Mouse single cell RNA-seq (Cd11c+)

The resource file below supports the paper “Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types” by Diego Adhemar Jaitin*, Ephraim Kenigsberg*, Hadas Keren-Shaul*, Naama Elefant, Franziska Paul, Irina Zaretsky, Alexander Mildner, Nadav Cohen, Steffen Jung, Amos Tanay†, Ido Amit†. science 2014.

  • A table containing the processed data is available here as well as in GEO submission GSE54006.

Each row contain the mRNA counts of a gene per each well (columns). Column names specify the batch ID and well ID, delimited by underscore.

Note that for the clustering analyis presented in the paper, we exlcuded the following genes that show significant batch specifc expression: “M34473″,”abParts”,”M13680″,”Tmsb4x”,”S100a4″,”B2m”,”Atpase6″,”Rpl23″,”Rps18″,”Rpl13″,”Rps19″,”H2-Ab1″,”Rplp1″,”Rpl4″,”Rps26″,”EF437368″

  • Experimental design file that contains information regarding each well, can be found here.

Each row in this file specifies the barcodes, index, and experimental details regarding one well (usually occupied by a single cell).

  • Log-likelihood table of single cells to the clusters of figure 2 can be found here.
    • Log10(probability)  of each single cell(rows) to be included in the different clusters (I-B cells, II-Macropages, III-NK/T, IV-Monocyte, V-pDC, VI-DC1, VII-DC2) shown in figure 2 of the paper (columns). The first column provide the cell_ID.

Low level analysis will require the following data:

This tarball contains 28 fastq files that corresponds to biological samples (amplification batches) included in the paper. Reads can be associated with a single well/molecule by the last 3 fields of the read header (plate_barcode:well_barcode:RMT).

To go from raw fastq files to the demultiplexed version, we were only extracting plate,well and umi barcodes from Read2, and storing the information in the read 1 header. Subsequent statistical processing, as described in this paper, may still further eliminate reads based on demultiplexing errors or other biases.

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