fast *

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result-driven *

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* high resolution figures

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Synopis

The analysis of transcription data derived from individual cells elevates RNA-Seq to a completely new level. Now we can analyze the individual expression patterns of cells in different developmental or stationary stages and detect important sub-clonal differences in the cell population. For the first time we are dealing now with hundreds if not thousands of samples in one experiment. We are prepared for this challenge.

No result counts, if not presented in the best way. We are aiming for high-quality figures. We provide high-resolution images and additionally pdf versions of your graphics, which enable you to manipulate colors, text and many other options. Please see an example video here.

In case you want to contract it’s biology to analyze your Single Cell RNA-Seq project, we will divide the whole process into 3 steps, with you choosing, which level of analysis you need:

No result counts, if not presented in the best way. We are aiming for high-quality figures. We provide high-resolution images and additionally pdf versions of your graphics, which enable you to manipulate colors, text and many other options. Please see an example video here.

In case you want to contract it’s biology to analyze your RNA-Seq project, we will divide the whole process into 4 steps, with you choosing which level of analysis you need:

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    Low-level data analysis

    Heat our workstations: Data quality assessment, read manipulation (trimming, filtering), alignment and quantification and normalization. Please visit our RNA-Seq section in our portfolio for more details.

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    Statistics and Visualizations

    Statistics and visualization of differential expressed genes/transcripts or differential developmental stages.

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    Interpretation and Integration

    Further analysis, like Gene-ontolgy enrichment, Pathway involvement or integration with results from other assays.

We are analyzing Single Cell RNA-Seq data from all mayor next-generation sequencers from Illumina or Ion-Torrent. We can start from files in FASTA, FASTQ, unaligned BAM files or SRA format.
We are not fixed to the usual suspects, like human, mouse or rat. Any species with a sequenced genome can be handled by us.

Please scroll down for more information about the single steps of our RNA-Seq workflow. Please contact us here, in case you have any question about our service.

Quality control
Read preparation
alignment
Quantification and Normalization

Please have a look at our RNA-Seq section in our Portfolio on more information about our quality checks, alignment procedures and quantification/normalization procedures. Since Single Cell RNA-Seq assays usually contain more samples than standard RNA-Seq experiments, we produce overview quality report pdfs for each quality parameter separately. This helps you and us in deciding on exclusion of samples or further quality manipulation.


One Example

A great example for functional analysis of differentiation of cell can be found in a data set from Cole Trapnell et.al., where they performed Single Cell Analysis from differentiating primary human skeletal muscle myoblasts (HSMM) cell on 4 different time points (0, 24, 48, 72 hours). Altogether they analyzed 271 single cells. Since cells are never 100 percent synchronized, they developed an analyzing workflow which orders the samples in respect to a pseudo-time line. By doing so, important genes for early or later stages of the development can be easily identified. Additionally functional stages can be detected and visualized. This functionality was released in the R package Monocle. We show some example plots we created to test this brilliant Single Cell workflow. Besides specialized workflows we also apply standard RNA-Seq techniques. This includes, but not solely, standard differential gene expression detection, clustering techniques like hierarchical clustering, SOM clustering, Principal Component Analysis (PCA), Gene-ontology (GO) and Pathway enrichment.