breast cancer spatial transcriptomics pipeline dataset Search Results


97
Sophia Genetics transcript numbers
Transcript Numbers, supplied by Sophia Genetics, used in various techniques. Bioz Stars score: 97/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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95
Broad Clinical Labs research whole exome sequencing deep coverage pipeline
Fig. 2 Comparison of <t>whole-exome</t> <t>sequencing</t> of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses
Research Whole Exome Sequencing Deep Coverage Pipeline, supplied by Broad Clinical Labs, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Thermo Fisher bioscope version 1.3 whole transcriptome pipeline
Fig. 2 Comparison of <t>whole-exome</t> <t>sequencing</t> of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses
Bioscope Version 1.3 Whole Transcriptome Pipeline, supplied by Thermo Fisher, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Lifetech Scientific Corporation lifescope 2.5.1 whole transcriptomic analysis pipeline
Fig. 2 Comparison of <t>whole-exome</t> <t>sequencing</t> of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses
Lifescope 2.5.1 Whole Transcriptomic Analysis Pipeline, supplied by Lifetech Scientific Corporation, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Pacific Biosciences isoseq pipeline
A flowchart describing the data processing steps required prior to the manual curation process. RNA is first sequenced using <t>both</t> <t>PacBio</t> and Illumina platforms. PacBio long reads are trimmed and refined using the <t>IsoSeq</t> pipeline, aligned to the reference genome using Minimap2, assembled into non-redundant transcripts using StringTie, and ORFs predicted using TransDecoder. Illumina short reads are trimmed using Fastp, aligned to the reference genome using STAR, and protein-coding genes are predicted using BRAKER.
Isoseq Pipeline, supplied by Pacific Biosciences, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Becton Dickinson rhapsody whole transcriptome assay analysis pipeline (v1.8
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Rhapsody Whole Transcriptome Assay Analysis Pipeline (V1.8, supplied by Becton Dickinson, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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rhapsody whole transcriptome assay analysis pipeline (v1.8 - by Bioz Stars, 2026-04
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90
Lexogen GmbH quantseq 2.3.6 fwd pipeline
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Quantseq 2.3.6 Fwd Pipeline, supplied by Lexogen GmbH, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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95
TaKaRa seeker v1 0 curio processing pipeline
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Seeker V1 0 Curio Processing Pipeline, supplied by TaKaRa, used in various techniques. Bioz Stars score: 95/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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90
Spatial Transcriptomics Inc seurat v4 pipeline
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Seurat V4 Pipeline, supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Complete Genomics Inc whole genome sequencing
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Whole Genome Sequencing, supplied by Complete Genomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Illumina Inc transcriptome sequencing pipeline
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Transcriptome Sequencing Pipeline, supplied by Illumina Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Personalis Inc ace cancer transcriptome analysis pipeline
Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell <t>transcriptome</t> data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.
Ace Cancer Transcriptome Analysis Pipeline, supplied by Personalis Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Image Search Results


Fig. 2 Comparison of whole-exome sequencing of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses

Journal: Nature communications

Article Title: Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors.

doi: 10.1038/s41467-017-00965-y

Figure Lengend Snippet: Fig. 2 Comparison of whole-exome sequencing of cfDNA to whole-exome sequencing of matched tumor biopsies. a Fraction of clonal (≥0.9 cancer cell fraction, CCF) and subclonal (<0.9 CCF) SSNVs detected by MuTect in WES of tumor biopsies and confirmed (i.e., supported by ≥3 variant reads) in WES of cfDNA. Sites with <3 reads that had power <0.9 for mutation calling were not included when computing the fraction of SNVs confirmed (“Methods”). b Fraction of clonal and subclonal SSNVs detected in WES of cfDNA and confirmed in WES of tumor biopsies. For 18 patients with WES of cfDNA at a second time point t2, SSNVs not detected in the matched tumor biopsy but confirmed at t2 are indicated with black. c Analysis of clonal dynamics in an ER+ breast cancer patient diagnosed with metastatic disease 1.5 years (yrs) prior to biopsy and cfDNA collection (t1, Day 0). Clustering analysis of CCF for SSNVs between matched tumor biopsy and cfDNA (t1) is shown in the left panel. The right panel shows the CCF of four mutation clusters, one containing ESR1 L536P (Subclonal Cluster 1, orange) and the other containing ESR1 D538G (Subclonal Cluster 2, light blue), at t1 and t2 (51 days apart) from a patient with ER+ metastatic breast cancer being treated with a SERD. The lymph node biopsy was taken at the same time as cfDNA t1. Mutations were clustered by the CCFs for each pair of samples using Phylogic39 (“Methods”). Error bars represent the 95% credible interval of the joint posterior density of the clusters. Mutations, excluding indels, having ≥90% estimated power based on coverage in both samples are shown; clusters with fewer than three mutations are excluded. The number of mutations in each cluster is indicated in the legend in parentheses

Article Snippet: Matched tumor biopsies were processed and sequenced through the Broad Institute Genomics Platform’s Research Whole Exome Sequencing deep coverage pipeline (http://genomics.broadinstitute.org/data-sheets/DTS_WES_1Page_52016_0.pdf).

Techniques: Comparison, Sequencing, Variant Assay, Mutagenesis

A flowchart describing the data processing steps required prior to the manual curation process. RNA is first sequenced using both PacBio and Illumina platforms. PacBio long reads are trimmed and refined using the IsoSeq pipeline, aligned to the reference genome using Minimap2, assembled into non-redundant transcripts using StringTie, and ORFs predicted using TransDecoder. Illumina short reads are trimmed using Fastp, aligned to the reference genome using STAR, and protein-coding genes are predicted using BRAKER.

Journal: bioRxiv

Article Title: Novel and improved Caenorhabditis briggsae gene models generated by community curation

doi: 10.1101/2023.05.16.541014

Figure Lengend Snippet: A flowchart describing the data processing steps required prior to the manual curation process. RNA is first sequenced using both PacBio and Illumina platforms. PacBio long reads are trimmed and refined using the IsoSeq pipeline, aligned to the reference genome using Minimap2, assembled into non-redundant transcripts using StringTie, and ORFs predicted using TransDecoder. Illumina short reads are trimmed using Fastp, aligned to the reference genome using STAR, and protein-coding genes are predicted using BRAKER.

Article Snippet: We sequenced the QX1410 transcriptome using both Pacific Biosciences (PacBio) Single-Molecule Real-Time (SMRT) and Illumina platforms and refined the PacBio long reads into 95,177 high-quality transcripts using the IsoSeq pipeline [ ].

Techniques:

A screen capture of the Apollo genome annotation editor showing a set of manually curated genes (located on chromosome X from 14,314,000 to 14,325,000) and their underlying evidence. Four individual tracks are displayed from top to bottom: BRAKER gene models, StringTie gene models, PacBio Iso-Seq refined transcript alignments, and paired-end Illumina RNA-seq alignments. The final set of curated gene models is displayed in the top box shaded in yellow labeled ‘User-created Annotations’. Both Illumina and PacBio RNA data suggest that the two BRAKER genes at the ends of this region were incorrectly split. StringTie models resolve the incorrect split but lack the two internal genes on the opposite strand (g2618.t1 and g2619.t1), because they lack long-read RNA coverage. We kept the StringTie model that best matches the RNA evidence and added the two internal genes on the opposite strand predicted by BRAKER and supported by short-read RNA-seq. Curated and predicted gene models are colored by coding sequence phase. Illumina and IsoSeq alignments are colored by strand orientation.

Journal: bioRxiv

Article Title: Novel and improved Caenorhabditis briggsae gene models generated by community curation

doi: 10.1101/2023.05.16.541014

Figure Lengend Snippet: A screen capture of the Apollo genome annotation editor showing a set of manually curated genes (located on chromosome X from 14,314,000 to 14,325,000) and their underlying evidence. Four individual tracks are displayed from top to bottom: BRAKER gene models, StringTie gene models, PacBio Iso-Seq refined transcript alignments, and paired-end Illumina RNA-seq alignments. The final set of curated gene models is displayed in the top box shaded in yellow labeled ‘User-created Annotations’. Both Illumina and PacBio RNA data suggest that the two BRAKER genes at the ends of this region were incorrectly split. StringTie models resolve the incorrect split but lack the two internal genes on the opposite strand (g2618.t1 and g2619.t1), because they lack long-read RNA coverage. We kept the StringTie model that best matches the RNA evidence and added the two internal genes on the opposite strand predicted by BRAKER and supported by short-read RNA-seq. Curated and predicted gene models are colored by coding sequence phase. Illumina and IsoSeq alignments are colored by strand orientation.

Article Snippet: We sequenced the QX1410 transcriptome using both Pacific Biosciences (PacBio) Single-Molecule Real-Time (SMRT) and Illumina platforms and refined the PacBio long reads into 95,177 high-quality transcripts using the IsoSeq pipeline [ ].

Techniques: RNA Sequencing Assay, Labeling, Sequencing

Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell transcriptome data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.

Journal: Advanced Science

Article Title: Highly Accurate Estimation of Cell Type Abundance in Bulk Tissues Based on Single‐Cell Reference and Domain Adaptive Matching

doi: 10.1002/advs.202306329

Figure Lengend Snippet: Overview of SCROAM. a) The deconvolution model that uses a reference requires two input datasets: bulk RNA‐seq count and a reference containing counts of scRNA‐seq reads. Additionally, the single‐cell transcriptome data must label the cell type to be quantified. b) SCROAM learns gene‐specific transformations of bulk data by utilizing the reference sequences observed in single‐cell data. This allows us to account for potential technical bias between sequencing technologies used in single‐cell and bulk RNA‐seq data. c) SCROAM begins with scRNA‐seq data and classifies the cells into different cell types, which were represented by different colors in the analysis. By calculating gene specificity in a given cell type, an expression matrix reflecting cell type specificity was constructed. d) SCROAM employs single‐cell reference data to estimate the cell type ratio in transformed bulk data.

Article Snippet: The raw sequencing reads from a cDNA library using the BD Rhapsody Whole Transcriptome Assay Analysis Pipeline (v1.8) were processed.

Techniques: RNA Sequencing Assay, Sequencing, Expressing, Construct, Transformation Assay