Proportions of scCMSs in the populations of enterocytes and goblet- and transit-amplifying (TA) cells.

Article Snippet: The processed single-cell RNA sequencing (scRNAseq) profiles and corresponding metadata of ten CRC tissue samples obtained from five CRC patients (border and core regions of tumor tissue of each patient) of Commissie Medische Ethiek UZ KU Leuven/Onderzoek, Belgium, were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database with the accession code GSE144735 [ ].

Techniques: Marker

Journal: Cancers

Article Title: Implications of Intratumor Heterogeneity on Consensus Molecular Subtype (CMS) in Colorectal Cancer

doi: 10.3390/cancers13194923

Figure Lengend Snippet: scCMSs from two CRC with mixed subtype from bulk transcriptome. UMAP plots of different single-cell types and scCMS predictions in ( a , b ) Patient #1 and ( c , d ) Patient #2 scRNAseq profiles, respectively. UMAP plots of single tumor cells were plotted using the normalized enrichment scores of the 27 marker gene sets obtained from ssGSEA. ( e ) Distributions of scCMSs in immune-, epithelial-, and stromal cell populations in patient #1 and patient #2.

Article Snippet: The processed single-cell RNA sequencing (scRNAseq) profiles and corresponding metadata of ten CRC tissue samples obtained from five CRC patients (border and core regions of tumor tissue of each patient) of Commissie Medische Ethiek UZ KU Leuven/Onderzoek, Belgium, were downloaded from the National Center for Biotechnology Information Gene Expression Omnibus (GEO) database with the accession code GSE144735 [ ].

Techniques: Marker

Journal: PLoS Computational Biology

Article Title: Cell type-specific mechanisms of information transfer in data-driven biophysical models of hippocampal CA3 principal neurons

doi: 10.1371/journal.pcbi.1010071

Figure Lengend Snippet: (A) Bidimensional representation of parameter values transformed using UMAP: each dot represents one individual, and closed lines indicate the convex hulls associated with all the individuals obtained with a given morphology (color-coded accordingly to both the convex hull and the points contained in it). (B) UMAP projection and clustering of CA3 excitatory neurons based on scRNAseq data. Using the Leiden clustering algorithm with a resolution of 0.65 delineated the primary division in the CA3 principal neuron population. Note that CA3 principal cells are primarily composed of a larger population of cells (cluster 1, black) and a second minority population (cluster 2, red). (C) Violin plots of the distributions of maximal conductance values for four different classes of ion channels (potassium, calcium, sodium and hyperpolarization-activated) for the model cells included in the analysis, normalized over the range of allowed variability of each parameter as reported in (black and red indicate thorny and a-thorny cells, respectively). Dashed lines indicate the median of the population, while the upper and lower dotted lines represent the 25th and 75th percentile of the distributions. Most parameter distributions were significantly different between the two cell-types (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001). For the remaining parameters see . (D) Expression levels for cells belonging to cluster 1 (black) or cluster 2 (red) for analogous classes of ion channel genes as shown in (C). Note that expression levels for most Na channel genes were not significantly different while Ca and K channel genes were significantly differentially expressed between the two clusters (non-parametric Kolmogorov-Smirnov test: * p < 0.05, ** p < 0.01, *** p < 0.001).

Article Snippet: We utilized scRNAseq data for 314 cells from the CA3 region of the hippocampus from the Allen institute for Brain Science (2018 Allen Institute for Brain Science.

Techniques: Transformation Assay, Expressing