Differentiation related variation of histone modification patterns.

<p>(a) Heatmap showing the gene-level H3K4me1 scores for the 100 most significantly different genes. (b) Heatmap of the chromatin states for the 722 “hotspot” genes, whose chromatin states are significantly different across differentiation statuses. Red – active state; yellow – null state; blue – nonactive state. The cell line information is shown at both sides of the heatmap and color-coded by the differentiation status (black – pluripotent cells (P); red – multipotent (M); green – unipotent/differentiated (U/D)).</p

Outcome of classifying the differentiation status of three cancer cell lines (K562, HeLa, and VCaP) by applying the support vector machine to histone modification data at different levels.

<p>The results are represented as three numbers, corresponding to the number of models for which the cell line classified as pluripotent (P), multipotent (M), or unipotent/differentiated (U/D), respectively.</p

Cancer cells display similar chromatin state patterns as fully differentiated cells.

<p>(a) A scatter plot of cell-type specific chromatin states associated with the three ES regulatory modules. The chromatin state of a module is summarized by the fraction of non-active-state genes. Each data point corresponds to one cell-type and is color-coded according to the differentiation status. The three cancer cells are labeled. (b) Hierarchical clustering of the 27 cell lines based on the chromatin states.</p

Representative chromatin domains identified by the hidden Markov model.

<p>Heatmap of the chromatin state distribution at the local genomic loci. (a) the HOXB gene cluster; (b) The histone gene cluster. Genes are ordered according to their genomic positions. Red – active state; yellow – null state; blue – nonactive state. The cell line information is shown at both sides of the heatmap and color-coded by the differentiation status (black – plutipotent (P) cells; red – multipotent (M); green – unipotent/differentiated (U/D)).</p

Image1_RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data.TIFF

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer’s disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.</p

Additional file 1 of Identifying quantitatively differential chromosomal compartmentalization changes and their biological significance from Hi-C data using DARIC

Additional file 1: Figure 1. Introduction of the DARIC framework. A Scatter plot showing the high correlation between PIS and PC1 values from the H1ESC Hi-C data. B MA plot showing the systematic differences between H1ESC and K562 cells. Each dot represents a 50kb bin. The Red dashed line represents the fitted line from the M and A values. C MA plot after normalization showing the elimination of the systematic differences between the two cell types. D-E The emission matrix (D) and state coverage matrix (E) for the 5-state HMM model. F Confusion matrix showing the overlap between the states of 5-state model and those of the 4-state model. Numbers represent 50kb bins. Figure 2. Functional association between gene regulation and differential compartments revealed by DARIC. A-B Heatmap showing the enrichment of cell type-specific genes (A) and superenhancers. (B) in the four states identified by DARIC. Values show the log2(observed/expected) enrichment. C Bar plots showing the expression of SOX2 and MYB genes in H1ESC and K562 cells. Figure 3. Comparison between DARIC and existing methods. A Venn diagram presenting the overlap between the ‘Strong-’ state revealed by DARIC and the ‘AB’ state in conventional analyses. The numbers in the plot represent the numbers of 50kb bins. B Violin plot showing the PIS differences for the three types of domains defined in (A). C-D Violin plots showing the comparisons of Lamina1-DamID signal changes (C), and gene expression fold changes (D) in the three types of domains defined in (A). E Venn diagram showing the overlap of genomic bins identified with decreased PIS/PC1 values in K562 by DARIC and dcHiC. Numbers of 50kb bins were shown in the diagram. F Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (E). G An exemplary region showing DARIC and dcHiC output with decreased PIS in K562 cells. H-K Performance comparison between DARIC and HOMER using H1ESC versus K562 as an example. (H) Venn diagram showing the overlap of genomic bins identified with increased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (I) Enrichment of K562-specific genes for the three types of genomic regions defined in (H). (J) Venn diagram showing the overlap of genomic bins identified with decreased PIS values in K562 by DARIC and HOMER. Numbers of 50kb bins were shown in the diagram. (K) Enrichment of H1ESC-specific genes for the three types of genomic regions defined in (J). Figure 4. DARIC is robust to technical variations in Hi-C data, such as choices of restriction enzymes and sequencing depth. A Snapshot of chromosome 6 showing the comparison in scaling differences in PIS from three different restriction enzymes before and after the normalization step performed by DARIC. B Snapshot of chromosome 6 showing the high similarity of PIS from Hi-C data at different sequencing depths. Figure 5. Applying DARIC to delineating compartment changes during cardiomyocyte differentiation. A Emission matrix resulting from the HMM model trained in the cardiomyocyte system. B Cardiomyocyte-specific genes associated with significant PIS increases during the differentiation tend to be involved in longer loops than those without PIS increases. C GO enrichment analysis for two sets of cardiomyocyte-specific genes classified by whether associated with significant PIS changes. Figure 6. Applying DARIC to a compendium of Hi-C datasets across many cell types. A Distribution of TSA-seq signals in the five variability states in the three cell lines. B Distribution of DamID signals in K562 cells. C Stacked bar plots showing the composition percentages of the five sub-compartments in the five variability states. D PIS variability comparison for the five sub-compartments

REPRODUCTION

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Table1_RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data.XLSX

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer’s disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.</p

Image2_RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data.TIFF

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer’s disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.</p

Additional file 1: Figure S1. of Predicting chromatin organization using histone marks

Prediction of Jin2013 hubs. (A) Distribution of chromatin anchors interactions frequency. Top 10 % are defined as hubs. (B) Percentage of super-enhancers covered by hubs. (C) Prediction accuracy using DNA sequence genetic features, including PhastCons conservation score, TSS proximity and GC content. AUC scores are shown in parentheses. (D) Prediction accuracy using individual histone marks. AUC scores are shown in parentheses. (E) Hubs prediction performance for hubs defined using different thresholds of interactions frequency. (F) Hubs prediction performance with various number of trees. Figure S2. Prediction of Rao2014 hubs. (A) Distribution of chromatin anchors interactions frequency. Top 10 % are defined as hubs. (B) Prediction accuracy using individual histone marks. AUC scores are shown in parentheses. Figure S3. TAD boundary prediction accuracy using individual histone marks. (PDF 1896 kb