BloodSpot help and information page

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BloodSpot is a database that provides gene expression profiles of genes and gene signatures in healthy and malignant hematopoiesis and includes data from both humans and mice. In addition to the default plot, that displays an integrated expression plot, two additional levels of visualization are available; an interactive tree showing the hierarchical relationship between the samples, and a Kaplan-Meier survival plot. The database is sub-divided into several datasets that are accessible for browsing.

A short quick guide is available on the main page and can be expanded by clicking "Quick Guide" under the main plot. Clicking on the three plots above the main viewing area will enlarge the selected plot and display it in the main viewing area.


Datasets are organized by organism of origin and disease status, and includes human healthy hematopoietic cells, human leukemia and healthy mouse hematopoietic cells. All datasets available were generated using oligonucleotide microarray chips, except for one mouse dataset that was generated using RNA-Seq.

There are 23 different datasets to choose from:

Normal human hematopoiesis with AMLs
Normal human hematopoiesis (Normal human hematopoiesis (DMAP))
Normal human hematopoiesis (HemaExplorer)
BloodPool: AML samples with normal cells
BloodPool: AML samples vs. normal cells
Leukemia MILE study
AML TCGA dataset
AML TCGA dataset vs. normal
AML Normal Karyotype
AML Normal Karyotype vs. normal
AML vs. normal
Mouse normal hematopoietic system
Mouse immgen abT cells
Mouse immgen Activated T cells
Mouse immgen B cells
Mouse immgen Dentritic cells
Mouse immgen gdT cells
Mouse immgen Key populations
Mouse immgen MFs Monocytes_Neutrophiles
Mouse immgen NK cells
Mouse immgen Stem and progenitor cells
Mouse immgen Stromal cells
Mouse normal (RNA-Seq)

One feature of BloodSpot is BloodPool, an aggregated and integrated dataset grouping the results of multiple studies focusing on AML. By means of our batch batch correction methods this dataset can be used to study gene expression in AML in comparison with healthy corresponding cells. BloodPool can be selected as any of the other available datasets.


BloodSpot takes a single gene name (or unambiguous gene alias) or gene signature name from MsigDB as query. CCL5 is the default gene displayed in the input field. Furthermore, the queries are not case sensitive and therefore the default gene CCL5 will also give an output in the mouse datasets where the name of the equivalent gene is Ccl5. A list of the datasets is available via the "Select dataset" drop-down menu. The default dataset is: "Normal human hematopoiesis with AMLs"It is possible to select which probe to display from the list in the upper right corner of the main plot. By default the probe with the overall highest intensity is at the top of the list. The option "Max probe" will use the one probe with the highest intensity within each population.

Three concomitant levels of visualization are available to the user: Expression plot (default view), tree plot and survival plot. For each of these plots, information about explorative and investigative features are discussed below.

Default plot

The center plot (default view) is a novel improved jitter strip chart of gene expression, a swift novel visualization plot that draws from bar plots and violin plots, where the jitter is controlled by the density of samples and normalized over the all columns in the chart. Thus, the width of the data cloud shows how many samples have similar values.

Tree plot

The plot shown to the right is an interactive hierarchical tree that shows the relationship between the samples displayed and allows changing the focus of the display. Expression level is visualized by size and color of the nodes, as indicated by the color legend. Mouse over the nodes to get the full name of cell type abbreviations. Click nodes to collapse a branch of the tree - this will also update the default scatter, removing the same populations.

Survival plot

The chart shown to the left is a survival plot based on a high quality AML dataset from The Cancer Genome Atlas (TCGA). It visually displays a full Kaplan-Meier analysis of survival, based on gene expression above or below median for a given gene query. The survival plots are only available for human datasets, sharing probes with the microarray platform used by the TCGA (Affymetrix U133 Plus 2).

Correlations of genes and signatures

For each gene and signature in every dataset, the top correlating genes or signatures can be explored from the drop-down menu using the "Gene Correlations" button. This feature allows for investigation of associations between putative co-regulated genes or signatures that exhibit similar or inverse expression patterns over the course of hematopoiesis. Correlation coefficients are given along with the genes or signatures.

Other built-in tools

Cell populations may be removed from the graphs using the "Select population" button. The current plot displayed can be exported as PDF using the "Print as PDF" button. The "T-Test" button can be used to add the results from a students t-test for significance between pairs of populations in the default expression plot.

Upload sample

By clicking the "Upload sample" it is possible to analyze user-supplied samples on the Affymetrix U133 plus 2 platform. Significantly, doing so allows for the comparison of any myeloid sample microarray data to normal human hematopoiesis. The resulting analysis is then displayed in a private session in the framework of BloodSpot, together with a principal component analysis that shows the location of the uploaded sample in the hematopoietic sample space. The analysis is anonymous and requires no login. The resulting dataset, including the uploaded sample, can then be queried along with the default datasets, in a private session. All names and array information are stripped from the uploaded file before creating the database for the user session. Hence, the uploaded sample in the private session will appear simply as S_1 in all charts. The private sessions and uploaded data will be deleted every day at GMT 1.30 pm.

Data processing method

For some datasets (see Data) several Affymetrix chips platforms from different laboratories were used to generate the dataset, which calls for some sort of batch correction. To this end, the CEL files were partitioned into batches (platform and origin) prior to RMA normalization. When several platforms are represented in the dataset, only the overlapping probesets are retained for further data processing. Afterwards, we used ComBat, an empirical bayes method implemented in the R language to correct for batch effect. Fold changes for AML samples vs. nl. are compared to their nearest normal counterpart, as described by Rapin et al. Blood, 2014. User-supplied uploaded samples obtain batch correction covariates from a majority vote among the nearest normals in a subspace of probes that are highly varying throughout the course of hematopoiesis. Hereafter the entire dataset including the uploaded patient sample is normalized and batch corrected using ComBat for full integrity of the dataset. PCA analysis and gene signature values are then calculated.

Available genes

The server is restricted to genes found in our database of Affymetrix Human 133U plus 2 , Affymetrix Human 133UA and Affymetrix Human 133UB, chips for human, and GeneChip Mouse Genome 430 2.0 and Affymetrix Mouse Gene 1.0 ST Arrays for mouse.


Abbreviations can be found below the plot by clicking "Abbreviations". Here information about the source of the data is also displayed for the current dataset.


Dataset Features Samples
Leukemia MILE study 67191 2095
Normal human hematopoiesis with AMLs 67191 296
Mouse immgen Key populations 47273 256
AML vs. normal 67191 252
AML TCGA dataset 67191 244
AML TCGA dataset_vs_nl 67191 244
AML Normal Karyotype 54675 234
AML Normal Karyotype_vs_nl 67191 234
Normal human hematopoiesis (DMAP) 35459 211
Mouse immgen abT cells 47273 190
Mouse immgen Dentritic cells 47273 151
Mouse immgen MFs Monocytes_Neutrophiles 47273 114
Mouse immgen B cells 47273 103
Normal human hematopoiesis (HemaExplorer) 57270 77
Mouse immgen gdT cells 47273 76
Mouse immgen Stem and progenitor cells 47273 76
Mouse normal hematopoietic system 57613 67
Mouse immgen Activated T cells 47273 55
Mouse immgen NK cells 47273 47
Mouse immgen Stromal cells 47273 39

Dataset Organism Source
Sample numbers
Cell typesReference
Normal human hematopoiesis with AMLs HumanGSE42519
HSC, MPP, CMP, MEP, GMP, early PM, late PM, MY, MM, BC, PMN Rapin et al. (1)
HemaExplorer Human GSE17054
HSC Majeti et al. (2)
HemaExplorer Human GSE19599
GMP, MEP Andersson et al. (3)
HemaExplorer Human GSE11864
Monocytes Hu et al. (4)
HemaExplorer Human E-MEXP-1242
Monocytes Wildenberg et al. (5)
DMAP Human GSE24759
Normal Hematopoiesis Novershtern et al. (6)
Normal mouse hematopoiesis Mouse GSE14833, GSE6506
Normal Hematopoiesis Di Tullio et al.13, Chambers et al.(7)
Immgen datasets Mouse GSE15907
Normal Hematopoiesis (8-14)
AML Normal Karyotype , AML Normal Karyotype vs. normal Human AML GSE15434
NK-AML, WBM Kohlman et al. (15, 16)
MILE study Human AML, ALL, CML, CLL and MDS GSE13159
AML, ALL and preleukemic stages. Haferlach et al. (17, 18)
Normal human hematopoiesis with AMLs, AML vs. normal Human AML GSE6891
NK-AML, WBM de Jonge et al.(19,20)
TCGA, TCGA vs. normal Human AML TCGA
Various genetic aberrations, including t(8;21), inv(16), t(15;17), t(11q23), complex karyotype, WBM TCGA (20)

Data References

1. Rapin, N. et al. Comparing cancer vs normal gene expression profiles identifies new disease entities and common transcriptional programs in AML patients. Blood 123, 894-904 (2014).
2. Majeti, R. et al. Dysregulated gene expression networks in human acute myelogenous leukemia stem cells. Proceedings of the National Academy of Sciences 106, 3396-3401 (2009).
3. Andersson, A., Edén, P., Olofsson, T. and Fioretos, T. Gene expression signatures in childhood acute leukemias are largely unique and distinct from those of normal tissues and other malignancies. BMC Medical Genomics 3, 6 (2010).
4. Hu, X. et al. Integrated Regulation of Toll-like Receptor Responses by Notch and Interferon-gamma Pathways. Immunity 29, 691-703 (2008).
5. Wildenberg, M. E., van Helden-Meeuwsen, C. G., van de Merwe, J.P., Drexhage, H. A. and Versnel, M. A. Systemic increase in type  I interferon activity in Sjögren's syndrome: A putative role for plasmacytoid dendritic cells. European Journal of Immunology 38, 2024-2033 (2008).
7. Novershtern, N. et al. Densely interconnected transcriptional circuits control cell states in human hematopoiesis. Cell 144, 296-309 (2011).
8. Di Tullio, A. et al. CCAAT/enhancer binding protein alpha (C/EBP(alpha))-induced transdifferentiation of pre-B cells into macrophages involves no overt retrodifferentiation. Proceedings of the National Academy of Sciences 108, 17016-17021 (2011).
9. Chambers, S. M. et al. Hematopoietic fingerprints: an expression database of stem cells and their progeny. Cell Stem Cell 1, 578-591 (2007).
10. Painter, M. W. et al. Transcriptomes of the B and T lineages compared by multiplatform microarray profiling. Journal of immunology (Baltimore, Md. : 1950) 186, 3047-3057 (2011).
11. Desch, A. N. et al. CD103+ pulmonary dendritic cells preferentially acquire and present apoptotic cell-associated antigen. The Journal of experimental medicine 208, 1789-1797 (2011).
12. Malhotra, D. et al. Transcriptional profiling of stroma from inflamed and resting lymph nodes defines immunological hallmarks. Nature immunology 13, 499-510 (2012).
13. Narayan, K. et al. Intrathymic programming of effector fates in three molecularly distinct [gamma][delta] T cell subtypes. Nature immunology 13, 511-518 (2012).
14. Miller, J. C. et al. Deciphering the transcriptional network of the dendritic cell lineage. Nature immunology 13, 888-899 (2012).
15. Kohlmann, A. et al. Gene expression profiling in AML with normal karyotype can predict mutations for molecular markers and allows novel insights into perturbed biological pathways. Leukemia 24, 1216-1220 (2010).
16. Kohlmann, A. et al. An international standardization programme towards the application of gene expression profiling in routine leukaemia diagnostics: the Microarray Innovations in Leukemia study prephase. British Journal of Haematology 142, 802-807 (2008).
17. Haferlach, T. et al. Clinical utility of microarray-based gene expression profiling in the diagnosis and subclassification of leukemia: report from the International Microarray Innovations in Leukemia Study Group. Journal of Clinical Oncology 28, 2529-2537 (2010).
18. Verhaak, R. G. W. et al. Prediction of molecular subtypes in acute myeloid leukemia based on gene expression profiling. Haematologica 94, 131-134 (2009).
19. de Jonge, H. J. M. et al. High VEGFC expression is associated with unique gene expression profiles and predicts adverse prognosis in pediatric and adult acute myeloid leukemia. Blood 116, 1747-1754 (2010).
20. Network, C. G. A. R. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. 368, 2059-2074 (2013).


This work was funded by the Danish Research Council for Strategic Research and by the NovoNordisk Foundation.


For technical questions contact: frederik [at]

For scientific questions contact: bo.porse [at] or nicolas.rapin [at]


BloodSpot: a database of gene expression profiles and transcriptional programs for healthy and malignant haematopoiesis
Nucl. Acids Res. (2015)
doi: 10.1093/nar/gkv1101