Heatmap of the 15 most variable genes in the GTEx heart samples post filtering, related to Figs. 1 and 3. Heatmap of the 15 most variable genes in the GTEx heart samples. Left, top 15 genes were chosen in an unsupervised manner using the normalized gene expression after a stringent filtering in a tissue-agnostic manner. Right, the 15 most variable genes were chosen in an unsupervised manner using the normalized gene expression after tissue-specific filtering. (PDF 277 kb
Details on the settings for the STAR algorithm and R commands to obtain normalized data. (DOCX 13 kb
EPIG-Seq analysis of the simulated data. A) Thumbnail plots of the simulated gene profiles that are ...
Table S1. Sequencing and mapping statistics. Listed are the sequencing details and mapping statistic...
Breakdown of gene types remaining in each data set after different filtering approaches. Filtering i...
Animated density plots of log-transformed counts when including more tissues, related to Fig. 1. G...
Gene specific heatmaps. (A) Human heart development comparing gene transcripts for vocal fold, lung,...
Expression estimates of genes are consistent across two independent analysis pipelines. RNA-Seq expr...
Spearmanâs correlation to RT-qPCR data of 30 genes. Correlation coefficients (x-axis) of 30 genes ...
Differential gene expression pattern analysis for dermal comparisons identified by RNA sequencing. (...
Fold-change consistency between EdgeR and DESeq2 methods. A) Correlation analysis between fold-chang...
Figure S3. Showing digital mRNA expression from RNA-sequencing. (A) Hierarchical clustering (heat ma...
Genes with at least two-fold change in expression between UHRR and HBRR have a nearly even distribut...
Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. ...
top-50 gene expression. Figure S1. First fifty top-ranked gene expression level by different methods...
Pipeline of data processing. Figure S2. Comparison with other methods to separate human and mouse re...
Details on the settings for the STAR algorithm and R commands to obtain normalized data. (DOCX 13 kb
EPIG-Seq analysis of the simulated data. A) Thumbnail plots of the simulated gene profiles that are ...
Table S1. Sequencing and mapping statistics. Listed are the sequencing details and mapping statistic...
Breakdown of gene types remaining in each data set after different filtering approaches. Filtering i...
Animated density plots of log-transformed counts when including more tissues, related to Fig. 1. G...
Gene specific heatmaps. (A) Human heart development comparing gene transcripts for vocal fold, lung,...
Expression estimates of genes are consistent across two independent analysis pipelines. RNA-Seq expr...
Spearmanâs correlation to RT-qPCR data of 30 genes. Correlation coefficients (x-axis) of 30 genes ...
Differential gene expression pattern analysis for dermal comparisons identified by RNA sequencing. (...
Fold-change consistency between EdgeR and DESeq2 methods. A) Correlation analysis between fold-chang...
Figure S3. Showing digital mRNA expression from RNA-sequencing. (A) Hierarchical clustering (heat ma...
Genes with at least two-fold change in expression between UHRR and HBRR have a nearly even distribut...
Figure S1. RNA-seq quality detection of differential regulated genes in air and ethylene treatment. ...
top-50 gene expression. Figure S1. First fifty top-ranked gene expression level by different methods...
Pipeline of data processing. Figure S2. Comparison with other methods to separate human and mouse re...
Details on the settings for the STAR algorithm and R commands to obtain normalized data. (DOCX 13 kb
EPIG-Seq analysis of the simulated data. A) Thumbnail plots of the simulated gene profiles that are ...
Table S1. Sequencing and mapping statistics. Listed are the sequencing details and mapping statistic...