ArrayAnalysis

Data upload

Before you can run the analysis workflow, you first need to upload the expression data and a metadata table.

1. Upload expression data

The expression data should be supplied as an .zip folder containing all .CEL / .CEL.gz files. The file names should match with the sample IDs in the metadata table.

Browse...

2. Upload metadata

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the metadata as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv metadata table. A Series Matrix File can be downloaded from the GEO website.

.tsv/.csv file

Series Matrix File

Browse...

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers), perform normalization, and choose your desired probeset annotation.

1. Remove samples

Keep all samples

2. Select experimental group

3. Normalization

Use all arrays

Per experimental group

4. Annotation

Annotation format

Upload annotation file

Browse...

5. Pre-processing

Statistical analysis

In the statistical analysis step, you can find differentially expressed genes by selecting which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariates

3. Add gene annotation

Add gene annotations

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

Perform ORA on ...

Upregulated genes only

Downregulated genes only

Both

Select DEGs based on ...

P threshold

Raw P value

Adjusted P value

logFC threshold

Top N most significant genes

4. Select gene identifier

5. Perform ORA

Data upload

Before you can run the analysis workflow, you first need to upload the expression data as well as the meta data.

1. Upload expression data

The expression data should be supplied as a Series Matrix File. A Series Matrix File can be downloaded from the GEO website.

Browse...

2. Upload metadata

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the metadata as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv metadata table. A Series Matrix File can be downloaded from the GEO website.

.tsv/.csv file

Series Matrix File

Browse...

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers) and perform transformation and normalization.

1. Remove samples

Keep all samples

2. Select experimental group

3. Transformation

4. Normalization

Use all arrays

Per experimental group

5. Pre-processing

Statistical analysis

In the statistical analysis step, you can select which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariated

3. Add gene annotation

Add gene annotations

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

Perform ORA on ...

Upregulated genes only

Downregulated genes only

Both

Select DEGs based on ...

P threshold

Raw P value

Adjusted P value

logFC threshold

Top N most significant genes

4. Select gene identifier

5. Perform ORA

Data upload

Before you can run the analysis workflow, you first need to upload the expression data and the meta data.

1. Upload expression data

The expression data should be supplied as a .tsv/.csv file.

Browse...

2. Upload meta data

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the meta data as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv meta data file. A Series Matrix File can be downloaded from the GEO website.

.tsv/.csv file

Series Matrix File

Browse...

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers) and perform gene filtering and normalization.

1. Remove samples

Keep all samples

2. Select experimental group

3. Filtering

Minimum number of counts in smallest group size

4. Pre-processing

Statistical analysis

In the statistical analysis step, you can select which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariates

3. Add gene annotation

Add gene annotations

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

Perform ORA on ...

Upregulated genes only

Downregulated genes only

Both

Select DEGs based on ...

P threshold

Raw P value

Adjusted P value

logFC threshold

Top N most significant genes

4. Select gene identifier

5. Perform ORA

Data upload

Before you can run the analysis workflow, you first need to upload the expression data as well as the meta data.

1. Upload expression data

The expression data should be supplied as a .tsv/.csv file.

Browse...

2. Upload meta data

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the meta data as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv meta data file. A Series Matrix File can be downloaded from the GEO website.

.tsv/.csv file

Series Matrix File

Browse...

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers) and perform gene filtering and normalization.

1. Remove samples

Keep all samples

2. Select experimental group

3. Transformation

4. Filtering

4. Normalization

Use all samples

Per experimental group

5. Pre-processing

Statistical analysis

In the statistical analysis step, you can select which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariates

3. Add gene annotation

Add gene annotations

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

Perform ORA on ...

Upregulated genes only

Downregulated genes only

Both

Select DEGs based on ...

P threshold

Raw P value

Adjusted P value

logFC threshold

Top N most significant genes

Welcome to ArrayAnalysis!

Do you want to analyze microarray or RNA-Seq data?

Data upload

Before you can run the analysis workflow, you first need to upload the expression data and a metadata table.

1. Upload expression data

The expression data should be supplied as an .zip folder containing all .CEL / .CEL.gz files. The file names should match with the sample IDs in the metadata table.

2. Upload metadata

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the metadata as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv metadata table. A Series Matrix File can be downloaded from the GEO website.

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers), perform normalization, and choose your desired probeset annotation.

1. Remove samples

2. Select experimental group

3. Normalization

4. Annotation

5. Pre-processing

Statistical analysis

In the statistical analysis step, you can find differentially expressed genes by selecting which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariates

3. Add gene annotation

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

4. Select gene identifier

5. Perform ORA

Data upload

Before you can run the analysis workflow, you first need to upload the expression data as well as the meta data.

1. Upload expression data

The expression data should be supplied as a Series Matrix File. A Series Matrix File can be downloaded from the GEO website.

2. Upload metadata

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the metadata as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv metadata table. A Series Matrix File can be downloaded from the GEO website.

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers) and perform transformation and normalization.

1. Remove samples

2. Select experimental group

3. Transformation

4. Normalization

5. Pre-processing

Statistical analysis

In the statistical analysis step, you can select which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariated

3. Add gene annotation

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

4. Select gene identifier

5. Perform ORA

Data upload

Before you can run the analysis workflow, you first need to upload the expression data and the meta data.

1. Upload expression data

The expression data should be supplied as a .tsv/.csv file.

2. Upload meta data

The meta data includes relevant information (e.g., diagnostic group) about the samples. You can upload the meta data as a .csv/.tsv file or upload a Series Matrix file. Click here for an example .csv meta data file. A Series Matrix File can be downloaded from the GEO website.

Pre-processing

In this pre-processing step, you can remove samples (e.g., outliers) and perform gene filtering and normalization.

1. Remove samples

2. Select experimental group

3. Filtering

4. Pre-processing

Statistical analysis

In the statistical analysis step, you can select which groups to compare to each other and which covariates to add to the statistical model.

1. Make comparisons

2. Add covariates

3. Add gene annotation

4. Perform statistical analysis

Overrepresentation analysis

In the gene overrepresentation analysis (ORA) step, you can find processes that are enriched by the differentially expressed genes.

1. Select comparison

2. Select geneset collection

3. Select differentially expressed genes

4. Select gene identifier

5. Perform ORA

Data upload